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What Is SOC 2 Compliance: Everything You Need To Know

Did you know? The average global cost of a data breach was approximately USD 4.44 million, a clear signal that the business cost of cyber risk remains enormous. Add to that the recent cyber-attack on one of the largest automakers from the UK, which has been estimated to have cost the UK economy around £1.9 billion (≈ USD 2.5 billion) after disruption to its supply chain and manufacturing operations. For organisations that process, store, or transmit client data, this is not a distant threat but a business reality. The integrity of your systems, the trust your clients place in you, and the resilience of your operations are all on the line. And once that trust is broken, the reputational, regulatory and financial fallout can have serious consequences. This is precisely why SOC 2 compliance becomes more important than ever. After reading this blog, whether you’re a CIO, CISO, compliance officer, service provider executive, cybersec enthusiast or risk lead, you’ll have a clear understanding of how to integrate SOC 2 into governance, risk, and assurance frameworks.

What Is SOC 2 Compliance?

SOC 2, short for System and Organisation Controls Type 2, is a globally recognised audit framework designed to ensure that service providers handle client data with consistent, provable security and operational discipline. It was established by the American Institute of Certified Public Accountants (AICPA) as part of its Statement on Standards for Attestation Engagements (SSAE 18). Unlike many technical standards that prescribe “what” must be done, SOC 2 focuses on “how effectively” an organisation’s internal controls operate in practice. It is an attestation report, not a certification — meaning a licensed independent auditor evaluates your organisation’s policies, procedures, and technical configurations to attest whether they meet the Trust Service Criteria (TSC) defined by the AICPA. The trust service criteria are built on the following five principles:

Principle	Objective	Typical Control Domains
Security	Protect systems and data from unauthorised access.	Access controls, intrusion detection, firewalls, endpoint protection.
Availability	Ensure systems remain available for operation and use as committed.	Uptime monitoring, disaster recovery, and incident management.
Processing Integrity	Confirm that systems process data accurately, completely, and promptly.	Input validation, change management, process automation.
Confidentiality	Safeguard information designated as confidential.	Data classification, encryption, and restricted data sharing.
Privacy	Manage personal information according to policies and commitments.	Data minimisation, consent management, and deletion protocols.

Every SOC 2 audit is unique because the controls differ according to each organisation’s systems and risk profile. A fintech platform, a verification service provider, and a cloud-hosting company will all implement distinct controls — yet their evaluation framework remains consistent under the SOC 2 model. The final deliverable, known as the SOC 2 report, provides an independent opinion on how the organisation’s controls meet the applicable criteria. This report is not public (SOC 3 reports are meant for public broadcast and as marketing collateral); it is typically shared under non-disclosure agreements with clients, regulators, or partners who require assurance before entrusting sensitive data. It communicates one simple but vital message to clients: your data is handled securely, consistently, and transparently.

Types Of SOC 2 Reports: Type I and Type II

SOC 2 audits come in two formats: Type I and Type II. Both follow the AICPA’s Statement on Standards for Attestation Engagements No. 18 (SSAE 18) and evaluate an organisation’s controls against the same five Trust Services Criteria (TSC) — Security, Availability, Processing Integrity, Confidentiality, and Privacy. What distinguishes them is scope and duration.

SOC 2 Type I

A Type I report assesses whether controls are properly designed and implemented at a specific point in time.
The independent auditor examines artefacts such as security policies, architectural diagrams, system configurations, and access-control lists to confirm that each control exists and is logically sound.

It answers the question: “Have we built the right safeguards to protect customer data?”

This version is most useful for organisations beginning their compliance journey or needing quick proof of governance readiness before a product launch or enterprise partnership.

Attribute	SOC 2 Type I
Scope	Control design and implementation
Timeframe	Point-in-time (single date)
Evidence	Policies, system settings, configurations
Assurance Level	Baseline readiness
Use Case	Early-stage companies proving initial maturity

SOC 2 Type II

A SOC 2 Type II report represents the highest assurance level under SOC 2. It evaluates both design and operating effectiveness of controls over an extended period — typically three to twelve months — to determine whether protective measures perform reliably in daily operations.

During the audit, licensed CPA firms gather empirical evidence from across the organisation, including:

Access-management logs showing user provisioning and de-provisioning.
Incident-response records confirming timely detection and remediation of security events.
Change-management tickets validating that system updates were tested and approved.
Backup and recovery logs demonstrating successful data-restore drills.
Vendor-risk reviews documenting third-party assurance activities.

The auditor’s opinion confirms whether these controls operated consistently throughout the review window, providing continuous proof of security and compliance discipline.

Attribute	SOC 2 Type II
Scope	Design + operating effectiveness
Timeframe	Typically 6–12 months
Evidence	Logs, tickets, incident and change records
Assurance Level	Continuous operational assurance
Use Case	Mature organisations handling regulated or client-sensitive data

Issuing Authority And Governance Framework Behind SOC 2 Reports

The Governing Body

SOC 2 audits are authorised and standardised by the American Institute of Certified Public Accountants (AICPA). Every SOC 2 engagement follows the Statement on Standards for Attestation Engagements No. 18 (SSAE 18), which outlines how an independent auditor must assess an organisation’s internal controls.

SOC 2 draws its structural principles from the COSO Internal Control Framework, a globally adopted model for designing and evaluating risk and control systems. Together, AICPA and COSO ensure that SOC 2 reporting is consistent, repeatable, and defensible, regardless of the industry being audited.

Only licensed CPA firms and AICPA-approved service auditors are permitted to perform a SOC 2 examination. The outcome is an attestation report, not a certificate — meaning the auditor is expressing a professional opinion that carries legal and ethical accountability. This distinction is what gives SOC 2 its credibility: it is independently validated, not self-declared.

The SOC 2 Audit Workflow

A SOC 2 engagement typically happens across the following phases:

Phase	Objective	Outcome
1. Planning & Scoping	Determine which systems, products, or services fall under the audit and which Trust Services Criteria (TSC) apply.	Defined system boundaries and scope statement
2. Readiness Review	Identify control gaps, align documentation, and prepare operational evidence before formal testing begins.	Gap assessment and remediation plan
3. Evidence Testing	Examine technical configurations, system logs, and procedural records to verify the existence and performance of controls.	Control testing results and audit workpapers
4. Report Finalisation	The auditor issues an opinion based on findings, supported by management’s assertion and the system description.	SOC 2 Type I or Type II report

In conclusion, the auditor’s opinion can be:

Unqualified (Clean): Controls were designed and operated effectively.
Qualified: Minor deficiencies, but overall objectives achieved.
Adverse: Controls failed to meet stated objectives.
Disclaimer: Insufficient evidence to form an opinion.

An unqualified opinion is the benchmark that indicates full compliance.

The Trust Services Criteria (TSC)

All SOC 2 reports measure controls against one or more of the five AICPA-defined criteria, as mentioned previously. Organisations choose which criteria are relevant to their services; a payment processor might include Security and Processing Integrity, while a healthcare SaaS platform would also select Privacy and Confidentiality.

Structure And Components Of A SOC 2 Report

The SOC 2 report’s format is governed by the AICPA SOC 2 Guide, ensuring uniformity regardless of the industry or the auditor.
Each section has a specific purpose, enabling readers — often CISOs, compliance officers, or client auditors — to assess how well the organisation protects and manages customer data.

1. Independent Auditor’s Opinion

This section presents the auditor’s professional opinion, signed by a licensed CPA firm authorised under SSAE attestation standards.
It specifies:

The scope of the audit — which systems, period, and Trust Services Criteria were covered.
The type of report (Type I or Type II).
The auditor’s conclusion, which may be:
- Unqualified (Clean) – Controls were suitably designed and operated effectively.
- Qualified – Minor exceptions, but overall objectives met.
- Adverse – Controls failed to meet objectives.
- Disclaimer – Insufficient evidence to form an opinion.

A “clean” (unqualified) opinion is the benchmark outcome most organisations aim for.

2. Management’s Assertion

Here, senior management accepts full responsibility for the design and operation of controls.
The assertion typically includes:

A description of the system or service examined.
The Trust Services Criteria selected for evaluation.
Management’s statement confirming that the information supplied to auditors was complete and accurate.

This section is important because auditors attest to management’s statements and do not replace them. It establishes accountability at the executive level for how data and controls are governed internally.

3. System Description

The system description provides a factual narrative of the environment under audit.
It outlines:

Core systems and infrastructure (networks, applications, databases).
Business processes supporting the service in scope.
Logical and physical security architecture.
Control responsibilities of third-party vendors.
Any limitations or boundaries of the audit (e.g., regions or systems excluded).

This gives readers a transparent view of how technology and policies combine to deliver security, availability, and privacy commitments.

4. Controls, Tests, And Results

Often presented in a tabular format, this section maps every control to its corresponding Trust Services Criterion and describes how the auditor tested it.
Each control entry contains:

Control Objective or Description – The purpose of the control.
Test Performed – How the auditor validated it (inspection, observation, re-performance, or inquiry).
Result – Whether the control operated effectively during the audit period, with details of any exceptions found.

For Type II reports, this is the most detailed section — it evidences months of operational reliability through sampled logs, ticket reviews, and change records.

5. Complementary User-Entity Controls (CUECs)

SOC 2 recognises shared responsibility between service providers and clients. CUECs specify what clients must do on their side for the audited controls to remain effective — for example, enforcing user password policies or managing endpoint security. This ensures the SOC 2 report cannot be misinterpreted as validating an entire supply chain, only the portion controlled by the service organisation.

6. Other Information And Appendices

The final part may include:

Notes on corrective actions taken for exceptions.
Supplementary certifications (ISO 27001, PCI DSS, or HIPAA mappings).
Diagrams, control narratives, or historical comparisons to prior audits.

These additions help contextualise results and demonstrate a culture of continuous compliance improvement.

How To Read A SOC 2 Report Effectively

For CISOs, vendor managers, and auditors reviewing a SOC 2 report, three focus points matter most:

Scope Alignment — Are the right systems and Trust Services Criteria included?
Opinion Strength — Was the auditor’s opinion unqualified?
Control Evidence — Do the tests and results support consistent control performance over time?

Key Measurement Metrics And Evaluation Criteria In SOC 2

This section outlines how audits under the American Institute of Certified Public Accountants (AICPA) evaluate your controls in a SOC 2 engagement — it focuses on what auditors measure, how they sample evidence, and how performance is judged over time.

Control Objectives and Related Metrics

Every control in a SOC 2 audit must map to a specific control objective (what you aim to achieve) and be measurable or monitorable. Common metrics include:

Number of unauthorised access attempts — helps measure the effectiveness of access-control mechanisms.
Mean time to detection (MTTD) and mean time to remediation (MTTR) — show how quickly incidents are spotted and resolved.
System availability percentage — indicates whether services are meeting the Availability criterion of the Trust Services Criteria.
Percentage of successful change-management events without rollback — reflects the Processing Integrity criterion.

These metrics, while not mandated verbatim by SOC 2, are illustrative of how operational performance is assessed.

Sampling and Test Procedures

For a Type II report, the auditor performs sampling because it is impractical to review every transaction or system event for the audit period. Typical procedures include:

Inspection — reviewing documents, policies, configurations, and screenshots.
Observation — watching a process being carried out (e.g., backup restore test).
Re-performance — executing a control again to see if it works as intended (e.g., a patch deployment followed by a penetration test).
Inquiry — talking with responsible personnel to understand roles and controls, and checking if their verbal description aligns with evidence.

Auditors aim for sufficient appropriate evidence over the period, meaning enough samples such that they have confidence that controls worked effectively as stated.

Exception Rates and Their Significance

When auditors test controls, they may find exceptions (instances where the control did not perform as expected). How these are handled is critical:

A low exception rate (e.g., 2 %) may still allow an unqualified opinion if the organisation can show remediation and risk was managed.
A high exception rate may lead to a qualified or adverse opinion, which signals to clients that controls were not reliably operating.

The auditor will consider the nature of exceptions (severity, frequency, compensating controls) when forming an opinion.

Audit Period and Evidence Retention

For a Type II engagement, the audit typically spans six to twelve months of operational history — allowing the auditor to evaluate the consistency of controls.
Evidence must be retained and available for this period — including logs, tickets, change-records, vendor-assessment files, etc. If the evidence window is shorter, the auditor may issue a limited-scope report or decline to provide an unqualified opinion.

Operational Maturity Indicators

From a cybersecurity expert’s angle, the following indicators signal that a SOC 2 audit is built on a mature control environment:

Controls are automated where feasible (for example, alert escalation, user-de-provisioning, backup verification).
Continuous monitoring is in place, with dashboards showing compliance trends, incident volumes, and change-control exceptions.
Regular remediation loops — documented follow-up on failed controls or exceptions from prior audits.
Third-party oversight — vendor assessments, subcontractor controls mapped to your audit scope.
Audit-ready documentation — evidence is stored in a consistent, searchable manner, enabling the auditor to quickly validate.

The Business Value of SOC 2 Type II Compliance

1. Builds Enterprise-Level Client Trust

Enterprise buyers increasingly demand continuous evidence of data-handling discipline.
While the AICPA does not publish adoption statistics, multiple independent vendor-risk studies confirm that SOC 2 Type II has become a de facto requirement in enterprise procurement, particularly within finance, healthcare, and technology sectors.

A current SOC 2 Type II report allows security teams to provide third-party-verified proof of control performance during due diligence. This directly reduces friction in onboarding, as many enterprise RFPs accept a valid SOC 2 Type II instead of bespoke audit questionnaires — an efficiency supported by every major compliance-automation provider.

2. Strengthens Security Posture and Control Discipline

Because SOC 2 Type II examines real evidence — access logs, incident tickets, backup validations — it forces operational accountability. Controls cannot exist only on paper; they must produce audit-ready artefacts for six to twelve consecutive months.

Organisations completing annual Type II cycles typically show demonstrable improvement in:

Incident-response readiness and documentation,
Change-management consistency, and
Reduction of configuration drift across production systems.

3. Reduces Long-Term Risk and Insurance Burden

With the global average breach cost reaching USD 4.45 million, rising by 15 % over three years, SOC 2 Type II controls directly mitigate the root causes of these losses.

While exact discounts vary by underwriter, possessing a recent SOC 2 Type II report typically qualifies organisations for favourable risk scoring and coverage terms — because it evidences control reliability verified by an external CPA.

4. Creates Efficiency Through Continuous Assurance

When organisations integrate monitoring and documentation tools — for example, centralised ticketing for change control or automated log retention — audit preparation time drops sharply after the first cycle.
Multiple reports suggest that clients who maintain year-round SOC 2 readiness reduce subsequent audit workloads by 30 – 50 %. This converts compliance from a reactive cost into a predictable, repeatable operating process.

5. Enhances Market Credibility and Investor Confidence

For publicly listed or investor-funded companies, an unqualified SOC 2 Type II opinion serves as tangible evidence of governance maturity. Investors and partners view it as assurance that leadership oversees security and privacy with the same rigour applied to financial reporting.
Because the report is issued by an independent CPA firm under SSAE standards, it carries professional liability, making it far more credible than internal certifications or self-assessments.

