September 2025

AuthBridge 2

Bank Statement Analysis for Digital Lenders: Signals That Matter

Introduction: The Growing Role of Bank Statement Analysis in Digital Lending

India’s digital lending ecosystem has grown at an unprecedented pace. A joint report by the Reserve Bank of India (RBI) and BCG estimated that the digital lending market in India could reach USD 350 billion by 2026, driven by fintech adoption, smartphone penetration, and a shift towards cashless transactions. Yet, as volumes grow, the pressure on lenders to maintain portfolio quality and meet regulatory expectations has never been greater.

One of the biggest hurdles is assessing borrowers who lack formal credit histories. According to TransUnion CIBIL, nearly 160 million Indians remain “new-to-credit”, making it difficult for lenders to rely on conventional bureau data alone. For this segment, bank statement analysis provides an alternative and highly reliable lens. By examining inflows, outflows, and transactional behaviour, lenders can accurately measure repayment capacity, financial discipline, and early-warning signs of distress.

This shift is also being nudged by regulators. The RBI’s 2022 digital lending guidelines emphasised transparency, data accuracy, and responsible underwriting. In practice, this means that lenders can no longer rely on self-declared income or loosely verified documents. Automated bank statement analysis, supported by AI/ML tools, not only accelerates decision-making but also provides an auditable trail of data-backed lending decisions, aligning with compliance requirements.

Case studies from Indian fintechs underline its importance. For instance, a mid-sized NBFC reported that integrating AI-powered bank statement analysers reduced loan application drop-offs by 40% and cut fraud attempts by detecting doctored PDF statements. Another digital-first lender in the MSME segment used automated analysis to identify irregular seasonal cash flows among small traders, enabling them to design flexible repayment schedules—improving both customer satisfaction and repayment rates.

Key Signals That Matter In Bank Statement Analysis

Bank statements are more than just records of money in and money out — they are behavioural fingerprints that reveal how borrowers manage their finances. For digital lenders in India, where nearly 160 million borrowers remain “new-to-credit” (TransUnion CIBIL, 2023), analysing these signals is essential to bridge the information gap. Below are the most critical dimensions:

Income Stability

Regular salary credits or predictable business inflows indicate a borrower’s repayment capacity. For salaried borrowers, lenders often look for three to six months of consistent credits, while MSMEs and self-employed individuals are evaluated on seasonal cash flow cycles. Inconsistencies, such as sudden salary drops or irregular deposits, may point to employment instability or informal income sources.

Expense Patterns

Spending habits can reveal whether a borrower lives within their means. Fixed expenses such as rent, utilities, and insurance premiums demonstrate financial discipline, while high discretionary spending may raise concerns about repayment ability. A fintech study in 2024 showed that borrowers with discretionary spends exceeding 40% of monthly inflows were 2.3x more likely to default on digital loans.

Obligations And Liabilities

Recurring EMI outflows, credit card bills, and loan repayments provide visibility into a borrower’s existing commitments. By calculating the Debt-to-Income (DTI) ratio, lenders can assess whether new credit would overburden the borrower. In India, NBFCs often flag borrowers with a DTI ratio above 45–50% as high risk.

Liquidity And Buffer

The average monthly balance, frequency of low-balance alerts, and overdraft incidents indicate financial resilience. For instance, a lender may reject applicants whose average monthly balance falls below 10% of their monthly inflows, suggesting they have no cushion for emergencies. Liquidity metrics are particularly crucial for BNPL products and micro-loans, where repayment cycles are short.

Fraud Detection Signals

Fraudulent behaviour remains a key concern. Doctored PDF bank statements, multiple small transactions near month-end (to inflate balances artificially), or sudden large unexplained credits often indicate potential fraud. A mid-tier Indian NBFC reported that AI-powered PDF forgery detection helped prevent 8% of attempted fraudulent applications in 2023.

Benefits Of Automated Bank Statement Analysis for Digital Lenders

The traditional way of manually reviewing bank statements is not only slow but also inconsistent and prone to oversight. In a highly competitive lending environment like India, where loan disbursals are expected in minutes, automation has become a necessity rather than a choice. Automated bank statement analysis powered by AI/ML algorithms offers digital lenders significant advantages across four major dimensions:

Faster Turnaround And Better Customer Experience

Speed is a key differentiator in digital lending. Borrowers today expect near-instant loan decisions, and lenders who take longer risk losing them to competitors. Automated tools can process hundreds of transactions within seconds, reducing loan processing times dramatically. For example, a leading Indian fintech reported cutting its average loan approval time from 48 hours to under 10 minutes after deploying automated statement analysers.

Improved Risk Accuracy

AI systems can identify risk patterns that human reviewers often miss. By analysing inflow stability, recurring obligations, discretionary spends, and liquidity buffers, automated tools create more precise risk scores. A 2023 PwC India study found that AI-driven bank statement analysis improved prediction accuracy for defaults by up to 25% compared to manual methods.

Regulatory Alignment And Auditability

The RBI’s digital lending guidelines (2022) mandate data transparency, fair practices, and proper documentation of underwriting decisions. Automated bank statement analysis provides an auditable trail of decision-making by logging how each lending decision was derived. This not only ensures compliance but also protects lenders in the event of disputes or regulatory reviews.

Fraud Detection And Portfolio Quality

India’s digital lending sector faces increasing cases of doctored PDF bank statements and fraudulent income claims. Automated systems can flag forged documents, duplicated transactions, or inflated balances far more reliably than manual checks. An NBFC using an AI-powered analyser reported detecting fraud in 8% of loan applications that would have otherwise slipped through manual reviews. Over time, this leads to a healthier loan portfolio with lower NPAs (Non-Performing Assets).

Cost Efficiency And Scalability

Manual reviews require large teams of underwriters, which increases operational costs and introduces inconsistencies. Automation allows lenders to scale without proportionally expanding manpower. For high-volume lenders, this translates into significant cost savings—one mid-sized Indian NBFC reported saving ₹1.2 crore annually after automating statement reviews across its MSME loan segment.

Real-Life Applications In India

  1. AI And Machine Learning Advancements

    2. Over the next three to five years, bank statement analysers are expected to shift from rule-based engines to predictive AI systems. These will not just review historical inflows and outflows but forecast future repayment capacity based on behavioural patterns. For instance, AI can predict income volatility for gig workers by comparing weekly deposits and external data such as platform payouts.

  2. Growing Role Of Regulatory Oversight

    3. The RBI’s Digital Lending Guidelines (2022) were only the beginning. With rising digital loan penetration, regulators are likely to demand greater transparency in underwriting algorithms, ensuring that credit scoring models are explainable and non-discriminatory. Bank statement analysis platforms will therefore evolve to include audit trails, explainability dashboards, and compliance reports to align with regulatory mandates.

    Integration With Broader Digital Public Infrastructure

    India’s financial stack, including Aadhaar, UPI, and Account Aggregators (AA), will converge with bank statement analysis to create a seamless lending journey. Through Account Aggregators, borrowers can securely share bank data in real time, allowing instant verification and reducing fraud risks. Analysts predict that by 2027, over 50% of digital loan applications in India will rely on AA-powered statement sharing.

  3. Expansion Beyond Credit Scoring

    4. Bank statement analysis will also move beyond underwriting to become a portfolio management and early-warning tool. By continuously monitoring borrowers’ cash flow health, lenders can trigger pre-emptive interventions—such as restructuring loans or offering top-ups—before defaults occur. This proactive approach could reduce NPAs by an estimated 15–20% across NBFC portfolios.

Conclusion

Bank statement analysis has become a cornerstone of digital lending in India, enabling lenders to serve a growing base of borrowers with speed, accuracy, and confidence. By moving beyond manual reviews to AI-powered, automated systems, lenders gain sharper insights into income stability, obligations, spending patterns, and fraud signals — all of which are critical for responsible lending.