6. Positions the Organisation for Regulatory Alignment

The AICPA Trust Services Criteria map closely to major regulatory and security frameworks — including:

ISO 27001 (Information Security Management Systems),
NIST SP 800-53 Rev. 5 (Security and Privacy Controls), and
EU GDPR and India’s Digital Personal Data Protection Act 2023 (privacy and accountability principles).

Challenges And Best Practices For Sustaining SOC 2 Type II Compliance

SOC 2 Type II compliance is not a one-time project but an ongoing commitment.
Many companies complete their first audit successfully but struggle to maintain the same standard year after year.

Below are some common challenges and practical ways to overcome them.

Defining The Right Scope

One of the biggest mistakes organisations make is setting too narrow a scope. Sometimes entire systems, third-party tools, or environments are left out because teams assume they’re “non-critical.”
However, the AICPA standard requires the audit to reflect all systems that handle customer data. The fix is simple — keep a clear inventory of every platform that processes or stores sensitive information, update it regularly, and make sure new integrations are included before each audit cycle.

Managing Evidence Properly

SOC 2 auditors don’t rely on verbal assurance; they rely on evidence. A missing access log or an outdated incident ticket can lead to exceptions even if the control worked in practice.

Create a central folder or tool for evidence storage, label everything by control area, and update it continuously. Automating evidence collection through ticketing or monitoring systems helps avoid last-minute issues.

Keeping Control Owners Accountable

Controls fail most often when ownership is unclear. Each control should have a specific person responsible for its execution and documentation. When people move roles, ownership should move with them. It’s also good practice to review control ownership quarterly — it keeps accountability fresh.

Watching Vendor Dependencies

Even if your internal systems are perfect, your vendors can cause problems. Cloud providers, payroll processors, or analytics platforms all play a part in your control environment. Always review their SOC 2 or ISO reports and document how you rely on them. This protects your report from being qualified due to “carve-outs” or third-party risks.

Handling Audit Exceptions

Finding a few exceptions is normal. Ignoring them is not. Auditors will always ask how those issues were corrected. Track every finding, note what caused it, who fixed it, and what changed to prevent recurrence.

Keeping Leadership Involved

SOC 2 is a management responsibility, not just an IT exercise. Leadership should review the control dashboard every quarter, approve updated policies, and stay aware of open risks. Auditors often mention the strength of “tone at the top” — visible executive engagement helps the entire compliance culture stay active.

Making Compliance Part Of Everyday Work

Finally, compliance should not feel like a separate event. Train employees to treat access reviews, change approvals, and incident documentation as normal workflow, not extra paperwork. When these habits become routine, audit readiness happens naturally.

Cost Considerations For SOC 2 Type II Reports

Achieving SOC 2 Type II compliance involves a mix of external and internal costs that reflect the depth of the audit and the maturity of your control environment. The overall expense depends on the organisation’s size, number of systems in scope, and how many of the five Trust Services Criteria are covered.

Cost Component	Typical Range (USD)	What It Covers
External Audit (CPA Attestation)	12,000 – 100,000+	The independent SOC 2 audit was conducted by a licensed CPA firm under SSAE standards.
Readiness Assessment	5,000 – 15,000	A pre-audit gap analysis to identify missing controls and documentation before the formal engagement.
Remediation & Control Implementation	10,000 – 100,000+	Internal work to implement or strengthen policies, monitoring systems, access controls, and data-handling practices.
Automation & Compliance Tools	7,000 – 25,000 per year	Evidence-collection and monitoring platforms that maintain continuous audit readiness.
Internal Labour & Opportunity Cost	Variable	Staff time for gathering evidence, supporting remediation, and managing the audit.
Annual Renewal / Continuous Monitoring	30 – 40 % less than the first cycle	The ongoing cost once controls and tooling are embedded in regular operations.

What To Expect

Type II audits cost more than Type I, since they evaluate performance over several months instead of a single date.
Scope has the biggest impact — including more systems or Trust Services Criteria increases audit depth and cost.
Readiness lowers future spend — once automated evidence management and control ownership are established, subsequent renewals become faster and cheaper.
The investment yields returns in faster enterprise onboarding, smoother vendor assessments, and lower security-assurance overheads in future deals.

Conclusion

SOC 2 Type II is more than a compliance milestone; it’s a reflection of operational integrity. It demands that an organisation’s promises about security and privacy are not assumed but demonstrated — consistently, over time, under real-world conditions. In this sense, the framework isn’t about ticking boxes; it’s about building proof of trust.

For AuthBridge, being SOC 2 certified is a testament to our discipline. It means every data flow, every process, and every client interaction operates within a verified structure of control and accountability. The certification doesn’t just attest that our systems are secure — it confirms that security is built into how we work, not layered on top. That’s the difference between compliance and confidence — and it’s the standard we hold ourselves to.

By Abhinandan, 6 hoursOctober 29, 2025 ago

BFSI

What Is RegTech? Definition, Technologies Involved & Uses

Introduction

In India, RegTech, or Regulatory Technology, has moved from being a buzzword to a backbone of financial integrity. With regulatory scrutiny higher than ever and digital ecosystems expanding fast, the demand for compliance-driven technology is now at an all-time high.

RegTech is the unsung hero behind the smooth digital banking, Digital KYC, and anti-fraud mechanisms we now take for granted. It doesn’t make loans or open accounts like a fintech app does. Instead, it ensures every transaction, identity, and document follows the rules automatically. This blog will guide you through everything about RegTech—from its definition and technologies to its applications, industries, and distinctions from FinTech.

What Is RegTech?

RegTech refers to the use of technology to help organisations comply with laws and regulations efficiently, accurately, and transparently. It employs technology-driven solutions that automate, simplify, and strengthen compliance management. This technology merges software, data, and analytics to monitor, report, and predict compliance obligations in real-time.

The term first appeared after the 2008 global financial crisis, when regulators worldwide tightened controls to prevent fraud and systemic risk. Financial institutions found traditional compliance, which comprised manual audits, paperwork, and checklists, to be too slow and expensive. Technology became the natural solution.

Why The Need For RegTech?

Every regulated industry faces three constant challenges:

Complex regulations that change frequently
Heavy penalties for non-compliance
Mounting operational costs for manual checks

RegTech addresses all three by turning compliance into a proactive system. Instead of waiting for auditors to find errors, firms can detect them instantly through AI models, dashboards, or automated alerts. Consider RegTech as a vigilant digital assistant sitting inside a company’s IT system. It reads rules (like the RBI’s KYC guidelines), compares them with ongoing business data (transactions, identities, documents), and flags anything that doesn’t fit. The same system can then produce regulations-ready and extremely accurate reports without any human spreadsheet juggling.

The Technologies Behind RegTech & Its Working

At the macro level, RegTech is an entire ecosystem. It makes use of the combination of data science, automation, and secure computing to create an always-on compliance framework. Each technology contributes to a wider framework often called RegOps or Regulatory Operations, which keeps financial institutions compliant with regulations. Here are the key technologies powering RegTech:

Artificial Intelligence and Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) sit at the centre of every mature RegTech stack. In India, AI-driven models help banks and NBFCs detect AML transaction typologies such as placement, layering, and structuring across payment rails like UPI, NEFT, and IMPS. Instead of flagging random alerts, modern systems apply behavioural scoring and entity resolution to connect related accounts and identify real risk.

ML algorithms continuously learn from past suspicious-activity reports, improving detection accuracy.
AI-assisted sanction-screening engines match customer names against fuzzy or partial entries across UN, OFAC, and domestic lists.
Predictive analytics help estimate the probability of non-compliance based on transaction patterns, geography, or product type.

Natural Language Processing

The pace at which RBI, SEBI, and IRDAI issue circulars makes manual tracking impossible. Natural Language Processing (NLP) addresses this by teaching systems to read, interpret, and summarise regulatory text automatically.

Compliance teams now rely on regulatory-intelligence platforms that parse circulars overnight, extract relevant sections, and map them to internal policies. Some advanced tools even employ semantic comparison models to show clause-level changes between old and new guidelines.

Robotic Process Automation (RPA)

RPA acts as a bridge between compliance policy and operational delivery. Bots handle routine, rule-based work: collecting KYC documents, validating PAN–GST combinations, reconciling account data, and filing STR/CTR reports to FIU-IND.

When the volumes become large, RPA operates alongside workflow orchestration tools so that exception handling is escalated to human reviewers while the rest of the pipeline runs autonomously. The result is higher throughput, lower operational risk, and near-zero manual data entry.

Big Data and Advanced Analytics

Data is what RegTech platforms thrive on. They integrate feeds from core-banking systems, loan origination platforms, payment gateways, and CRM tools. Using stream-processing engines and distributed data lakes, they can monitor millions of transactions in real time.

These analytics help identify emerging risk clusters, predict defaults, and help quantify exposure for internal risk committees. Dashboards powered by self-service BI tools give compliance heads near-instant visibility across branches, products, and geographies.

Blockchain and Distributed Ledger Technology

Few technologies inspire as much trust as a distributed ledger. In RegTech, Blockchain ensures that compliance records are immutable and verifiable.

India’s ongoing pilots under the RBI’s Regulatory Sandbox Framework explore shared KYC utilities where banks can access a verified customer profile once it’s approved by any other regulated entity. This model reduces duplication while maintaining complete traceability under customer-consent protocols.

Cloud Computing, Microservices, and APIs

The cloud is what makes RegTech scalable. Modern solutions are built as cloud-native microservices, allowing banks and regulators to process compliance enforcements securely and at scale.

Most RegTech providers host their services on compliant local data centres in Mumbai, Hyderabad, or Chennai to satisfy data-localisation norms.
Open APIs power instant verifications — from pulling CIN and DIN details via MCA to checking e-sign validity through NIC or UIDAI gateways.
API gateways with JWT-based authentication and TLS 1.3 encryption ensure inter-institution data exchanges meet RBI’s cybersecurity directives.

Cloud adoption also enables SupTech (Supervisory Technology), where regulators themselves use cloud-based dashboards to monitor reporting entities in near real time.

Optical Character Recognition (OCR) and Computer Vision

Document authenticity remains a key metric for compliance. OCR extracts data from physical forms, while computer-vision algorithms detect forgery, tampering, or mismatch.

During Video KYC processes, OCR reads identity details from an Aadhaar or passport; facial-recognition models confirm liveness and match the applicant to official records. Both these tools, combined, have made remote customer onboarding both regulatorily compliant and operationally viable in India.

Knowledge Graphs and RegData

Financial crime hardly ever occurs in isolation. Knowledge graphs help visualise the relationships among different entities like directors, shareholders, subsidiaries, vendors, and politically exposed persons (PEPs).

By integrating data from MCA, stock-exchange filings, and sanctions databases, RegTech platforms can automatically expose beneficial-ownership overlaps or undisclosed connections between borrowers and suppliers — critical for corporate due diligence and third-party risk assessment.

Cybersecurity and Encryption

Every RegTech process involves sensitive information. With the Digital Personal Data Protection Act, encryption, consent management, and data retention governance have become mandatory duties.

Industry-grade RegTech platforms employ:

AES-256 encryption for data at rest and TLS 1.3 for data in transit.
Zero-trust network architectures with adaptive access control.
Immutable audit logs for regulator-verified trails.

Applications Of RegTech

Consider compliance synonymous with a human being; RegTech would be its nervous system, responsible for sensing, interpreting, and responding instantly to regulatory signals. Over the past decade, its applications have expanded from simple KYC checks to full-scale governance, risk, and compliance (GRC) ecosystems. Let’s look at the applications of RegTech:

1. Digital KYC and Customer Onboarding

The BFSI sector processes numerous new accounts every month, and each account must undergo KYC (Know Your Customer) verification. Traditionally, this translated to photocopies, physical signatures, and delayed customer onboarding. RegTech transformed it into a two-minute digital process.

When a user begins onboarding, OCR (Optical Character Recognition) extracts information from Aadhaar or PAN documents, face-matching AI confirms identity in real time, and geo-fencing ensures that the interaction occurs within India’s borders. The system cross-checks data with government APIs such as CKYC, UIDAI, or GSTN.

The Reserve Bank of India’s Video-based Customer Identification Process (V-CIP) guideline, updated in 2025, has legitimised this automation. It allows fully remote onboarding while maintaining human oversight through live video interaction — one of the most successful examples of RegTech adoption globally.

2. Anti-Money-Laundering and Fraud Detection

Anti-Money-Laundering (AML) compliance requires financial institutions to monitor transactions for suspicious behaviour. This is a task that human teams alone can’t manage at scale, efficiently.

How RegTech helps in these situations:

Behavioural analytics studies how money moves through systems like UPI, NEFT, or IMPS. If funds circulate repeatedly among linked accounts below reporting thresholds, the system flags the pattern.
Entity resolution links multiple accounts belonging to the same individual or shell company, helping investigators see the larger network.
Machine-learning models continuously learn from previous Suspicious Transaction Reports (STRs) submitted to the Financial Intelligence Unit (FIU-IND), improving future detection.

This approach replaces rule-based red-flagging with adaptive intelligence, significantly reducing false positives and audit fatigue.

3. Regulatory Reporting and “RegOps”

“RegOps”, short for Regulatory Operations, is the practice of automating the creation and submission of mandatory reports to regulators.

In the past, compliance officers exported data from different systems, formatted it manually, and emailed spreadsheets to RBI or SEBI. RegOps automates that entire chain.

APIs pull data directly from core banking and trading systems.
Validation scripts check for format accuracy and missing fields.
RPA (Robotic Process Automation) submits the data through secure channels, creating an audit trail.

The result is near real-time reporting and fewer human errors. Regulators are also adopting SupTech (Supervisory Technology) — cloud-based portals that receive these automated submissions, allowing continuous supervision rather than quarterly reviews.

4. Corporate and Third-Party Due Diligence

As companies outsource services and build larger partner networks, knowing who you are doing business with is now extremely critical. RegTech platforms automate third-party due diligence by combining corporate registries, litigation data, financial filings, and sanctions lists into a single risk profile.

For instance:

A bank assessing a new vendor can instantly check if the company’s directors appear on any regulatory watchlist or if their GST status is inactive.
Some solutions even use knowledge-graph visualisation to reveal hidden ownership — such as two suppliers connected to a single black-listed promoter.

In sectors like infrastructure and renewable energy, due diligence extends to land-record verification and developer validation, ensuring that titles are clean before project finance is released.

5. Data Privacy and Consent Management

With the government asking companies to stay compliant with the changing norms and upcoming bills and acts like the DPDP Act, data privacy has now become an area of significant interest for everyone.

RegTech platforms now include privacy modules that:

Log user consent and allow revocation at any time.
Automate data deletion after retention periods expire.
Generate proof of compliance during audits.

This ensures that personal data is used only for its intended purpose. For banks and insurers, it also strengthens customer confidence.

6. Risk and Governance Platforms

Many large financial institutions are replacing spreadsheet-based compliance trackers with integrated GRC (Governance, Risk, and Compliance) suites powered by RegTech. These systems map every regulation to internal policies and assign ownership within the organisation. Dashboards show real-time compliance status, overdue actions, and potential penalties.