In a market where over 160 million Indians are new-to-credit and digital loan volumes are set to exceed USD 350 billion by 2026, the ability to interpret bank data efficiently will determine which lenders thrive. Coupled with regulatory expectations from the RBI and the adoption of Account Aggregators, bank statement analysis is no longer optional; it is a strategic necessity.

For digital lenders, the future lies in embedding these tools not just at the point of underwriting, but across the lifecycle — from onboarding to monitoring and collections. Done right, bank statement analysis can deliver the dual promise of financial inclusion and portfolio resilience.

FAQ

Bank statement analysis involves reviewing a borrower’s inflows, outflows, and balance patterns to assess creditworthiness. In digital lending, automated tools use AI/ML to extract signals like income stability, obligations, and fraud risks directly from bank statements.

India has a large population of borrowers with limited or no credit bureau history. Bank statements provide an alternative, reliable record of financial behaviour. They help lenders serve “new-to-credit” customers while maintaining compliance with RBI’s digital lending guidelines.

Key signals include regular income inflows, fixed vs discretionary expenses, debt-to-income ratios, liquidity buffers, and red flags such as suspicious large credits or doctored PDFs.

Automation enables lenders to process statements in seconds, reduce manual errors, detect forgery, and generate risk scores. It also ensures auditability, which helps meet regulatory requirements.

Yes. Automated tools can detect anomalies such as identical transactions, out-of-order dates, forged fonts, or inflated balances. Several Indian NBFCs have reported preventing fraud worth crores through automated analysis.

RBI’s 2022 Digital Lending Guidelines emphasise transparency, data accuracy, and fair practices. Automated statement analysis provides auditable trails, documented risk assessments, and ensures data is processed responsibly.

Account Aggregators (AA) will allow borrowers to share bank data securely in real time, making statement analysis seamless. By 2027, it is expected that over half of India’s digital loan applications will rely on AA-based data sharing.

By Siddharth, ago
RBI Master Direction September 2025 PA

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

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).

  1. 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.
  2. Contact Point Verification (CPV) refers to the physical verification of the merchant’s address or place of business.
  3. 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.
  4. An inward transaction refers to any transaction involving the inflow of foreign exchange, while an Outward transaction consists of the outflow of foreign exchange.
  5. 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.
  6. A Merchant means an entity or marketplace that sells goods, provides services, or offers investment products. This also includes exporters and overseas sellers.
  7. Payment channel refers to the method or manner through which a payment instruction is initiated and processed in a payment system.
  8. 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.

  1. 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.

Talk to sales - AuthBridge

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.

  • 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.

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, ago
Increased 2025 UPI Limits

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 Limits 2025
Source: NPCI

Increased UPI Limit Benefits On Businesses And Consumers

A. Impact on Businesses

  1. 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.
  2. 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.
  3. 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

  1. 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.
  2. 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.
Talk to sales - AuthBridge

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, ago
Role-of-AI-in-Vendor-Risk-Management-blog-image2

Role of AI in Vendor Risk Management

Introduction: Why AI Matters Now For Vendor Risk Management

The procurement officer glances at the clock. Another vendor application sits in the queue—hundreds of pages of incorporation documents, compliance records, and financial reports to be checked before approval. What should take days drags into weeks, and yet a single oversight could expose the organisation to regulatory penalties or reputational damage.

This is the everyday reality of vendor onboarding. In fact, a 2024 EY survey found that 62% of executives rank third-party risk among their top five operational concerns, while another study reported that organisations using AI reduced onboarding timelines by up to 40%. The stakes are high: vendors are no longer just service providers—they are extensions of the enterprise, with their risks becoming your risks.

AI is rapidly shifting the equation. Instead of being bogged down by manual due diligence, companies are deploying machine learning and natural language processing to automate verification, score vendor risks in real time, and flag potential red alerts before they escalate. This is not just about efficiency. It is about compliance, resilience, and protecting brand trust in a volatile risk environment shaped by new regulations such as the EU AI Act and India’s DPDPA.

The stakes are clear: in sectors such as financial services and healthcare, a weak vendor due diligence process can result in heavy penalties. For instance, in 2023, several global banks paid fines totalling over USD 2 billion for failures in third-party compliance monitoring. AI, when integrated into onboarding, helps mitigate these risks by providing continuous monitoring, early red-flag detection, and contextual scoring of vendors.

How AI Transforms Vendor Onboarding Processes

Artificial Intelligence is not just streamlining vendor onboarding—it is reshaping the very architecture of how organisations evaluate, approve, and monitor third parties. Instead of relying on fragmented processes, AI enables onboarding to become continuous, intelligent, and risk-aligned.

Automating Data Collection And Validation

One of the most time-consuming stages in vendor onboarding is gathering and validating documents such as incorporation certificates, financial statements, compliance records, and licences. AI-driven platforms can automatically extract, classify, and cross-verify this data against external databases. For example, optical character recognition (OCR) coupled with natural language processing (NLP) allows automated checks of vendor tax registrations or sanction list screenings in seconds rather than days. This not only speeds up onboarding but also reduces error rates that often arise in manual reviews.

Risk-Based Scoring And Predictive Insights

AI enables risk-scoring models that go beyond surface-level checks. By combining financial health indicators, ownership structures, litigation history, ESG (Environmental, Social and Governance) credentials, and even adverse media signals, vendors can be assessed holistically. A 2024 survey by OneTrust found that 74% of organisations using AI-driven risk assessments were able to reduce onboarding timelines by up to 40%, while simultaneously improving the accuracy of red-flag detection. Predictive models also forecast the likelihood of future non-compliance, giving procurement teams the foresight to mitigate risks before contracts are signed.

Continuous Monitoring Rather Than Point-in-Time Checks

Traditional onboarding treats due diligence as a one-off exercise. AI shifts this towards continuous oversight by scanning structured and unstructured data sources on an ongoing basis. For example, if a vendor’s beneficial ownership changes or if negative news coverage emerges, the system can trigger alerts instantly. This allows organisations to maintain compliance with evolving frameworks like the EU AI Act or the Indian DPDPA without repeatedly restarting the onboarding cycle.

Enhancing Supplier Diversity And ESG Alignment

AI is also helping organisations diversify their vendor base and align with ESG commitments. Machine learning models can identify small and medium-sized enterprises (SMEs), women-led businesses, or sustainable suppliers who might otherwise be overlooked. By embedding ESG scoring into onboarding, companies not only strengthen compliance but also demonstrate commitment to responsible sourcing—a factor increasingly valued by regulators and investors alike.

Challenges And Risks Of Using AI In Vendor Onboarding

AI is transforming vendor onboarding, but adoption is not without its pitfalls. Organisations must balance the promise of speed and efficiency with the risks of data misuse, algorithmic opacity, and compliance gaps. Without the right governance, AI can create new vulnerabilities even as it solves old ones.

  1. Data Privacy And Regulatory Compliance

    2. AI-driven onboarding relies on sensitive vendor information—financials, beneficial ownership, regulatory licences—which makes data protection a critical concern. Regulations such as the GDPR in Europe and India’s DPDPA demand explicit consent, purpose limitation, and strong security controls. Failure to comply can be costly: in 2023, the UK’s Information Commissioner’s Office (ICO) reported £200m in fines linked to data protection lapses, many tied to weak third-party oversight.

2. Algorithmic Bias And Transparency

AI models learn from historical datasets, which can embed unintended bias. In vendor onboarding, this could mean unfairly deprioritising small businesses, startups, or suppliers from emerging markets if training data skews towards large, established entities. Moreover, the “black box” nature of many AI models makes it difficult for compliance officers to explain decisions to regulators, creating governance challenges.

3. Over-Reliance On Automation

Automation accelerates onboarding, but excessive dependence on AI can weaken human oversight. For example, a false positive during sanction screening could result in a vendor being unfairly rejected, leading to operational delays and strained supplier relationships. Striking the right balance between AI-driven automation and human judgement remains critical.