7. Cross-Sector Adoption

While banking and NBFCs lead adoption, other sectors are catching up:

Insurance: IRDAI-regulated insurers use RegTech to screen agents, verify policyholder identity, and detect claim fraud.
Capital Markets: SEBI-supervised brokerages deploy trade-surveillance algorithms to detect insider trading or price manipulation.
FinTech and Payments: Merchant-onboarding APIs check business authenticity through PAN, GST, and UDYAM verifications.
Telecom and E-commerce: Platforms verify vendor legitimacy and monitor data privacy compliance under sectoral codes.

8. Continuous Compliance

Most companies and institutions are now racing towards continuous compliance, where checks occur automatically within business workflows rather than after the fact. A loan disbursement system, for example, won’t proceed unless KYC, PAN-GST matching, and bureau checks pass predefined thresholds, taking care of compliance before the risks emerge.

RegTech Uses Across Different Industries

Banking and Financial Services (BFSI)

The banking sector remains India’s largest RegTech user — not because it leads innovation, but because it faces the highest regulatory exposure. Every loan disbursal, fund transfer, or deposit activity sits under the RBI’s compliance framework.

To manage this volume, banks have adopted automated AML systems, real-time transaction-monitoring dashboards, and AI-driven risk-classification tools. The impact? What once took days and weeks of manual reconciliation is now handled in near real time. This translates to reduced compliance costs, faster reporting cycles, and little to no regulatory breaches.

FinTech and Digital Payments

FinTechs built their reputation on speed and simplicity — but that speed must coexist with accountability. RegTech ensures that growth doesn’t come at the cost of governance and compliance issues.

Payment aggregators and digital lenders now embed e-KYC APIs, sanction-screening checks, and consent-management systems directly into their platforms.

As UPI and wallet transactions continue to multiply, behaviour analytics engines monitor micro-payments for suspicious clustering, while RPA scripts prepare statutory reports automatically.

Insurance

Insurance companies face two significant hurdles: abiding by the regulations from IRDAI and the complex operations of verifying customers, intermediaries, and claims.

RegTech solutions help insurers verify agent credentials, policyholder identity, and claim authenticity in real time. OCR and facial-matching systems validate documents instantly, and anomaly-detection models flag duplicate or inflated claims.

With DPDP rules now binding insurers to safeguard sensitive health and financial data, including Personally Identifiable Information (PII), RegTech tools also handle consent logging, encryption auditing, and retention-period monitoring.

Capital Markets

The capital markets ecosystem, consisting of brokers, depositories, fund houses, and exchanges, uses RegTech to keep trading transparent and compliant with various regulatory guidelines.

Machine-learning systems analyse millions of orders to detect patterns such as circular trading, insider transactions, or collusive behaviour. Trade-surveillance tools also cross-reference market data with communication logs and timing patterns, producing alerts within seconds rather than days.

Fund houses employ automated compliance dashboards to track investment limits, related-party transactions, and exposure thresholds. The net effect is a market that can self-monitor almost as quickly as it trades.

Corporate and Enterprise Sector

Procurement and compliance teams in companies use integrated platforms to assess vendor legitimacy, cross-verify director identities through MCA filings, track litigation exposure, and monitor credit signals. For manufacturers, logistics providers, and infrastructure developers, this prevents reputational risk from non-compliant partners.

In real-estate-linked sectors, land-record verification and ownership checks are now standard before financing or acquisitions. Continuous monitoring ensures that any change in ownership, insolvency status, or regulatory flag triggers an instant alert.

Regulators and Supervisory Bodies

Regulators themselves are becoming part of the RegTech ecosystem through Supervisory Technology (SupTech). RBI and SEBI are piloting frameworks where banks and intermediaries submit structured data via APIs rather than static reports.

This allows supervisors to track compliance indicators continuously, identify systemic risks earlier, and reduce manual interpretation errors. For the first time, both the regulator and the regulated are operating on a shared digital backbone — improving transparency and mutual trust.

Differences Between FinTech and RegTech

FinTech and RegTech are two terms that you will find used often, interchangeably. However, they are not the same thing. FinTech, which reimagines how money moves, and RegTech, which ensures that those movements remain compliant and secure.
Both rely on data, automation, and APIs, yet their intent and impact differ heavily.

What Is FinTech?

FinTech — short for financial technology — transformed finance from a slow, paper-driven process into a click-based service. In India, it turned payments into tap-to-pay experiences and lending into instant approvals. From UPI and neobanks to BNPL and digital investment apps, FinTech built the rails that now carry billions of daily transactions.

The sector’s purpose is inclusion and efficiency: bringing formal financial services to every smartphone user. But that very scale creates vulnerabilities.
Every new API call, every customer onboarding, and every stored dataset introduces regulatory exposure — around data protection, anti-money-laundering (AML), and KYC compliance.
This need for constant, automated oversight gave rise to RegTech.

FinTech vs RegTech — Key Differences

Aspect	FinTech	RegTech
Core Purpose	Expand access and convenience	Ensure compliance, accuracy
Primary Users	Consumers, lenders, merchants	Banks, regulators, compliance teams
Focus Area	Payments, credit, wealth	KYC, AML, reporting
Measure of Success	Adoption and revenue	Trust and risk reduction

How RegTech Complements FinTech

In practice, the two work in tandem.

A lending app relies on RegTech APIs to verify PAN, Aadhaar, and CKYC data instantly.
A payments platform uses transaction-monitoring engines to flag suspicious behaviour.
An insurance portal automates claim checks and records every consent trail.

FinTech drives customer interaction; RegTech ensures regulatory integrity. Together, they make financial inclusion sustainable rather than experimental.

AuthBridge As Your RegTech Partner

Indian regulators have moved from periodic oversight to continuous supervision, with many of the regulators now requiring evidence of continuous compliance. Here’s why AuthBridge remains one of the top RegTech platforms in India today:

1. Automating RBI KYC and PMLA Obligations for the BFSI Sector

Identity APIs linking PAN, Aadhaar (offline XML/QR modes), CKYC, Voter ID, and Udyam registries.
AML Screening against RBI, SEBI, FIU-India, and global watchlists.
Geo-verified Video KYC using face-match, liveness, and timestamped audit logs to satisfy RBI’s V-CIP norms.
Regulatory Reporting Feeds are exportable in machine-readable formats for RBI inspection tools like DAKSH.

This replaces paper-based KYC and spreadsheet tracking with verifiable digital records that meet both RBI and FIU expectations.

2. Fraud Prevention and Agent Verification

Agent Licence Verification is directly mapped to the IRDAI registries.
OCR and Document AI to extract and validate policy and claim data.
Facial Recognition and Duplicate-Claim Detection to flag fraud patterns.
Consent and Data Handling Workflows aligned to DPDP privacy principles.

Insurers can establish audit trails for every agent and claim interaction without manual reconciliation.

3. Capital Markets

Corporate KYB & UBO Mapping via MCA and GSTN data to identify direct and indirect owners.
Litigation and Adverse-Media Screening using NLP to detect disclosure risks.

Brokerages and fund houses use these feeds to maintain “always-clean” UBO records for SEBI reporting.

4. Third-Party Due Diligence and ESG Readiness

Vendor and Distributor Verification through MCA, GST, and Udyam registries.
Litigation & Insolvency Tracking via NCLT and court databases.
Land and Asset Ownership Verification for project finance and lease compliance.
Periodic Re-verification triggers when ownership or registration changes.

This gives manufacturers and developers evidence-based supply-chain integrity for ESG and anti-bribery audits.

5. Data Protection and Consent in line with DPDP Act

Consent Ledger: Cryptographically sealed consent artefacts linked to every verification.
Role-Based Access and Data Residency Controls: ensuring processing within India.
Retention and Deletion Automation: for DPDP Schedule compliance.

Organisations can produce proof of lawful processing and user consent on demand.

6. Technology Stack and Delivery Assurance

Secure API Gateway with JWT/OAuth authentication and transaction-level logging.
AI/ML Models for OCR, face comparison, liveness detection, and document classification.
NLP Pipelines for court data and adverse-media analysis.
India-hosted cloud infrastructure for regulatory data residency.

Across BFSI and enterprise sectors, AuthBridge’s RegTech infrastructure allows compliance teams to generate machine-readable evidence aligned with RBI, SEBI, IRDAI, and DPDP requirements. It transforms oversight into operational governance, where every KYC, KYB, and consent record is instantly provable.

By Abhinandan, 1 dayOctober 28, 2025 ago

Background Checks

Agentic AI In Employee Onboarding: Benefits & How To Implement

Introduction

With the trends around hiring and background verification changing faster than ever, thanks to AI, HR teams and BGV (Background Verification) professionals often find trouble in employee onboarding and document verification. It’s a critical but time-consuming task that involves sifting through numerous documents, such as payslips, offer letters, and employment records, to verify accuracy, completeness, and consistency.

The process is far from perfect. With human errors, long turnaround times (TAT), and the inevitable insufficiencies (missing or inaccurate documents), this process not only delays hiring decisions but also increases operational costs. This inefficiency is amplified when candidates submit incomplete or inconsistent data, which leads to back-and-forth communication, further delaying the process.

But what if there was a way to automate these tasks and make the verification process more efficient and accurate? This is where Agentic AI comes into play.

Agentic AI is transforming employment verification by automating document checks, identifying missing information, and interacting with candidates autonomously, making the entire process quicker, more accurate, and far more scalable.

In this blog, we’ll explore how Agentic AI works, its role in transforming employment verification, and how AuthBridge’s AI services can help HR teams optimise their background verification processes, making them faster, more accurate, and cost-efficient.

What Is Agentic AI And How Does It Work?

Artificial Intelligence (AI) has evolved significantly in the past decade. From simple automation tasks to complex decision-making systems, AI’s capabilities are transforming industries across the globe. Agentic AI, however, represents a next-generation leap in this evolution, particularly for complex workflows like employment verification.

While traditional AI models focus on handling isolated tasks, such as identifying patterns in data or providing recommendations, Agentic AI extends these capabilities by introducing autonomy, adaptation, and decision-making within dynamic and complex environments. Autonomous agents equipped with decision-making abilities can act independently, choose between alternative solutions, and self-improve over time.

This ability to make independent decisions and act autonomously within a predefined goal structure is the hallmark of Agentic AI. Instead of simply executing predefined tasks based on input, Agentic AI systems can independently perceive the environment, reason through complexities, plan and adapt their actions, and execute tasks in a continuous feedback loop.

The Key Technologies Behind Agentic AI

Let’s break down the main components that make Agentic AI so powerful, particularly in employment verification.

1. Perception and Data Extraction: OCR and Computer Vision

The first step in automating employment verification is the ability to extract data from the vast range of documents candidates submit. Traditional document review is manually intensive, often requiring HR professionals to cross-check various employment records like pay slips, offer letters, and relieving letters.

Optical Character Recognition (OCR): OCR is at the heart of Agentic AI’s document reading capability. OCR technology scans documents, whether they are images, PDFs, or handwritten, and extracts relevant textual data. This includes identifying candidate names, job titles, salaries, dates of employment, and more. Unlike basic OCR used in many document management systems, Agentic AI’s OCR is augmented by deep learning to handle handwriting, varied fonts, and complex document structures.
Computer Vision: Beyond reading the text, computer vision technologies enable the AI to understand the visual structure of documents. It can verify the authenticity of a document by analysing logos, stamps, signatures, and other visual cues that signify validity or tampering. This capability is critical in preventing fraud or errors in document submission.

2. Contextual Understanding: Natural Language Processing (NLP)

While OCR extracts raw data from documents, Natural Language Processing (NLP) steps in to ensure the AI understands the context and meaning of the data. This is especially critical in employment verification, where the AI must interpret various documents, each with potentially different formats and structures.

Data Normalisation: NLP helps standardise the extracted data (such as employment dates or job titles) so the AI can consistently compare it across multiple documents. For example, it may detect that “Director” and “Manager” are synonymous roles in different contexts.
Discrepancy Detection: NLP enables the system to understand contextual discrepancies between documents. For instance, if a candidate’s job title on the offer letter doesn’t match the one listed on the pay slip, NLP helps the AI identify this mismatch. The system then knows to either flag this issue for human review or request clarification from the candidate.
Semantic Understanding: Beyond simple keyword extraction, NLP allows the AI to understand the relationship between different pieces of information (e.g., matching employment dates between a pay slip and offer letter), ensuring that any inconsistencies are flagged.

3. Decision-Making & Autonomy: Agent Planning and Reinforcement Learning

The true power of Agentic AI lies in its decision-making abilities. These systems make decisions based on the data they’ve processed, then take action. This decision-making is powered by technologies like Agent Planning and Reinforcement Learning.

Agent Planning: At the core of Agentic AI’s decision-making is its ability to plan and orchestrate workflows. For instance, when processing an employment verification case, the AI may first validate documents, then detect insufficiencies, and finally, send follow-up requests to the candidate. These actions are carefully planned and executed based on predefined rules, but also take real-time information into account (such as a candidate’s historical response time).
Reinforcement Learning: One of the critical features that sets Agentic AI apart is its learning capability. Using Reinforcement Learning (RL), the AI continuously improves its decision-making over time. It learns from feedback, refining its actions based on successful (or unsuccessful) interactions. For example, if the AI learns that a candidate is often slow to respond to an email request for documents, it might try different communication channels, such as SMS or even chatbots, to increase response rates.

4. Context-Awareness & Feedback Loops: Memory and Adaptation

One of the distinguishing features of Agentic AI is its memory. While traditional AI systems treat each task independently, Agentic AI can remember prior actions and interactions, using this memory to improve future decision-making. This is especially useful in employment verification, where context is often key to understanding the verification workflow.

Memory and Adaptation: Agentic AI retains a record of previous interactions with a candidate or a particular verification process. If a candidate has submitted incomplete documents in the past, the AI can adapt by requesting additional documents upfront, saving time and reducing the likelihood of future insufficiency cases.
Feedback Loops: The AI also benefits from continuous feedback loops. As it processes more cases, it learns to make better decisions. For example, if the system initially struggles with a certain document type (e.g., handwritten forms), it will adapt by learning from mistakes and improving its recognition accuracy.

How Agentic AI Helps In Employee Background Verification & Onboarding

To understand how Agentic AI helps in employment verification, let’s break down its impact across key stages of the workflow.

1. Automating Document Verification

In legacy-based background verification processes, HR professionals manually review each document submitted by a candidate. They check for key details like the candidate’s name, job title, salary, dates of employment, and more. This requires constant human oversight and can be prone to mistakes due to the volume of documents handled.

With Agentic AI, this process is fully automated, thanks to OCR (Optical Character Recognition) and Natural Language Processing (NLP):

Agentic AI scans documents and automatically extracts text from payslips, offer letters, and other employment records. OCR technology allows the AI to interpret both printed and handwritten text, so even non-structured documents are accurately processed.
While OCR provides the raw data, NLP ensures that the AI can understand the context of the extracted information. For instance, it can discern whether a job title on a pay slip matches the one on the offer letter, or if the dates of employment are consistent across documents.

The result is faster, more accurate document verification with zero human errors.

2. Cross-Document Validation

Once data is extracted from the documents, the next step is cross-checking this information. In traditional systems, HR teams have to manually compare the data in the offer letter, pay slip, relieving letter, and other documents to ensure consistency. This step is not only time-consuming but also error-prone, particularly if the documents are in different formats or contain different levels of detail.