4. Cybersecurity And Model Integrity

AI models themselves can be a target for cyberattacks. Adversarial inputs—such as manipulated vendor documents—can trick algorithms into producing false outputs. According to a 2024 EY study, 58% of executives considered AI model security one of their top three risks in third-party management. Protecting AI pipelines with encryption, access controls, and robust audit trails is therefore essential.

5. Cost And Change Management

Implementing AI-driven onboarding platforms requires significant investment, not just in technology but also in training, procurement and compliance teams. Resistance to change, particularly in organisations with entrenched manual workflows, can slow adoption. Moreover, smaller firms may lack the budget to deploy sophisticated AI tools, widening the technology gap across industries.

Best Practices For Implementing AI In Vendor Onboarding

Adopting AI in vendor onboarding is not just about technology—it is about embedding governance, risk, and compliance principles into digital-first workflows. Below are best practices that leading organisations follow, paired with suggested visuals for stronger presentation.

1. Start With Risk Segmentation

Organisations should categorise vendors into high, medium, and low-risk tiers before designing onboarding journeys. AI models can then be calibrated to apply more stringent checks—such as enhanced due diligence or beneficial ownership mapping—only where required. This ensures resources are optimised.

2. Ensure Data Quality And Governance

AI models are only as strong as the data they consume. Establishing a single source of truth through clean, standardised datasets prevents duplication and enhances reliability of AI-driven insights. Governance frameworks should clearly define ownership of vendor data across procurement, compliance, and IT teams.

3. Balance Automation With Human Oversight

AI accelerates onboarding, but human judgement remains vital in interpreting ambiguous results, especially in areas like ESG performance or adverse media coverage. A hybrid “human-in-the-loop” approach reduces the likelihood of false positives or missed risks.

4. Prioritise Explainability And Transparency

Regulators increasingly expect organisations to demonstrate how AI decisions are made. By investing in explainable AI (XAI) frameworks, companies can provide audit trails and clear rationales for vendor risk scores. This is particularly important under laws such as the EU AI Act, where “high-risk systems” must offer traceability.

5. Embed Continuous Monitoring And Feedback Loops

AI models should not be static; they must evolve with changing vendor behaviour, regulatory shifts, and market dynamics. Building feedback loops—where human reviewers tag model errors—ensures the system continuously improves.

6. Foster Cross-Functional Collaboration

AI in vendor onboarding touches multiple stakeholders—from procurement and compliance to IT and legal. Establishing cross-functional governance councils ensures alignment between efficiency goals and regulatory obligations.

AI in vendor onboarding is no longer a future vision—it is already transforming supply chains and third-party ecosystems across industries. By looking at practical applications, we can see how organisations are realising measurable impact in terms of compliance, cost savings, and operational efficiency.

Case Studies And Real-Life Applications Of AI In Vendor Onboarding

AI in vendor onboarding is no longer a future vision—it is already transforming supply chains and third-party ecosystems across industries. By looking at practical applications, we can see how organisations are realising measurable impact in terms of compliance, cost savings, and operational efficiency.

Banking And Financial Services

Large banks are under immense regulatory scrutiny when it comes to onboarding vendors, especially in high-risk regions. One European bank integrated AI into its onboarding workflow to screen vendors against 1,200+ global sanction and watchlists in real time. As a result, it reduced its average onboarding time from 14 weeks to 6 weeks, while cutting manual review costs by 35%. Importantly, the AI system flagged a high-risk vendor with ties to politically exposed persons (PEPs) that manual checks had missed—averting potential reputational and compliance risks.

Healthcare

Healthcare institutions face strict compliance requirements such as HIPAA in the US and GDPR in Europe. One global hospital network adopted AI-driven onboarding to validate credentials of medical equipment suppliers. The AI tool scanned millions of regulatory filings and licence databases to verify authenticity. Within the first year, it caught three vendors attempting to submit falsified certifications—preventing potential legal exposure and safeguarding patient safety.