With Agentic AI:

Automatic Data Matching: Agentic AI doesn’t rely on manual comparison. It cross-checks information across all documents submitted by the candidate. For example, it ensures the date of joining on the pay slip matches the one on the offer letter.
Flagging Inconsistencies: If any discrepancies are found — such as inconsistent job titles, salary mismatches, or incorrect employment dates — the AI flags them for further review or action. It ensures that nothing is overlooked.

This removes the manual effort and the potential for missed discrepancies, allowing the verification team to focus on cases that require human judgment, while Agentic AI handles the repetitive checks.

3. Insufficiency Detection and Resolution

Insufficiency is one of the most frustrating and time-consuming aspects of employment verification. When candidates submit incomplete or incorrect documents, HR teams must reach out to candidates to request the missing information. This creates a back-and-forth communication loop, delaying the verification process and creating a poor candidate experience.

With Agentic AI, this inefficiency is eliminated:

Automated Insufficiency Detection: As Agentic AI scans documents, it automatically detects any insufficiencies in the submitted documents. For instance, if a relieving letter is missing or if a pay slip doesn’t match the offer letter, the AI immediately identifies the issue.
Auto-Resolution: Agentic AI can automatically generate and send requests to the candidate for the missing documents. This happens in real-time, reducing delays and ensuring continuous progress.
Escalation and Follow-ups: If the candidate fails to respond to the initial request, the AI can escalate the issue or send additional reminders. This reduces the burden on HR staff to chase candidates for missing information.

Agentic AI helps to speed up the verification process by automatically detecting and addressing insufficiencies, resulting in faster turnaround times (TAT) and a smoother candidate experience.

4. Real-Time Monitoring and Decision Making

While automation significantly speeds up the employment verification process, it’s important to note that Agentic AI is more than just an automation tool. It also provides real-time monitoring and decision-making capabilities, which can dynamically adjust the verification process based on the situation.

Agentic AI continuously monitors the progress of each case, ensuring that it moves through the workflow without delay.
If the system encounters a complex case (e.g., an unusual document format), it can adjust its approach in real-time. It can escalate the case to a human HR professional or alter its action plan to deal with the issue more effectively.

This capability is critical in ensuring that complex cases are handled appropriately, while routine tasks continue to be processed autonomously.

5. Seamless Integration into Existing Systems

A major advantage of Agentic AI is its ability to integrate into your existing HR or BGV systems seamlessly. Rather than requiring a complete overhaul of your infrastructure, Agentic AI works alongside your current tools, enhancing your workflows without disrupting existing processes.

Plug-and-Play Integration: Agentic AI integrates easily with your existing HRMS (Human Resource Management System) or background verification platform, ensuring smooth data flow between systems.
API-Driven: Integration is typically API-driven, making it quick and easy to set up without requiring significant system changes.

This non-intrusive integration means that HR teams can continue using their current systems, while reaping the benefits of a more automated and efficient verification process.

Benefits Of Agentic AI In Employee BGV & Onboarding

The introduction of Agentic AI into employment verification has numerous advantages in terms of efficiency, accuracy, and cost-effectiveness. These benefits directly address the pain points typically encountered in manual, error-prone verification workflows, improving overall HR operations.

1. Faster Turnaround Times (TAT)

One of the most significant improvements brought about by Agentic AI is the drastic reduction in turnaround time (TAT) for employment verification. Traditional manual verification processes involve multiple steps, such as document submission, manual checks, cross-referencing, and follow-ups, all of which contribute to long delays.

With Agentic AI:

Documents are automatically processed in real-time, significantly reducing the time spent on manual checks.
The AI system cross-verifies information across various documents instantly, which eliminates the need for manual comparison and validation.
If a document is missing or there’s an inconsistency in the information, Agentic AI flags the issue immediately and initiates an automated resolution process.

The result? What once took days can now be completed in minutes or hours, ensuring that candidates’ employment verification is processed much faster, accelerating the hiring process.

2. Cost Savings

Automating employment verification with Agentic AI leads to significant cost savings. Traditional verification processes are resource-intensive, requiring HR teams to manually review and cross-check documents, chase candidates for missing information, and deal with discrepancies. These manual tasks are not only time-consuming but also costly.

With Agentic AI, much of this work is automated, reducing the need for human involvement in routine tasks. As a result, companies can save on:

Manpower Costs: By reducing the need for manual intervention in document checks, follow-ups, and data entry, organisations can cut down on HR department overheads.
Operational Costs: The AI-driven automation reduces the need for specialised verification teams, freeing up resources for other important HR functions.
Error Mitigation Costs: Human errors in verification often lead to costly mistakes, such as incorrect hires or compliance issues. Agentic AI significantly reduces the risk of such errors.

3. Improved Accuracy

Human error is one of the primary reasons for inefficiencies and delays in employment verification. Agentic AI helps eliminate this by providing precise, consistent, and reliable validation.

Here’s how Agentic AI improves accuracy:

Error-Free Data Extraction: OCR and NLP technologies extract data with 100% accuracy, minimising human error in data entry.
Cross-Document Consistency: Agentic AI ensures that the information across different documents matches consistently, such as employment dates, job titles, and salaries. This eliminates discrepancies that may occur with manual verification.
Fraud Detection: By leveraging computer vision, Agentic AI can identify forged documents and tampered information, which might go unnoticed during manual checks.

4. Enhanced Candidate Experience

The speed, accuracy, and automation provided by Agentic AI also greatly improve the candidate experience during the verification process. Candidates no longer have to deal with the frustration of waiting for weeks to have their documents validated or following up multiple times to provide missing information.

5. Scalability

As businesses grow, so does the volume of employment verification required. Manual processes can’t scale to meet the increased demand. Agentic AI is designed to handle large volumes of documents and verification cases without additional cost or operational overhead. It allows businesses to scale their employment verification processes as they expand, without the need to hire more HR staff or outsource verification tasks.

How Agentic AI Enables Scalability:

Handling High Volumes With Ease: Whether your organisation hires 50 people per month or 500, Agentic AI can handle the same volume of work without compromising on quality or speed.
No Additional Human Resources: As the demand for employment verification increases, Agentic AI can simply be scaled up without needing to hire more personnel or invest in additional infrastructure.

6. Future-Proofing HR Operations

Reinforcement learning and continuous adaptation allow Agentic AI to grow smarter with every case it processes, ensuring that your HR systems remain future-proof and prepared for future challenges.

How Agentic AI Future-Proofs Your Processes:

Constant Improvement: The AI doesn’t just work today; it improves tomorrow based on lessons learned from previous verification cases.
Adaptability: Agentic AI is capable of adapting to new types of documents, different formats, and new verification requirements as they emerge, ensuring your processes stay up to date.

How To Implement Agentic AI In Employee Onboarding

Adopting Agentic AI for employee onboarding is a move that can significantly enhance efficiency, accuracy, and scalability. However, successful implementation requires careful planning, the right technical integration, and a structured approach to ensure that the AI system operates seamlessly within existing HR workflows.

In this section, we’ll outline the key steps involved in implementing Agentic AI for employment verification, from technology integration to pilot programs and scalability considerations.

1. Assess Your Existing Verification Process

Before adopting Agentic AI, it’s important to assess your current employment verification process. This will help you understand where automation can have the most significant impact and what areas need improvement.

Key Questions to Ask During Assessment:

How much time is spent on document verification? Identify bottlenecks and areas where manual verification is slowing down the process.
What errors are most common in the process? Pinpoint areas where human error is causing discrepancies, missed documents, or delays.
How often do you experience issues with incomplete or inconsistent documentation? Evaluate how much time HR teams spend chasing candidates for missing or incorrect documents.
What’s the volume of cases? Consider the scale of verification required, particularly if your company experiences fluctuations in hiring demand.

By answering these questions, you can pinpoint the areas where Agentic AI can deliver the most immediate and measurable improvements.

2. Choosing the Right Technology Solution

Once you’ve assessed your current process and identified areas for improvement, the next step is to choose the right Agentic AI-powered solution for your business. It’s crucial to select a solution that aligns with your verification needs and integrates seamlessly with your existing HR infrastructure.

Key Factors to Consider:

Integration with Existing HR Systems: Ensure that the Agentic AI solution integrates smoothly with your HRMS (Human Resource Management System), document management system, and other tools used in the verification process.
Scalability: Choose a solution that can scale with your growing verification needs. Agentic AI should be able to handle increases in the volume of documents without requiring additional resources or slowing down the process.
Customisation: Verify that the solution can be customised to suit your specific verification requirements (e.g., handling different types of employment records or country-specific verification standards).

3. Implementing the Solution: Technology Integration

Once you’ve selected the right Agentic AI solution, the next step is to integrate it into your existing systems. This stage requires collaboration between your HR teams, IT teams, and AI providers to ensure smooth implementation.

Steps in Integration:

API Integration: Most Agentic AI solutions are API-driven, which means they can be easily integrated with your HRMS, BGV platforms, and document management systems. This allows you to seamlessly transfer data between platforms without disrupting your existing infrastructure.
Data Flow Setup: Set up the data flow for document submission, verification, and reporting. Ensure that data is properly extracted from documents and sent through the verification process automatically, with results being fed back into your system in real-time.
User Interface (UI) Customisation: While the AI operates autonomously in the backend, HR teams will still need an intuitive user interface to monitor progress, intervene when necessary, and track verification cases. Customising the UI to meet your team’s needs will ensure ease of use.
Data Security and Compliance: Given the sensitive nature of employment verification, ensure that your Agentic AI solution complies with all relevant data protection regulations (e.g., GDPR for European candidates, DPDP in India). Encryption and secure data storage should be prioritised.

4. Running Pilot Programs

Implementing Agentic AI at scale can seem daunting, but pilot programs are an excellent way to test the system’s performance and measure its effectiveness before a full rollout.

Steps for Pilot Implementation:

Select a Test Group: Choose a subset of your hiring processes or candidates for the pilot program. This could include a particular department or job type with a consistent volume of verifications.
Define Metrics for Success: Set clear KPIs (Key Performance Indicators) to measure the success of the pilot. This could include TAT reduction, cost savings, accuracy rates, and candidate experience scores.
Monitor Performance: Track the AI’s performance closely during the pilot phase. Monitor how well it handles different document types, identifies insufficiencies, and integrates into your existing workflow.
Collect Feedback: Gather feedback from both HR teams and candidates involved in the pilot. This will help identify any areas for improvement before full-scale implementation.

5. Training and Upskilling HR Teams

While Agentic AI can handle much of the verification work autonomously, it’s still essential that HR professionals understand how to work with the system and interpret its results. Training and upskilling your HR teams will ensure they can leverage the AI to its full potential.

Training Focus Areas:

Understanding AI Outputs: Train HR staff on how to interpret the results generated by Agentic AI, particularly when it comes to insufficiency flags and cross-document validation.
Handling Complex Cases: While Agentic AI handles routine cases, there will still be edge cases that require human intervention. Train HR professionals on how to handle these cases.
AI System Feedback: Ensure HR teams understand how reinforcement learning works within the system and how their feedback will improve the AI over time.

6. Scaling the Solution

Once the pilot program has been successful, you can move to scaling the solution across your entire organisation. This involves expanding the use of Agentic AI to handle a larger volume of verifications, and possibly even different types of employment checks (e.g., educational verification, reference checks).

Considerations for Scaling:

Increased Volume Handling: Ensure your Agentic AI solution can handle the higher volumes of documents as your company grows or during peak hiring seasons.
Custom Workflows: Customise workflows for different types of hires (e.g., full-time employees, contractors, remote workers) to ensure the AI handles each case appropriately.
Global Expansion: If your company is expanding internationally, ensure your Agentic AI system can handle country-specific verification requirements and document formats.

7. Continuous Improvement and Monitoring

Once Agentic AI is fully implemented and scaled, continuous monitoring is essential to ensure the system continues to function at peak performance. The beauty of Agentic AI is that it’s not a static solution; it continuously learns from each verification case, becoming more accurate and efficient over time.

Ongoing Monitoring:

Track Key Metrics: Continue to track the KPIs defined during the pilot phase (e.g., TAT, cost savings, accuracy) to ensure the system is meeting performance expectations.
AI Learning: The reinforcement learning model of Agentic AI ensures that it continuously improves as more data is processed. However, regular review and fine-tuning may still be necessary.
Feedback Loops: Collect feedback from HR teams and candidates to identify any areas where the system can be improved further.

Why Should You Choose AuthBridge’s Agentic AI Solution?

AuthBridge’s Agentic AI provides an advanced, AI-powered solution that optimises the employment verification process by automating critical tasks such as document validation, cross-checking data, and insufficiency handling. The result? A faster, more accurate, and cost-effective system that eliminates traditional bottlenecks and enhances HR operations.

Let’s explore AuthBridge’s Agentic AI solution and how it provides measurable benefits for HR teams looking to improve employment verification workflows.

1. Real-Time Document Processing and Accuracy Enhancement

AuthBridge’s Agentic AI automates document verification with real-time data extraction and contextual understanding.

Agentic AI extracts relevant data from multiple document types (e.g., job titles, salary, employment dates) within minutes, reducing the time spent on manual data entry.
By utilising Natural Language Processing (NLP), Agentic AI understands the context behind the data. For example, it checks if job titles, dates, and salaries are consistent across documents, automatically flagging any discrepancies.
With computer vision capabilities, Agentic AI detects tampered documents by validating logos, stamps, and signatures, preventing fraudulent submissions.

AuthBridge’s Agentic AI reduces document verification times by up to 80%, processing documents within minutes instead of days.

2. Effortless Insufficiency Detection and Automated Follow-ups

Automated Insufficiency Detection: The system instantly detects missing documents or inconsistencies (e.g., missing relieving letter or mismatched job titles) and flags them in real-time.
Automated Candidate Follow-ups: Agentic AI sends real-time notifications to candidates, requesting missing documents through email, SMS, or WhatsApp, ensuring swift resolution.
Seamless Escalation: If a candidate does not respond, the system automatically escalates the issue to HR teams for immediate attention.

For multiple clients using AuthBridge’s Agentic AI, follow-up times have reduced by 60%, ensuring quicker resolutions and improved candidate satisfaction.

Seamless Integration with Existing HR Systems

Integrating Agentic AI into existing HR workflows is simple, requiring no major overhaul of your current infrastructure. AuthBridge’s AI solution is designed to integrate smoothly with your HRMS and BGV platforms via API.

Seamless Data Flow: Agentic AI integrates with your existing systems, allowing for real-time document submission, data extraction, and verification results.
No Disruption to Current Workflows: HR professionals can continue using their existing tools while Agentic AI automates verification tasks, ensuring business continuity.

4. Scalable Solutions for High-Volume Hiring

During seasonal hiring peaks or rapid business growth, traditional manual systems struggle to handle high volumes of verification tasks efficiently. Agentic AI can scale effortlessly to meet increasing demands without compromising performance. AuthBridge’s Agentic AI has helped organisations scale their verification processes by up to 80% during peak periods without increasing costs or needing additional staff.

5. Advanced Enterprise-Grade Security and Compliance

Data security is critical in employment verification. AuthBridge’s Agentic AI solution is designed to ensure high security and compliance with local and international regulations.

End-to-End Encryption: Agentic AI ensures that all sensitive data is encrypted, safeguarding against unauthorised access and ensuring data confidentiality.
GDPR and Privacy Compliance: AuthBridge’s solution complies with GDPR, DPDP, and other data protection regulations, making it easier to handle sensitive candidate data responsibly.
Audit Trails: The solution automatically generates audit logs, providing full traceability for all actions taken during the verification process.

AuthBridge’s Agentic AI is fully compliant with global privacy laws and offers enterprise-grade security, ensuring that all data remains protected and audit-ready.

6. Continuous Learning and Adaptation

Through reinforcement learning, AuthBridge’s Agentic AI system continuously improves its performance, becoming more efficient at handling complex document verification tasks.

Agentic AI evolves as it processes more cases, refining its ability to identify discrepancies, handle complex documents, and improve verification accuracy.
With each case, Agentic AI learns to make better, more accurate decisions, ensuring that it handles each verification task with increasing precision.

Clients using Agentic AI report a 30% improvement in verification accuracy after just six months, thanks to the AI’s continuous learning capabilities.

Conclusion

By automating tasks like document validation, cross-checking data, and insufficiency resolution, Agentic AI significantly reduces verification time, enhances accuracy, and lowers operational costs. With its ability to seamlessly integrate into existing systems, Agentic AI not only accelerates the hiring process but also improves candidate experience and enables HR teams to scale efficiently during peak hiring periods.

By Abhinandan, 2 weeksOctober 17, 2025 ago

Background Checks

AI-Based Document Classification: All You Need To Know

Introduction To AI In Document Processing

Many organisations today are drowned in documents, be it digital or physical, structured or messy, scanned or typed. HR teams, financial institutions, insurers, and compliance departments spend countless hours handling files that range from résumés and ID proofs to contracts and bank statements. IDC estimates that over 80% of enterprise data is unstructured, and most of it remains underutilised because it cannot be processed at scale through traditional systems. As businesses race to automate, Artificial Intelligence (AI) has emerged as the key entity to bringing structure to this data. In particular, AI-based document classification, a field utilising machine learning (ML) and natural language processing (NLP), is changing how organisations read, understand, and act on documents in real time. What was once a manual, error-prone process that required teams of people to review pages of text is now handled by AI systems that can interpret thousands of documents per minute, extract relevant details, and classify them automatically. This leap not only reduces operational costs but also strengthens compliance, accuracy, and speed. From HR onboarding and background checks to legal due diligence and financial verification, AI-based document classification has become a key enabler behind every efficient digital workflow. And AuthBridge is taking it further — combining deep AI models with verification intelligence to build a future where trust and automation coexist seamlessly.

What Is AI-Based Document Classification, And How Does It Work?

Document classification powered by artificial intelligence is far more than automated sorting. It is an integrated cognitive system designed to read, understand, and reason with information contained in documents of all shapes and structures. At its core, it replicates human comprehension, recognising layout, language, tone, and purpose, but executes this reasoning at a scale and consistency unattainable for people. The technology draws on four AI disciplines: Computer Vision, Natural Language Processing (NLP), Machine Learning (ML), and Knowledge Engineering. Together, these elements build an end-to-end pipeline that can interpret a document from the moment it is uploaded to the instant it is routed into a business workflow.

1. Document Ingestion and Normalisation

The pipeline begins with data ingestion, where files arrive from multiple sources, including applicant-tracking systems, Customer Relationship Management systems (CRMs), email gateways, cloud storage, and Robotic Process Automation (RPA) bots. The ingestion layer uses connectors and message queues to ensure high-volume handling and traceability. Once collected, the pre-processing stage cleanses and standardises every file:

Image normalisation: rotation correction, de-skewing, and noise reduction improve clarity.
Compression and binarisation: optimise document weight without compromising text quality.
Segmentation: divides the page into logical regions such as headers, tables, or signatures.

This step transforms unstructured image data into an OCR-ready format that preserves spatial cues.

2. Optical and Intelligent Character Recognition

Here, Optical Character Recognition (OCR) and Intelligent Character Recognition (ICR) engines convert visual patterns into machine-readable text. Modern systems employ deep-learning OCR models that recognise fonts, handwritten content, and multi-language scripts with confidence scores for each recognised token.

OCR extracts printed characters and numbers.
ICR extends this capability to cursive or handwritten text.
Layout analysis preserves positional metadata ( coordinates of text blocks, bounding boxes, and reading order).

The outcome is a digitised document object model where every word, number, and graphical element is mapped precisely in a coordinate space.

3. Feature Extraction and Semantic Enrichment

After text extraction, the system moves from visual to linguistic understanding. The NLP layer performs multiple analyses:

Tokenisation and lemmatisation — breaking text into fundamental units and normalising words to their roots.
Part-of-speech tagging and dependency parsing — determining grammatical relationships that reveal meaning.
Named-entity recognition (NER) — identifying entities such as company names, PAN numbers, addresses, or degrees.
Semantic embeddings — converting words and phrases into numerical vectors that capture context.

State-of-the-art models integrate both text and layout features, enabling the model to comprehend that a number located under “Invoice Total” is a financial figure, while the same pattern elsewhere could be a roll number on a certificate.

4. Model Training and Classification

The classification engine is trained on a corpus of annotated documents, each labelled by type (for example, Aadhaar Card, Payslip, Offer Letter, Bank Statement). Training follows a supervised learning approach, in which the model learns statistical patterns unique to each document class. Common architectures include:

Model Type	Description	Use Case
Support Vector Machines (SVM)	Classical ML model using text features	Structured text documents
Convolutional Neural Networks (CNN)	Captures visual cues and layout	Scanned forms, IDs
Recurrent / LSTM Networks	Learns sequential dependencies	Narrative or multi-page documents
Transformer Models (BERT, RoBERTa, Longformer)	Encodes long-range relationships	Mixed-content enterprise data

During inference, the trained model assigns a probability distribution across potential document classes. A confidence threshold determines whether the classification is accepted automatically or escalated for human review.

5. Validation and Business-Rule Enforcement

Classification alone is not enough; validation ensures trustworthiness. A business-rule engine checks extracted attributes against defined logic:

PAN numbers must follow an alphanumeric structure.
Dates of birth must indicate an age above the legal threshold.
Educational degrees should align with recognised universities.

For compliance-sensitive sectors, integration with external verification APIs (such as DigiLocker or NSDL) confirms the authenticity of data, transforming classification into verified intelligence.

6. Human-in-the-Loop and Continuous Learning

Low-confidence predictions enter a Human-in-the-Loop (HITL) interface where reviewers verify and correct outcomes. Each correction is captured and fed back into the active-learning mechanism. Periodic retraining through MLOps pipelines ensures that the model evolves with new templates, formats, and regulatory updates. This creates a self-improving system: the more it processes, the smarter and faster it becomes.

7. Integration and Orchestration

Finally, classified and validated documents are passed to downstream systems, onboarding dashboards, ERP modules, or audit repositories, through secure APIs. The entire flow is orchestrated via Business Process Management (BPM) or Robotic Process Automation (RPA) platforms, enabling straight-through processing with complete audit trails.

Why Is AI-Based Document Classification Important?

From Operational Bottlenecks to Data Intelligence

For decades, documents have been the slowest link in an otherwise digital chain. Even the most advanced enterprises still depend on manual interpretation for onboarding, compliance, and auditing. The cost is both time and lost intelligence. Every scanned invoice, employee ID, or contract represents unstructured data — information that remains dormant unless technology can understand it. AI-based document classification turns these static assets into operational intelligence. Instead of spending hours identifying document types or verifying details, organisations can focus on using that information — approving a loan faster, onboarding a candidate sooner, or closing an audit with confidence.

Quantifying The Business Impact

When implemented effectively, document classification improves outcomes across every significant operational metric.

Turnaround Time (TAT): Automated classification and routing shorten verification cycles from hours to seconds, directly improving customer experience and employee productivity.
Accuracy and Consistency: AI models trained on thousands of samples apply identical logic across every file. Human reviewers handle only exceptions, ensuring both speed and reliability.
Scalability: Unlike manual teams, AI systems scale linearly with data volume. Seasonal surges — for example, in insurance claims or campus hiring — no longer create operational strain.
Audit Readiness: Each classification carries metadata (model version, timestamp, reviewer ID, and confidence score), producing a complete audit trail — something regulators increasingly expect.

AI-Based Document Classification Use Cases

Human Resources and Workforce Onboarding

Recruitment and background verification are document-intensive processes. AI-based classification enables instant identification of payslips, degree certificates, and identity proofs. Each is automatically directed to its respective verification workflow — digital ID validation, education check, or employment history match. The outcome is faster onboarding, fewer compliance errors, and a traceable audit trail for every employee record.

Banking, Financial Services, and Fintech

Banks, NBFCs, and fintech firms manage stringent Know Your Customer (KYC) and Anti-Money Laundering (AML) mandates. AI classification streamlines these by recognising and mapping uploaded documents to Officially Valid Documents (OVDs) under Reserve Bank of India norms. When integrated with digital-public infrastructures such as DigiLocker, the process allows instant authentication while maintaining full compliance with FATF and RBI guidelines.

Insurance and Healthcare

Claims processing and underwriting depend on rapid evaluation of policy documents, invoices, and medical reports. AI models can distinguish between these categories and trigger appropriate checks — medical scrutiny, fraud review, or reimbursement validation — improving both TAT and accuracy.

Legal, Governance, and Risk Functions

In law firms and corporate legal teams, classification accelerates document discovery. Contracts, NDAs, and case files are automatically grouped and indexed. Key clauses or dates can be extracted and compared across hundreds of documents in minutes, allowing legal and risk teams to focus on strategic analysis rather than mechanical search.

Procurement and Supply Chain

Invoice verification, purchase-order matching, and vendor due diligence tasks are all document-heavy. AI classification identifies each document type, validates structure and content, and integrates results with enterprise resource planning (ERP) systems to enable faster payment cycles and stronger financial control.

Turning Compliance and Security Into Competitive Advantage

In regulated industries and sectors, compliance is often perceived as a cost centre. Intelligent classification converts it into a differentiator. Because every document is handled under traceable logic, organisations gain defensible transparency — the ability to show regulators not only what was done but how it was done. Modern classification systems incorporate privacy-by-design principles:

Encryption at rest and in transit to protect sensitive data.
Role-based access controls to restrict visibility to authorised users.
Anonymisation or redaction of personally identifiable information during model training.

These controls align with frameworks such as the EU GDPR and India’s Digital Personal Data Protection Act (2023), reducing compliance exposure while strengthening customer trust.

The Shift from Automation to Organisational Intelligence

The next stage of maturity is not faster automation but smarter orchestration. Once classification becomes reliable, it acts as the backbone for more advanced capabilities:

Intelligent routing that prioritises high-risk or high-value documents.
Predictive analytics that detect anomalies or fraud patterns early.
Self-learning feedback loops that refine accuracy with each human correction.

AI-based classification provides a single, consistent interpretive layer across all document types. The business implications include:

Dimension	Without AI	With AI Document Intelligence
Speed	Manual routing, limited throughput	Real-time classification at enterprise scale
Accuracy	Dependent on human diligence	Model-driven, verifiable precision above 98 %
Auditability	Scattered logs, inconsistent evidence	Unified metadata trail: model version, timestamp, reviewer
Compliance	Manual checks for OVDs or AML docs	Automated mapping to regulatory frameworks
Scalability	Cost rises with headcount	Linear scale without proportional cost increase

AuthBridge’s State-of-the-art AI-Based Document Classification Suite

Trust begins with understanding, and AuthBridge has built its verification ecosystem around that very principle.
Across its portfolio of solutions, from digital KYC to field verification, AuthBridge leverages AI-based document classification to convert unstructured documents into verified, actionable intelligence.
This technology doesn’t simply automate document handling; it transforms every uploaded file into a digital proof of trust.

TruthScreen

TruthScreen, AuthBridge’s flagship AI verification platform, showcases how classification drives smarter compliance.
When a user uploads an ID (Aadhaar, PAN, driving licence, or voter card), the system doesn’t just extract text. It first identifies what type of document it is, and then applies the relevant verification protocol using OCR, facial recognition, and liveness detection.

This ability to classify before verifying enables multiple ID formats to be processed within one streamlined journey. The inclusion of deepfake and image forgery detection further ensures that only authentic, high-integrity documents pass through.
For enterprises, this means faster KYC approvals, reduced manual dependency, and greater compliance confidence — where every classified document becomes a verified identity.

Digital KYC

AuthBridge’s Digital KYC solution takes the intelligence behind TruthScreen and extends it to enterprises that need instant, paperless onboarding.
Here, the document classification system is detecting whether the uploaded document is an identity or address proof, parsing fields accordingly, and connecting instantly with authoritative data sources like DigiLocker or government databases.

The process, classify, extract and verify, forms the foundation of AI-based document processing. It minimises manual effort, reduces verification errors, and delivers near-instant onboarding, helping fintechs, insurers, and NBFCs move customers from registration to activation in record time.
The result: higher completion rates and a stronger balance between user experience and regulatory accuracy.

iBRIDGE and AI-BGV

For enterprise-scale employee verification, AuthBridge’s iBRIDGE and AI-BGV platforms bring order to the document-heavy world of background checks.
These systems handle vast volumes of ID proofs, payslips, experience letters, and degree certificates — each automatically classified by AI models to determine the correct verification track.

A payslip routes to employment validation; a degree certificate triggers education verification; an address proof goes to residence verification.
This intelligent sorting removes human bottlenecks and ensures that verification remains consistent, traceable, and efficient across thousands of employees or gig workers.
Through document classification, AuthBridge transforms background verification from a reactive process into a proactive compliance mechanism — reducing turnaround times by more than half while improving accuracy.

GroundCheck.ai

In field verification, GroundCheck.ai extends AuthBridge’s classification capabilities beyond the desktop.
When field agents capture photographs or supporting documents, the system automatically identifies the content, distinguishing between a storefront, a business licence, or an identity proof, and decides the next step.

Its Agentic AI layer interprets visual inputs to guide whether the verification can be digitally confirmed or requires manual escalation.
This adaptive intelligence allows GroundCheck.ai to handle verifications across 20,000+ PIN codes in India with consistency and precision.
By integrating classification into physical operations, AuthBridge has transformed field verification from a manual audit process into an AI-orchestrated decisioning system.

AuthBridge AI

Powering all of these solutions is the AuthBridge AI Platform, launched in 2025 and trained on over 1.5 billion proprietary records.
This platform unifies the company’s document intelligence across identity, employment, and business verification products, applying machine learning, OCR, and natural language models to automatically recognise, extract, and validate information from multiple document types.

Delivering up to 95% verification accuracy and an 82% reduction in turnaround time, it’s a scalable infrastructure that converts document classification into business velocity.
For clients, this means measurable ROI: faster verification cycles, enhanced fraud control, and transparent audit trails, powered by intelligent automation.

Conclusion

Document classification is all about enabling AI to reason. The coming phase of document AI will move beyond extraction and accuracy metrics to systems that understand context, infer intent, and validate authenticity autonomously. This evolution will redefine how organisations view trust: not as a one-time outcome, but as a continuous, intelligent process embedded in every interaction. As AI matures, the goal isn’t faster verification alone, but it’s smarter understanding, where every document becomes a reliable source of truth.