Manufacturing And ESG

A multinational manufacturer leveraged AI to assess ESG compliance across its 2,000+ supplier base. By monitoring open-source intelligence (OSINT) feeds and regulatory disclosures, the AI engine produced ESG scores that influenced procurement decisions. Within 18 months, the company reported a 25% improvement in ESG compliance rates, enabling it to meet investor expectations and avoid supply chain disruptions linked to unethical practices.

Technology And E-Commerce

A leading e-commerce platform implemented AI-driven continuous monitoring to secure its fast-growing vendor ecosystem. Using machine learning models, it scanned for cybersecurity vulnerabilities across suppliers’ IT infrastructures. This approach detected a data breach in a logistics partner early, allowing the company to switch vendors before customer data was compromised—preserving both compliance and customer trust.

Industry

AI Use Case

Time Saved

Cost Reduction

Key Risk Mitigation

Banking

Automated sanctions & PEP screening

8 weeks

35%

Flagged undisclosed PEP ties

Healthcare

Credential & compliance verification

10 weeks

40%

Identified falsified certificates

Manufacturing

ESG scoring for global suppliers

6 weeks

20%

Screened suppliers for child labour

Tech/E-commerce

Continuous monitoring of vendor networks

12 weeks

30%

Detected cybersecurity breach risk

Future Outlook: AI, Regulation, And The Evolution Of Vendor Onboarding

The future of vendor onboarding will be defined by the intersection of AI innovation and regulatory evolution. While today AI is primarily deployed to accelerate due diligence and reduce costs, tomorrow it will serve as a compliance-first framework that balances transparency, accountability, and resilience in third-party ecosystems.

Stricter Regulatory Expectations

Global regulations are fast catching up with the use of AI in sensitive business functions. The EU AI Act is expected to categorise vendor risk assessment systems as “high-risk,” meaning organisations will need to ensure explainability, bias mitigation, and human oversight. Similarly, India’s DPDPA enforces explicit consent and purpose limitation on data used in AI onboarding models. These frameworks will demand that enterprises not only deploy AI but also build governance architectures around it.

AI-Powered Continuous Assurance

Vendor onboarding will evolve into continuous assurance—where vendors are not only screened once but monitored dynamically throughout the relationship lifecycle. With AI models analysing sanction updates, ownership changes, ESG performance, and cyber events in near real-time, organisations will shift from reactive due diligence to proactive risk prevention.

Integration With Blockchain And Smart Contracts

Looking ahead, AI-powered onboarding will likely converge with blockchain. Smart contracts could automatically verify vendor credentials against immutable registries, while AI risk models adjust contract terms dynamically (for example, tightening SLAs if a vendor’s risk score deteriorates). This convergence could redefine trust in global supply chains.

Rise Of Industry-Specific AI Models

AI will become more sector-specific, with models trained on banking compliance datasets, healthcare certifications, or manufacturing ESG disclosures. These domain-focused engines will reduce false positives and provide contextual insights that generic risk models cannot. For instance, an AI system specialised in healthcare might flag discrepancies in FDA certifications more effectively than a cross-industry tool.

Human-AI Collaboration As The Norm

Finally, the future is not about replacing compliance officers but augmenting them. Human experts will continue to guide policy interpretation, ethical judgement, and exception handling, while AI systems provide scale and speed. Organisations that embrace this hybrid model will be best placed to manage third-party risk while meeting regulatory scrutiny.

In short, the next wave of vendor onboarding will be faster, safer, and more transparent, but only for organisations willing to pair AI efficiency with robust governance.

By Siddharth, ago
Perpetual-KYC-Guide-2025--blog-image

Perpetual KYC Guide 2025 | Continuous Compliance & Monitoring Explained

Introduction To Perpetual KYC

The regulatory landscape surrounding financial services has never been more dynamic. With mounting pressure from regulators, rising instances of financial crime, and increasing customer expectations for seamless experiences, the traditional approach to periodic Know Your Customer (KYC) reviews is no longer sufficient. This has given rise to Perpetual KYC (pKYC) — a model that focuses on continuous monitoring and updating of customer data rather than relying on infrequent reviews.

Perpetual KYC is not merely a compliance obligation; it is fast becoming a strategic enabler for financial institutions, fintechs, and businesses operating in highly regulated sectors. Unlike traditional KYC, which often results in outdated customer profiles between review cycles, pKYC leverages advanced technologies such as artificial intelligence (AI), machine learning (ML), and automation to ensure that customer information remains accurate in real time.

According to a recent report by PwC, nearly 70% of financial institutions in Asia and Europe are exploring or implementing perpetual KYC models to enhance compliance efficiency and customer trust. The approach not only reduces the risk of penalties but also provides an improved customer experience by eliminating redundant requests for documentation and verification.

By embedding continuous due diligence into their workflows, organisations can better identify suspicious behaviour, manage emerging risks, and ensure ongoing compliance with tightening global standards such as the Financial Action Task Force (FATF) recommendations, GDPR, and India’s Digital Personal Data Protection Act (DPDPA).

What Is Perpetual KYC And How Does It Differ from Traditional KYC?

At its core, Perpetual KYC (pKYC) is an evolved form of customer due diligence that moves away from the conventional, periodic review cycles of customer information. Traditional KYC frameworks typically require financial institutions to update customer records at fixed intervals — for instance, every one, three, or five years, depending on the risk classification of the customer. While this approach ensures some degree of compliance, it leaves long gaps during which customer information may become outdated or risk profiles may change unnoticed.

Perpetual KYC, in contrast, focuses on continuous and dynamic monitoring of customer data. Instead of waiting for the next review cycle, institutions receive real-time updates and alerts whenever a customer’s risk profile changes. For example, if a client is added to a sanctions list, flagged in adverse media, or experiences significant changes in financial behaviour, the system immediately triggers a review. This allows organisations to respond proactively, rather than retrospectively.

The use of automation, machine learning, and big data is what makes perpetual KYC practical. Automated workflows connect to external data sources such as credit bureaus, sanctions lists, regulatory databases, and news outlets, ensuring that customer profiles are always current. This reduces the reliance on manual interventions, minimises operational overheads, and lowers the likelihood of human error.

A practical illustration can be seen in the BFSI sector, where regulators are tightening their stance on anti-money laundering (AML) and counter-terrorism financing (CTF). In India, for instance, the Reserve Bank of India (RBI) has pushed banks towards stronger ongoing monitoring practices, aligning with the global shift towards perpetual KYC. Similarly, in Europe, regulatory bodies under the Fifth and Sixth Anti-Money Laundering Directives (AMLD) have encouraged institutions to adopt more proactive monitoring measures.

Why Is Perpetual KYC Important?

The growing emphasis on perpetual KYC (pKYC) is not simply a matter of regulatory compliance; it is an operational and strategic necessity in today’s complex financial environment. With global financial crime estimated to cost the economy over US $3 trillion annually (United Nations Office on Drugs and Crime), regulators across jurisdictions are urging institutions to adopt more robust and continuous monitoring measures.

Enhancing Regulatory Compliance

Perpetual KYC ensures organisations remain compliant with tightening global and domestic regulations. Traditional periodic reviews can create regulatory blind spots where risks may go undetected for years. Continuous monitoring eliminates these gaps by ensuring that any change in a customer’s profile — such as inclusion in a sanctions list, a politically exposed person (PEP) designation, or adverse media mention — is flagged immediately. This proactive approach reduces the likelihood of penalties and reputational damage.

Improving Risk Management

By keeping customer data updated in real time, financial institutions can manage risks more effectively. For instance, if a vendor or customer shows signs of financial distress or legal entanglement, the system raises alerts that allow timely intervention. This dynamic risk assessment model strengthens the institution’s ability to detect money laundering, fraud, and terrorist financing activities before they escalate.

Reducing Operational Inefficiencies

Traditional KYC reviews often require repeated outreach to customers for updated documents, which not only frustrates clients but also consumes significant internal resources. pKYC automates much of this process by leveraging application programming interfaces (APIs) and external data sources, thereby reducing manual workload. A study by Deloitte notes that automation in compliance processes can reduce operational costs by up to 30%.