By Abhinandan, 2 weeksOctober 15, 2025 ago

RBI’s-New-Authentication-Mandate-blog-image

BFSI

RBI’s New Authentication Mandate: Strengthening Digital Payments

Introduction

The Reserve Bank of India’s (RBI) latest directive on digital payment authentication marks a pivotal moment in the evolution of India’s financial ecosystem. Beginning 1 April 2026, all digital transactions will require two factors of authentication, with at least one dynamic and unique factor per transaction. This is not just a compliance update — it’s a fundamental redesign of how digital trust is built in India’s payment landscape.

Moving Beyond OTPs: A Shift Towards Adaptive Authentication

For over a decade, SMS-based OTPs have served as the primary mode of authentication for digital transactions in India. While familiar, they’ve also become increasingly vulnerable — from SIM swaps and phishing attacks to malware interception. RBI’s new directions move the ecosystem decisively toward adaptive, context-aware authentication, using technologies such as biometric verification, device binding, behavioural analytics, and tokenisation.

This paradigm shift ensures that authentication is no longer static or uniform. Instead, it adapts dynamically to the user, device, and transaction risk — a critical upgrade for a country where digital transaction volumes continue to grow exponentially.

Complementing the Broader UPI Ecosystem Evolution

The announcement builds on the momentum from the Global Fintech Fest (GFF) 2025, where several new UPI features were unveiled to enhance payment security and inclusion. From on-device biometric authentication to Aadhaar-based facial verification for setting or resetting UPI PINs, and multi-signatory approvals for joint accounts, these developments share a common vision: to make authentication more secure, frictionless, and inclusive.

Together, these measures position India’s digital payment ecosystem among the most sophisticated in the world, combining regulatory oversight with technological innovation to protect users and institutions alike.

Implications for Banks, Fintechs, and Consumers

The new authentication framework carries distinct implications for every stakeholder in the financial ecosystem:

Banks and Payment Providers: Must re-engineer customer journeys, integrating multi-factor authentication methods like biometric, behavioural, or device-based checks without compromising experience.
Fintechs: Will need to build adaptive authentication engines that balance compliance and usability — creating opportunities for innovation in secure, seamless user experiences.
Consumers: Stand to benefit from stronger protection against fraud and identity theft, alongside smoother onboarding and verification experiences.

By enforcing transaction-specific authentication, RBI is not only mitigating risk but also aligning India’s security practices with global standards — from PSD2 in Europe to NIST frameworks in the United States.

AuthBridge’s Role in Enabling Secure, Verified Payments

As authentication becomes more distributed — spanning devices, biometrics, and dynamic credentials — pre-verification and continuous trust become indispensable. AuthBridge’s identity intelligence and verification APIs are uniquely positioned to complement this new security architecture.

Pre-Transaction Validation: AuthBridge’s UPI ID Verification API confirms that the payment destination belongs to the intended entity before any authentication trigger, reducing failed transactions and misdirected payments.
Device and Channel Consistency: Whether payments originate via mobile, wearable, or Aadhaar-based verification, AuthBridge ensures that identity signals remain consistent and traceable across channels.
Compliance and Audit-Ready Trails: Each verification event can be logged and mapped to payment authorisation flows, creating cleaner audit trails and supporting RBI-mandated reporting standards.

These capabilities enable banks, fintechs, and enterprises to integrate compliance and risk mitigation directly into their payment architecture — ensuring that every digital interaction is secure by design.

Looking Ahead: Building a Trust-First Digital Economy

Digital payments have become the backbone of India’s economy — driving inclusion, accessibility, and innovation. The RBI’s new authentication mandate signals a clear intent: security and scale must advance together.

By combining multi-factor, real-time authentication with verified digital identities, India is setting a global precedent for how a nation of over a billion people can transact securely at scale.

As the ecosystem prepares for this transition, AuthBridge remains a trusted partner, helping organisations operationalise this new layer of digital trust through verified identities, frictionless integrations, and continuous compliance.

By Siddharth, 3 weeksOctober 9, 2025 ago

BFSI

What Is The GST Appellate Tribunal? Read All Key Details Here

What Is The GST Appellate Tribunal?

Since its introduction, GST has been the pillar of India’s economic reforms under the idea of “One Nation, One Tax, One Market.” While it has expanded the tax base, encouraged formalisation, and strengthened revenues, the absence of a dedicated appellate tribunal often meant disputes reached the High Courts directly, creating delays and inconsistencies across states. The launch of GSTAT aims to close this gap.

On 24 September 2025, the Union Finance and Corporate Affairs Minister, Smt. Nirmala Sitharaman formally launched the Goods and Services Tax Appellate Tribunal (GSTAT) in New Delhi. The Tribunal is a statutory appellate body created under the GST laws (Section 109 of the Central Goods and Services Tax Act, 2017) to hear appeals against orders passed by the GST Appellate Authorities under Sections 107 and 108.

Where And How Will The GSTAT Operate

Principal And State Benches

The GST Appellate Tribunal will function through a Principal Bench in New Delhi and 31 State Benches spread across 45 locations in India. This network has been deliberately designed to ensure that every taxpayer, be it an MSME in a small town or a large corporate operating across multiple states, can access the Tribunal without being bothered by distance or administrative hurdles.

Bench Composition

Each Bench of GSTAT will include:

Two Judicial Members
One Technical Member (Centre)
One Technical Member (State)

In addition, the framework provides for single-member benches for simpler matters, a measure that enhances flexibility and allows speedier disposal of straightforward cases.

Structure, Scale, And Synergy

Revenue Secretary Shri Arvind Shrivastava described GSTAT’s design around the three pillars of Structure, Scale, and Synergy.

Structure brings together judicial and technical perspectives.
Scale ensures reach, with multiple benches operating across the country.
Synergy lies in combining human expertise with technology and streamlined processes to deliver justice efficiently.

The GSTAT e-Courts Portal

An important highlight from the GSTAT’s launch was the unveiling of the GSTAT e-Courts Portal, built by the Goods and Services Tax Network (GSTN) in collaboration with the National Informatics Centre (NIC). The platform is designed to anchor the Tribunal in a digital-by-default framework from the very start.

Key Features Of The e-Courts Portal

e-Filing of Appeals: Taxpayers and practitioners can file cases online without needing to visit offices physically.
Case Tracking: Parties can monitor the progress of their appeals transparently and in real time.
Virtual Hearings: The system allows participation in hearings digitally, reducing cost and time, and increasing accessibility.

To ensure a smooth transition, the Tribunal has allowed staggered filing of appeals until 30 June 2026. In addition, the portal includes comprehensive support material such as FAQs, explanatory notes, and instructional videos. These resources aim to simplify the process, even for smaller businesses and individual taxpayers who may not be familiar with formal legal procedures.

Finance Minister Nirmala Sitharaman outlined her expectations clearly by suggesting jargon-free decisions in plain language; simplified formats and checklists; digital-by-default filings and virtual hearings; and time standards for listing, hearing and pronouncement.

Impact Of GSTAT On Businesses And Compliance

From a practitioner’s perspective, the launch of GSTAT finally addresses a weakness in the GST framework: the absence of a uniform, specialised appellate body. Taxpayers across sectors have struggled with appeals moving directly from the Appellate Authority to the High Courts. This not only created heavy dependency but also led to variations in how similar matters were interpreted across states.

With a Principal Bench in Delhi and State Benches across 45 locations, GSTAT offers reach and consistency. For MSMEs, this means disputes over refunds or input tax credit can now be handled within a structured timeframe, preventing working capital from being locked away for months or years. For exporters, faster resolution of refund disputes can directly impact competitiveness, since delayed refunds have long been a pain point.

Larger corporations, especially those with operations in multiple states, stand to benefit from uniformity of interpretation. One of the challenges under GST has been the lack of predictability — identical issues being treated differently across jurisdictions. GSTAT is expected to bring alignment, supported by judicial and technical members sitting together. Justice Sanjaya Kumar Mishra, President of GSTAT, pointed out that the Tribunal will also play a role in reducing the existing backlog of appeals.

From Dispute Resolution To Dispute Prevention

The launch of GSTAT provides a long-awaited mechanism for appeals under GST. With its network of benches, digital filing system, and commitment to timely hearings, the Tribunal is expected to reduce pendency and bring uniformity to rulings. For taxpayers, that means disputes will now move through a clearer and more predictable channel.

But an appellate forum, however efficient, is still the last stop in the chain. For businesses, the real efficiency gain lies in preventing issues from reaching that stage at all. Many GST disputes originate from routine oversights such as invalid GSTINs, registrations that do not match PAN details, or entities that fail to file returns regularly. These problems can amplify into contested demands or refund delays if left unchecked.

By validating GSTINs, confirming their linkage with PAN, and monitoring filing behaviour at the point of onboarding and during periodic reviews, companies can cut down the chances of avoidable conflicts and also create a ready-made audit trail.

Solutions like those provided by AuthBridge help businesses put this verification discipline into practice. There are two significant outcomes from this:

Fewer disputes escalate to the Tribunal,
When they do, organisations are better prepared with consistent, verifiable records.

GSTAT now provides the structure for fair and independent adjudication. Complementing it with strong verification processes ensures businesses engage with the GST framework not just reactively, but proactively, reducing friction, protecting cash flows, and operating with higher confidence.

By Abhinandan, 1 monthSeptember 29, 2025 ago

BFSI

The 7 Best RegTech Platforms In India

Introduction

Regulatory compliance has now become a boardroom priority, from being a back-office necessity. In India, this transition is a lot more prominent: financial regulators such as the RBI and SEBI have introduced strict frameworks around customer due diligence, data protection, anti-money laundering, and fraud prevention. At the same time, the sheer scale of digital adoption — over 1.2 billion Aadhaar enrolments and UPI processing more than 14 billion transactions a month in 2025 — has created compliance challenges that manual systems can no longer manage.

This confluence of regulatory pressure and digital scale has given rise to Regulatory Technology (RegTech) as a distinct sector in India. RegTech firms have now become key entities, helping banks, NBFCs, fintechs, insurers, and even e-commerce platforms maintain the trust of the various stakeholders while scaling fast.

What Is RegTech?

RegTech, short for Regulatory Technology, refers to the use of technology to simplify, standardise, and automate regulatory compliance. While definitions often reduce it to KYC or AML solutions, in reality, RegTech has a wide scope, ranging from transaction monitoring and fraud analytics to e-signatures, digital identity, and regulatory reporting.

The value proposition of RegTech is threefold:

Operational efficiency: replacing manual compliance checks with automated, API-driven workflows that can process millions of cases in real time.
Regulatory accuracy: ensuring businesses interpret and implement complex rules consistently, reducing exposure to fines and reputational damage.
Scalability: allowing organisations to keep pace with growth without compliance becoming a bottleneck.

Common RegTech Services

RegTech Service providers have specialised across several compliance-critical domains, driven by regulatory frameworks and digital infrastructure. The most common service categories include:

Digital KYC And Video KYC

Video-based customer identification (Video-KYC), Aadhaar-based KYC, and eKYC via DigiLocker or CKYC repositories form the base of compliance in financial services.

Anti-Money Laundering (AML) And Sanctions Screening

Transaction monitoring, watchlist screening, and adverse media checks are essential to comply with FATF and domestic AML obligations.

Fraud Detection And Risk Management

Not just regulatory compliance, but RegTech platforms play a crucial role by preventing identity theft, document forgery, and synthetic fraud.

Digital Document Execution

The shift to paperless operations has created demand for Aadhaar eSign, digital stamping, and eMandates.

Corporate And Workforce Compliance

Large enterprises increasingly need tools to verify not just customers, but also employees, vendors, and suppliers.

How To Choose The Best RegTech Platform?

Selecting a RegTech platform requires balancing regulatory obligations with business strategy. Here is a list of a few factors that you can keep in mind when selecting a RegTech service provider for your business needs:

Specialisation In Relevant Compliance Areas

Evaluate whether the provider covers your regulatory needs — be it AML and financial crime detection, digital KYC and onboarding, or digital contracting.

Proven Scale And Reliability

Check for operational benchmarks such as turnaround times (TAT), uptime, and throughput. AuthBridge, for instance, processes 15M+ verifications per month for more than 3,000 clients, showcasing enterprise-grade reliability.

Seamless Integration

Look for API-first architecture and pre-built connectors. AuthBridge explicitly positions itself as integration-friendly, enabling plug-and-play with banking cores, HR systems, or onboarding platforms.

Regulatory Alignment And Certifications

Prioritise providers with proven track records in working with large BFSI clients and compliance with standards such as ISO 27001 or data protection readiness under India’s DPDP Act.

Responsiveness To Regulatory Change

Agile providers update their platforms and services swiftly to keep clients compliant with the fast-changing regulations and directives without disruptions.

Long-Term Value

Price per verification is only one metric. Consider the total cost of ownership, factoring in integration success, downtime risk, and regulatory penalties avoided. A strong RegTech partner delivers both compliance assurance and measurable business ROI.

List Of The Top 7 RegTech Platforms In India

1. AuthBridge

Founded in 2005 and headquartered in Gurugram, AuthBridge is India’s largest and most diversified RegTech service provider. With over 3,000 enterprise clients and 15 million+ verifications processed every month, AuthBridge has become synonymous with compliance at scale.

Core Offerings

Digital KYC & Video-KYC – RBI-compliant onboarding, powered by AI, OCR, and computer vision.
AML & Sanctions Screening – Watchlist screening, adverse media checks, and transaction risk filters.
Workforce & Gig Verification – Identity, employment, education, address, and criminal record verification for employees and delivery agents.
Corporate & Third-Party Due Diligence – MCA checks, GST verification, litigation records, and financial risk profiling.
Digital Address Verification (DAV) – AI-driven geolocation tools replacing field-intensive checks.
E-sign & Digital Contracting – Platforms such as SignDrive are integrated with Aadhaar and DSC.
Contact Point Verification (CPV) – GroundCheck.ai blends field and digital verification for fast turnaround.

AuthBridge’s strength lies in combining two decades of domain expertise with AI-first platforms. Its solutions are API-first, enabling seamless integration into banking systems, HR workflows, and enterprise onboarding portals.

2. IDfy

Founded in 2011 and headquartered in Mumbai, IDfy specialises in digital identity verification and fraud detection. Its platform covers eKYC, Video-KYC, background checks, and fraud analytics, serving banks, fintechs, insurers, and internet platforms. IDfy also offers Privy, a DPDP-compliant privacy and consent management layer.

3. HyperVerge

Established in 2014, with offices in Bengaluru and Palo Alto, HyperVerge is an AI-driven verification provider. Its offerings include Video-KYC, face authentication, KYB, and AML screening, leveraging proprietary computer vision technology. HyperVerge claims to have processed over 1 billion identity checks globally, making it one of the most widely adopted Indian-born RegTech players.

4. Digio

Founded in 2016 in Bengaluru, Digio focuses on digital documentation and consent-driven compliance. Its services include Aadhaar eSign, eStamp, eMandates (eNACH), CKYC integrations, Video-KYC, and AML screening. Digio’s platforms are heavily used by banks, NBFCs, and fintechs to digitise paperwork while staying compliant with IT Act and RBI rules.

5. Signzy

Founded in 2015 and headquartered in Bengaluru, Signzy is a global digital onboarding and compliance automation platform. It offers KYC, KYB, AML checks, transaction monitoring, and digital contracting via its no-code platform. Signzy has partnered with major banks and regulators, serving 500+ clients worldwide, and is recognised for its ability to adapt swiftly to regulatory change.

6. Jocata

Founded in 2010 and based in Hyderabad, Jocata is known for its flagship platform GRID, which integrates AML, KYC remediation, fraud detection, and onboarding into a unified case management system. Jocata serves leading Indian banks and NBFCs, helping them comply with AML/CFT frameworks while reducing operational risk.

7. Leegality

Founded in 2016 and headquartered in Gurugram, Leegality is a specialist in digital documentation and execution workflows. Its products include Aadhaar eSign, BharatStamp (digital eStamping), and document workflow automation, enabling legally valid, paperless compliance. Leegality has gained traction among BFSI, insurance, and enterprise clients, modernising their contracting processes.

Conclusion

As regulation tightens and digital adoption accelerates, RegTech has become the silent infrastructure of trust in India’s financial and corporate sectors. The seven providers outlined here demonstrate the breadth of innovation driving this shift, but AuthBridge’s scale, breadth of services, and proven track record set it apart as the partner of choice for enterprises where compliance and growth must go hand in hand.

By Abhinandan, 1 monthSeptember 26, 2025 ago

Background Checks

RBI’s Directive On Unclaimed Deposits 2025 & The Role Of Digital Address Verification

Introduction

In September 2025, the Reserve Bank of India (RBI) issued a clear and time-bound directive to scheduled commercial banks across the country: return over ₹67,000 crore in unclaimed deposits within three months. These funds, which have been lying dormant in banks for over a decade, reflect savings and investments that depositors or their heirs have not claimed.

According to official data presented in Parliament, ₹67,270 crore in unclaimed deposits had accumulated by June 2025, with nearly 87 per cent of these funds held by public sector banks. The State Bank of India alone accounts for close to ₹19,330 crore, followed by Punjab National Bank and Canara Bank, each with over ₹6,000 crore. Among private banks, ICICI Bank leads with over ₹2,000 crore in unclaimed deposits.

The central bank has set a strict three-month window—from October to December 2025—for institutions to intensify their efforts to trace account holders or their heirs.

What Are Unclaimed Deposits?

Unclaimed deposits are amounts parked in bank accounts or term deposits that remain untouched for ten years or more. If there are no customer-initiated transactions, such as withdrawals, deposits, or instructions, over this period, the account is treated as inoperative.

By regulation, once these deposits cross the dormancy threshold, they are transferred by banks to the Depositor Education and Awareness (DEA) Fund maintained by the RBI. The intent behind this framework is to protect idle money from misuse and to ensure that rightful owners or their heirs can claim it at any point through a structured process.

Despite these measures, the scale of the problem is enormous. The funds in question represent both financial assets forgotten by individuals and systemic gaps in outreach. Many heirs are unaware of accounts held by deceased relatives, and in other cases, documentation gaps make it difficult for claimants to establish ownership.

The Scale Of Unclaimed Deposits

The RBI’s disclosure puts the size of unclaimed deposits at ₹67,270 crore as of June 2025. Public sector banks dominate this pool, reflecting their large customer base and legacy operations. Here are a few of the banks with their unclaimed deposits:

Bank	Unclaimed Deposits (₹ crore)
State Bank of India (SBI)	19,329.29
Punjab National Bank (PNB)	6,910.67
Canara Bank	6,278.14
Bank of Baroda	5,277.36
Union Bank of India	5,104.50
ICICI Bank	2,063.45
Other Private Banks (combined)	8,673.72
Total (All Banks)	67,270

RBI’s Instructions To Banks

The Reserve Bank of India has issued time-bound instructions to banks, directing them to intensify efforts between October and December 2025 to return unclaimed deposits.

Key Directives From The RBI

Special Outreach Drive (Oct–Dec 2025):
Banks have been asked to run a targeted campaign over three months to trace account holders or their heirs. The focus will be on proactive engagement rather than passive compliance.
Role Of State Level Bank Committees (SLBCs):
SLBCs are required to review progress at a granular level, breaking down data by region and age of deposit, and ensuring that lagging banks step up their efforts.
Public Awareness Measures:
Banks must reach out to customers through various media, including print, electronic, and digital channels, with a special focus on rural and semi-urban areas where awareness levels are often lower.
Grievance Redressal:
Institutions must strengthen grievance redressal mechanisms to ensure that claimants face fewer procedural hurdles when retrieving funds.
UDGAM Portal:
A central plank of this drive is the UDGAM (Unclaimed Deposits – Gateway to Access Information) portal maintained by the RBI. This digital platform allows individuals to search for unclaimed deposits across multiple banks using simple identifiers such as their name, PAN, or address.

As of July 2025, nearly 8.6 lakh users had registered, and the portal now covers banks that account for around 90% of unclaimed deposit value.

Challenges In Returning Dormant Deposits

While the RBI’s directive is clear and time-bound, executing it on the ground poses significant challenges. The sheer magnitude of ₹67,270 crore in dormant funds means banks must overcome structural, operational, and human barriers to reunite depositors with their money.

Tracing The Rightful Owners

One of the greatest hurdles lies in locating the original depositors or their heirs. Over time, customers may have moved houses, migrated abroad, or passed away, leaving no clear trail for banks to follow. Inheritance complexities add another layer of difficulty, especially in the absence of updated nominee information.

Documentation And Proof

Even when claimants are identified, retrieving deposits often hinges on producing valid documents such as identity proofs, succession certificates, or death certificates of deceased account holders. In many cases, these documents are either missing or difficult to obtain, delaying the process.

Awareness And Financial Literacy Gaps

A large proportion of dormant deposits belong to individuals in rural and semi-urban regions. Limited awareness of banking rules, lack of digital access, and low financial literacy mean that many potential claimants are unaware of their rights or the steps required to reclaim funds.

Operational Inefficiencies

Banks themselves face operational bottlenecks. Branch-level staff may not always have updated contact information, and in some cases, the processes for claim settlement remain manual, cumbersome, and time-consuming.

Risk Of Fraudulent Claims

Efforts to return unclaimed deposits must also be safeguarded against fraudulent attempts, where impostors may try to exploit gaps in verification mechanisms. This necessitates robust verification tools that can balance customer convenience with security.

The Scale of the Challenge

As per RBI’s directive, banks must return ₹67,000 crore lying in dormant accounts within 3 month

These deposits, untouched for over a decade, often belong to individuals who:

Have changed residences or migrated abroad.
Passed away without clear nominee details.
Remain unaware of their dormant accounts, especially in rural or semi-urban areas.

Traditional outreach methods — phone calls or emails — often fail. Contact numbers are outdated, email addresses bounce, and in many cases, families are unaware of the accounts at all. Simply shutting the account isn’t enough; banks must first trace and credit the rightful customer or heir.

AuthBridge’s Role: From Tracing To Compliance

When banks are pressed to act fast and at scale, mere promises don’t suffice. What matters is whether a solution can deliver across jurisdictions, risk tiers, connectivity constraints, and fraud vectors. AuthBridge’s address and contact point verification stack is built to meet exactly those demands. Below is a close look at the services.

At AuthBridge, we specialise in bridging the gap between compliance requirements and customer realities:

1. Skip Tracing For Account Closure
We leverage alternate data sources — credit bureau, utility, and telecom records — to trace rightful owners or heirs when contact details are missing.

2. Mobile-To-Address API (Powered by Shiprocket)
Our mobile-to-address API helps confirm and enrich contact data, scoring addresses against 12–13 trusted sources including national ID repositories. This accelerates discovery when customers cannot be reached directly.

3. Address Augmentation & Verification
Using mobile numbers, we link multiple data points to verify and augment addresses, reducing false positives and ensuring accurate outreach.

4. Re-KYC & Claimant Verification
Through video KYC, name screening, and account verification, we help banks securely re-onboard dormant customers or verify claimants before settlement.

5. Hybrid Approach: Digital + On-Ground
Where needed, our field verification teams complement digital workflows, ensuring even rural or hard-to-reach customers are traced effectivel

Conclusion

The RBI’s call to return ₹67,270 crore in unclaimed deposits within three months is both a challenge and an opportunity for banks. Success will depend on how effectively institutions can trace rightful claimants while safeguarding against fraud and delay. Digital tools such as AuthBridge’s Digital Address Verification (DAV) and GroundCheck.ai provide a practical answer—enabling banks to verify addresses in minutes, escalate seamlessly to on-ground checks when required, and build a transparent audit trail at every step. By adopting these solutions, banks not only stand to meet the RBI’s directive on time but also send a clear message of trust, accountability, and customer commitment.

By Abhinandan, 1 monthSeptember 25, 2025 ago

BFSI

RBI’s Updated Guidelines For Payment Aggregators 2025: Key Details

Introduction

On 15 September 2025, the Reserve Bank of India (RBI) issued the Master Direction on Regulation of Payment Aggregators (PAs). This consolidated framework supersedes earlier circulars — the 2020 and 2021 guidelines on Payment Aggregators and Gateways, and the 2023 directions on Cross-Border Payment Aggregators.

The new Direction has been issued under the powers conferred by Section 18, read with Section 10(2) of the Payment and Settlement Systems Act, 2007, together with Section 10(4) and Section 11(1) of the Foreign Exchange Management Act, 1999. It harmonises regulations for online, physical and cross-border aggregation of payments, introducing a common compliance regime for banks, non-banks, authorised dealer (AD) banks and scheduled commercial banks.

Key Definitions Under The RBI’s New Payment Aggregator Guidelines 2025

To understand the scope of the 2025 Master Direction, it is essential to first look at the definitions provided by the Reserve Bank of India. These definitions set the base for regulating Payment Aggregators (PAs) and Payment Gateways (PGs).

A cash-on-delivery transaction is a merchant transaction in which banknotes or currency notes, being legal tender in India, are offered or tendered at the time of delivery of goods and services.
Contact Point Verification (CPV) refers to the physical verification of the merchant’s address or place of business.
E-commerce refers to the buying and selling of goods and services, including digital products, conducted over digital and electronic networks. For this definition, the term ‘digital and electronic network’ includes networks of computers, television channels, and other internet applications used in an automated manner, such as web pages, extranets and mobile platforms.
An inward transaction refers to any transaction involving the inflow of foreign exchange, while an Outward transaction consists of the outflow of foreign exchange.
A Marketplace is an e-commerce entity that provides an information technology platform on a digital or electronic network to facilitate transactions between buyers and sellers.
A Merchant means an entity or marketplace that sells goods, provides services, or offers investment products. This also includes exporters and overseas sellers.
Payment channel refers to the method or manner through which a payment instruction is initiated and processed in a payment system.
A Payment Aggregator (PA) is an entity that facilitates the aggregation of payments made by customers to merchants through one or more payment channels, using the merchant’s interface (physical or virtual), to purchase goods, services, or investment instruments. Subsequently, it settles the collected funds to the merchant. The Directions categorise PAs into three types:

PA–Physical (PA–P): Facilitates transactions where the acceptance device and payment instrument are physically present in proximity.
PA–Cross Border (PA–CB): Facilitates aggregation of cross-border payments for current account transactions permissible under FEMA, through the e-commerce route. Two sub-categories exist under PA–CB: inward transactions and outward transactions.
- It is clarified that non-bank entities authorised as AD Category-II, and facilitating current account transactions not prohibited under FEMA (other than purchase or sale of goods or services), do not fall within the purview of PA–CB business.
- Similarly, a card transaction where the foreign exchange settlement is facilitated by a card network and the aggregator receives payment in local currency is not treated as PA–CB activity.
PA–Online (PA–O): Facilitates transactions where the acceptance device and payment instrument are not present in proximity at the time of payment.

A Payment Gateway (PG) is defined as an entity that provides the technology infrastructure to route and facilitate the payment transaction processing without handling funds.

Finally, terms such as Central KYC Records Registry (CKYCR), Officially Valid Document (OVD), equivalent e-document, digital KYC, and Video-based Customer Identification Procedure (V-CIP) carry the same meanings as set out in the RBI’s Master Direction on Know Your Customer (2016), as amended from time to time.

Authorisation For Payment Aggregator Business

The Master Direction distinguishes between banks and non-bank entities operating as a Payment Aggregator. Here are the differences between banks and non-banks operating as PAs:

Banks do not require a separate authorisation from the RBI to provide PA services. Their existing powers and supervisory framework govern their activities.
Non-bank entities, however, must seek explicit authorisation from the RBI under the Payment and Settlement Systems Act, 2007. Only companies incorporated under the Companies Act, 2013, are eligible to apply.

To operationalise this requirement, the RBI has mandated that all non-bank Payment Aggregators submit their applications through the designated portal. Those who fail to apply by 31 December 2025 must wind down their PA business operations by 28 February 2026.

Capital Requirements For Payment Aggregators

To ensure that only entities with sufficient monetary capacity operate as PAs, the RBI has imposed a phased capital requirement:

At the time of application, a non-bank Payment Aggregator must demonstrate a minimum net worth of ₹15 crore.
By the end of the third financial year from the date of authorisation, this net worth must rise to ₹25 crore.

For this purpose, net worth is calculated in line with the Companies Act and relevant accounting standards. Compulsorily convertible preference shares may be included, but deferred tax assets are specifically excluded.

Governance And Management

The RBI has raised governance standards for Payment Aggregators in line with their growing role in handling public funds. Every PA is expected to be professionally managed, with its promoters and directors meeting the central bank’s fit and proper criteria. This entails solid financial integrity, a reputation for honesty, and freedom from disqualifications such as insolvency or conviction.

RBI has also closed the door on ownership changes slipping through unnoticed. Any takeover or acquisition of control, whether direct or indirect, requires prior approval from the RBI. This ensures that entities entrusted with merchant and customer funds remain under the regulator’s watch even when corporate structures shift.

To embed accountability, Boards of Payment Aggregators must frame policies on risk management, information security, and customer protection. These policies must not be a one-time exercise but must be subject to periodic review.

Dispute Resolution Framework

The RBI has mandated a time-bound framework for dispute resolution and refunds, aligned with its earlier Turn Around Time (TAT) prescriptions for failed transactions.

Payment Aggregators must enter into legally enforceable agreements with merchants and acquiring banks. These contracts must clearly allocate responsibility for settlement, refunds, and handling of disputes, reducing ambiguity in the payments chain.

Equally important is transparency for customers. Refund policies must be disclosed upfront, so payers know how their funds will be handled in the event of a reversal. Each PA must also appoint a grievance redressal officer and provide an escalation matrix to track and resolve complaints efficiently.

Security, Fraud Prevention And Risk Management

Every Payment Aggregator must implement a comprehensive risk management framework, including fraud prevention, suspicious activity monitoring, and controls safeguarding customer information.

Compliance with internationally recognised standards is compulsory. Aggregators must adhere to Payment Card Industry – Data Security Standards (PCI-DSS) and Payment Application – Data Security Standards (PA-DSS) where relevant.

To verify adherence, Payment Aggregators must undergo an annual audit by a CERT-In empanelled auditor. This ensures independent validation of cybersecurity and system integrity. In addition, the Directions mandate compliance with RBI’s Cyber Resilience and Digital Payment Security Directions, 2024.

Data handling is another area where obligations are explicit. All payment system data must be stored in India, per the RBI’s 2018 data localisation circular.

General Directions For Payment Aggregators

RBI has laid down a series of general directions that shape day-to-day business conduct for Payment Aggregators:

Contractual exclusivity: Aggregators may only facilitate payments for merchants with valid contracts. This ensures accountability and prevents misuse of aggregator platforms for unauthorised transactions.
Marketplace restriction: PAs are prohibited from running their own marketplaces. This prevents conflicts of interest between operating as a payments intermediary and competing as a merchant platform.
Merchant Discount Rate (MDR): PAs must comply fully with RBI’s prescriptions on MDR. Importantly, they are required to ensure that charges are transparently disclosed to merchants.
Refund rules: Refunds must, by default, be processed back to the original payment method. The only exception is when the customer opts for an alternative account under the same ownership.
Authentication norms: Using ATM PINs as an authentication factor is explicitly disallowed for card-not-present transactions.

Special Directions For Cross-Border Payment Aggregators

Entities facilitating payments for imports or exports via the e-commerce route must comply with additional safeguards to prevent misuse of outward remittances and to ensure alignment with FEMA.

Key provisions include:

Segregation of funds: Aggregators must maintain separate accounts for inward and outward flows. Inward and outward remittances cannot be commingled.
Transaction limits: Outward transactions are capped at ₹25 lakh per transaction. This ceiling prevents the misuse of aggregator channels for large-scale capital transfers.
Banking arrangements: Only Authorised Dealer (AD) Category-I–banks can be used to maintain collection accounts for inward (InCA) and outward (OCA) flows. This ensures settlement happens only through banks with full foreign exchange authorisation.
Settlement currency: Non-INR settlement is permitted only in cases where the merchant is an Indian exporter directly onboarded by the aggregator. For other cases, settlement must be in Indian Rupees.
Regulatory reporting: Cross-border PAs must provide sufficient data to their AD banks for reporting into RBI’s Export Data Processing and Monitoring System (EDPMS) and Import Data Processing and Monitoring System (IDPMS).

KYC And Due Diligence

Merchant onboarding lies at the heart of the Directions. RBI has imposed obligations that are closely aligned with its broader KYC Master Directions:

Complete due diligence: Aggregators must conduct comprehensive Customer Due Diligence (CDD) of all merchants, using officially valid documents, PAN, and other identifiers.
Simplified process for small merchants: A streamlined onboarding process may be applied when a merchant’s annual domestic turnover does not exceed ₹40 lakh, or where export turnover does not exceed ₹5 lakh. This involves verifying PAN, conducting Contact Point Verification (CPV), and collecting an officially valid document (OVD).
Background Verification and categorisation: Aggregators must validate the background of merchants, classify them under appropriate Merchant Category Codes (MCCs), and ensure that their names are accurately reflected in customer-facing transactions.
Monitoring: Onboarding is not a one-time exercise. PAs are responsible for continuous monitoring of merchants, including watchlist screening, tracking changes in legal status, and observing for adverse media.
Registration with FIU-IND: Non-bank aggregators must register with the Financial Intelligence Unit – India (FIU-IND) and adhere to reporting standards under the Prevention of Money Laundering Act.
Legacy merchants: All existing merchants must comply with these requirements by 31 December 2025. Merchants not verified by then must be re-onboarded from 1 January 2026.

Escrow Accounts And Settlement Requirements

The Directions mandate that all non-bank Payment Aggregators maintain merchant funds in escrow accounts with Scheduled Commercial Banks. For cross-border activity, separate accounts are required: an Inward Collection Account (InCA) for receipts from overseas customers and an Outward Collection Account (OCA) for payments made by Indian customers to overseas merchants. Funds relating to inward and outward transactions must be kept segregated.

Settlement Framework

Existing non-bank PAs must migrate to the escrow arrangement within two months of receiving RBI authorisation.
Credits and debits to the escrow account are restricted to transactions permitted explicitly under the Directions, ensuring that merchant funds are not diverted for unrelated purposes.
Interest may be earned only on the core portion of the escrow balance, calculated as the average of the lowest daily balances in each fortnight over the preceding 26 fortnights. This provision allows recognition of a stable minimum balance without enabling misuse of settlement float.
Following separate arrangements, escrow accounts must not be used for cash-on-delivery (COD) transactions.

Certification And Reporting

Quarterly: Payment Aggregators must obtain auditor certification confirming compliance with escrow guidelines.
Annually, the auditor and the escrow bank must certify adherence to RBI requirements.

Compliance And Reporting Obligations

Payment Aggregators are subject to extensive compliance and reporting requirements under the Directions.

Monthly: Aggregators must report transaction statistics to the Reserve Bank, covering volumes and values across different payment channels.
Quarterly: They must obtain an auditor’s certificate confirming compliance with escrow account operations and a certificate from the bank maintaining the escrow account on credits and debits.
Annual: Every aggregator must submit a net worth certificate, an information systems and cyber security audit report, and confirmation of compliance with the governance and operational provisions of the Directions.
Event-based: Any change in promoters, directors, or key managerial personnel must be communicated to the Reserve Bank, supported by a declaration confirming compliance with the fit-and-proper criteria.

How Can AuthBridge Streamline Your Compliance Under RBI’s New Directions?

Meeting RBI’s new master directions requires both robust governance structures and scalable verification infrastructure. AuthBridge’s solutions are aligned to support entities in implementing these requirements:

Merchant Onboarding And KYC/CDD
RBI requires full customer due diligence, including PAN, CKYCR, OVD checks, and Contact Point Verification for merchants. AuthBridge enables this through automated identity verification APIs, digital address verification, and V-CIP for high-risk profiles.
Ongoing Monitoring And Due Diligence
The Directions emphasise continuous monitoring of merchants, including adverse news screening and changes in legal status. AuthBridge provides automated monitoring tools and dynamic risk scoring, allowing compliance teams to act on early warning signals.
Duplicate and Mule Account Detection
With Address Augmentation across 12–13 independent datasets (including NIDs and logistics service providers), AuthBridge helps identify inconsistencies, link identities across data points, and flag suspicious mule and duplicate accounts early.
AML And FIU-IND Reporting
Non-bank aggregators must register with FIU-IND and comply with SAR/STR reporting. AuthBridge offers workflows that automate case detection and reporting, reducing the operational burden on compliance teams.
Skip Tracing for Dormant Accounts
Dormant accounts present severe issues, particularly when registered email or phone contacts are unresponsive. AuthBridge’s Mobile-to-Address API with address scoring enables banks to trace customers through fresh, activity-based address signals, ensuring balances are credited to the rightful owner before closure.
Governance And Fit-And-Proper Checks
RBI mandates promoters and directors to meet fit-and-proper criteria and requires risk management and customer protection policies. AuthBridge supports this with director background checks, conflict-of-interest screening, and governance-focused due diligence services.

By Abhinandan, 1 monthSeptember 22, 2025 ago

Background Checks

New Increased UPI Transaction Limits 2025: Everything You Need To Know

Introduction

The National Payments Corporation of India (NPCI) has recently announced an update to the Unified Payments Interface (UPI) limits, which has a significant impact on how high-value digital payments are processed in India. Effective now, users can make Person-to-Merchant (P2M) transactions of up to ₹5 lakh per transaction, and a maximum of ₹10 lakh in total within 24 hours for specified categories. This update changes how UPI will handle large payments and has been designed to make digital transactions more efficient, secure, and accessible for users across various sectors.

Key Changes To UPI Transaction Limits

1. Per-Transaction Limit for P2M Transactions Increased to ₹5 Lakh

The single transaction limit for Person-to-Merchant (P2M) transactions has now been raised to ₹5 lakh in specified categories. Previously, the limit for such transactions was much lower, but this change enables businesses in specific industries to accept higher-value payments without relying on multiple smaller transactions.

2. Daily Aggregate Limit Raised to ₹10 Lakh in Select Categories

In addition to the raised per-transaction limit, the daily aggregate limit for P2M transactions has been increased to ₹10 lakh within 24 hours for specific categories, including:

Insurance premiums
Capital markets
Travel
Collections
Government e-Marketplace (GeM)

This revision allows users to conduct more extensive daily transactions, supporting businesses that need to process large payments over a day. For instance, in the insurance sector, where large premium payments are common, companies can process these payments in a single day without requiring multiple smaller transactions.

3. P2P Transfer Limit Remains at ₹1 Lakh per Day

Despite the increase in transaction limits for P2M payments, the limit for Person-to-Person (P2P) transfers remains unchanged at ₹1 lakh per day. This helps maintain a clear distinction between personal transfers and commercial transactions, ensuring that high-value commercial transactions are subject to stricter conditions. On the contrary, personal transfers stay within a manageable limit.

4. Investment Payments in Capital Markets and Insurance Increased

For capital market investments and insurance premiums, the per-transaction limit has been raised from ₹2 lakh to ₹5 lakh, with a daily aggregate limit of ₹10 lakh. This will benefit investors, particularly those looking to make significant investments, by offering more room for digital transactions, eliminating the need to break down payments into multiple smaller ones.

5. GeM and Government Transactions Raise Transaction Limits

The Government e-Marketplace (GeM), which facilitates procurement by government departments, now has an increased transaction limit for payments such as tax payments, earnest money deposits, and other government-related transactions. Previously capped at ₹1 lakh, the per-transaction limit has now been increased to ₹5 lakh, simplifying and streamlining government transactions that often involve substantial sums.

6. Credit Card Bill Payments Now Higher

The transaction limit for credit card bill payments has also been raised to ₹5 lakh per transaction, with a daily cap of ₹6 lakh. This change offers more flexibility for consumers who need to make large credit card payments, whether for personal use or business expenses.

Increased UPI Limit Benefits On Businesses And Consumers

A. Impact on Businesses

Increased Flexibility for High-Value Transactions
This update brings significant flexibility for businesses, especially those in the capital markets, insurance, travel, and e-commerce sectors. Businesses can now process higher-value transactions more easily without splitting payments into smaller amounts. This is particularly helpful for industries like insurance, where premiums can often exceed the previous limits.
Faster and Smoother Payment Flow
With the ability to accept higher-value transactions, businesses can offer smoother payment experiences to their customers. This reduces friction in the payment process, allowing businesses to close deals faster and improve cash flow.
Simplified Compliance and Reporting
The new limits provide an opportunity for businesses to streamline their compliance processes. With the ability to conduct more substantial transactions within a single window, companies can focus on fewer transactions, reducing the need for complex reporting and reconciliation tasks.

B. Impact on Consumers

Increased Convenience for High-Value Transactions
Consumers will find it easier to complete large payments in sectors like insurance and capital markets, where high-value transactions are the norm. With the higher limits, they no longer have to split payments into multiple parts, making the process more efficient and less time-consuming.
Improved Payment Security
The revised transaction limits are designed to accommodate large payments without compromising security. With verified merchants required for specified categories, the risk of fraud or error in high-value transactions is reduced.

How Authbridge Can Support Businesses With The New UPI Updates

As businesses adapt to these changes to UPI transaction limits, AuthBridge can help ensure that compliance, fraud prevention, and merchant verification processes are streamlined.

1. Merchant Verification and KYC Services

For businesses handling larger payments, merchant verification becomes even more critical. AuthBridge’s merchant verification services, including Know Your Business (KYB) and KYC checks, help businesses deal with verified and trustworthy merchants. This is especially important as the scale of transactions increases in the insurance, capital markets, and e-commerce sectors.

2. Compliance with Regulatory Requirements

AuthBridge’s AML (Anti-Money Laundering) and KYC services ensure businesses comply with regulations while conducting large transactions. As transaction limits rise, the need for comprehensive background checks to verify the identity of merchants and customers becomes even more critical.

3. Fraud Prevention Tools

With higher-value transactions, the potential for fraud also increases. AuthBridge’s fraud prevention tools, such as UPI verification, address verification, and contact point verification (CPV) powered by DIGIPIN, ensure that merchants and consumers are thoroughly verified before engaging in large-value transactions. This helps businesses protect themselves from fraudulent transactions and reduce the risk of financial loss.

Conclusion

With verified merchants now eligible for larger transaction amounts, businesses in sectors such as insurance, capital markets, travel, and GeM will find it easier to process large payments without compromising security or efficiency. For businesses looking to take advantage of these changes, AuthBridge’s services can play a major role in ensuring that all necessary verification, compliance, and fraud prevention measures are in place.

By Abhinandan, 1 monthSeptember 17, 2025 ago

Employee Screening

Risk & Compliance

Financial Intelligence

Identity Verification

Utilities

Employment

Entity/Business Level

Professional

Banking & Payments

Fraud Detection

Background Verification

Due-Diligence

Resource Library

Featured Resource

AuthBridge

What Is SOC 2 Compliance?

Types Of SOC 2 Reports: Type I and Type II

SOC 2 Type I

SOC 2 Type II

Issuing Authority And Governance Framework Behind SOC 2 Reports

The Governing Body

The SOC 2 Audit Workflow

The Trust Services Criteria (TSC)

Structure And Components Of A SOC 2 Report

1. Independent Auditor’s Opinion

2. Management’s Assertion

3. System Description

4. Controls, Tests, And Results

5. Complementary User-Entity Controls (CUECs)

6. Other Information And Appendices

How To Read A SOC 2 Report Effectively

Key Measurement Metrics And Evaluation Criteria In SOC 2

Control Objectives and Related Metrics

Sampling and Test Procedures

Exception Rates and Their Significance

Audit Period and Evidence Retention

Operational Maturity Indicators

The Business Value of SOC 2 Type II Compliance

1. Builds Enterprise-Level Client Trust

2. Strengthens Security Posture and Control Discipline

3. Reduces Long-Term Risk and Insurance Burden

4. Creates Efficiency Through Continuous Assurance

5. Enhances Market Credibility and Investor Confidence

6. Positions the Organisation for Regulatory Alignment

Challenges And Best Practices For Sustaining SOC 2 Type II Compliance

Defining The Right Scope

Managing Evidence Properly

Keeping Control Owners Accountable

Watching Vendor Dependencies

Handling Audit Exceptions

Keeping Leadership Involved

Making Compliance Part Of Everyday Work

Cost Considerations For SOC 2 Type II Reports

What To Expect

Conclusion

Introduction

What Is RegTech?

Why The Need For RegTech?

The Technologies Behind RegTech & Its Working

Artificial Intelligence and Machine Learning

Natural Language Processing

Robotic Process Automation (RPA)

Big Data and Advanced Analytics

Blockchain and Distributed Ledger Technology

Cloud Computing, Microservices, and APIs

Optical Character Recognition (OCR) and Computer Vision

Knowledge Graphs and RegData

Cybersecurity and Encryption

Applications Of RegTech

1. Digital KYC and Customer Onboarding

2. Anti-Money-Laundering and Fraud Detection

3. Regulatory Reporting and “RegOps”

4. Corporate and Third-Party Due Diligence

5. Data Privacy and Consent Management

6. Risk and Governance Platforms

7. Cross-Sector Adoption

8. Continuous Compliance

RegTech Uses Across Different Industries

Banking and Financial Services (BFSI)

FinTech and Digital Payments