Strengthening Customer Experience

Customers today expect seamless and frictionless interactions with financial institutions. Repeated requests for documentation during periodic reviews can negatively impact trust and satisfaction. Perpetual KYC solves this challenge by minimising unnecessary customer interactions, updating records silently in the background, and only engaging the customer when a genuine risk or compliance gap is detected.

Supporting Global Expansion

For multinational corporations and banks, perpetual KYC is critical in managing compliance across diverse jurisdictions. Global frameworks such as the FATF recommendations and regional regulations like the EU AML Directives demand proactive monitoring. Adopting pKYC ensures organisations remain globally audit-ready while accommodating local compliance variations.

Step-By-Step Process of Implementing Perpetual KYC

Transitioning from periodic reviews to a perpetual KYC framework requires more than technology adoption; it involves rethinking compliance workflows, aligning stakeholders, and embedding continuous monitoring into the organisation’s DNA. Below is a structured approach that institutions can follow.

Step 1: Define Risk Appetite And Framework

The first step is to clearly define the organisation’s risk appetite. This includes identifying what constitutes high, medium, and low-risk customers, and how frequently their data should be refreshed. For instance, a politically exposed person (PEP) may require near real-time monitoring, whereas a low-risk salaried customer might only require periodic updates supported by automated checks.

Step 2: Integrate Data Sources

Perpetual KYC depends heavily on real-time data ingestion. Institutions must integrate multiple external and internal data sources such as government registries, sanctions and watchlists, adverse media feeds, and credit bureaus. These integrations ensure that any significant change in a customer’s profile is captured immediately.

Step 3: Deploy Automation And Artificial Intelligence

Automation lies at the heart of pKYC. By using APIs, robotic process automation (RPA), and artificial intelligence (AI), organisations can automate repetitive tasks such as document verification, screening, and flagging anomalies. Machine learning models can also predict emerging risks based on behavioural patterns, providing a forward-looking lens to compliance.

Step 4: Establish Trigger-Based Reviews

Instead of periodic, calendar-based reviews, pKYC works on a trigger-based model. This means that events such as a change in ownership, sudden shifts in transaction behaviour, or inclusion in a sanctions list automatically initiate a KYC review. This ensures risks are addressed promptly rather than waiting for the next scheduled review cycle.

Step 5: Build Compliance Dashboards And Reporting Mechanisms

To make perpetual KYC effective, institutions need real-time dashboards that consolidate alerts, risk scores, and customer profiles into a single view. These dashboards should be accessible to compliance officers, auditors, and regulators to ensure transparency and audit readiness.

Step 6: Train Teams And Foster A Compliance Culture

Technology alone cannot deliver perpetual KYC. Staff need to be trained to interpret AI-driven alerts, escalate high-risk cases, and engage with customers where necessary. Building a compliance-first culture ensures that pKYC is embedded across departments, not siloed in compliance functions.

Step 7: Continuous Feedback And Improvement

Finally, organisations must regularly review and refine their pKYC processes. By analysing false positives, monitoring efficiency metrics, and collecting feedback from both compliance teams and customers, institutions can continuously improve the accuracy and effectiveness of their framework.

Challenges In Adopting Perpetual KYC

While the benefits of perpetual KYC (pKYC) are significant, many institutions face hurdles in transitioning from traditional periodic reviews to a continuous monitoring framework. These challenges are not only technological but also regulatory, cultural, and operational in nature.

Data Integration And Quality Issues

One of the most pressing challenges is the integration of disparate data sources. Banks and financial institutions often rely on fragmented legacy systems that do not communicate seamlessly with each other. Without accurate, up-to-date, and harmonised data, perpetual KYC loses its effectiveness. Furthermore, poor data quality can lead to false positives or overlooked risks, undermining the reliability of the entire system.

High Implementation Costs

Deploying a perpetual KYC system requires significant upfront investment in automation, artificial intelligence, and data analytics infrastructure. Smaller financial institutions may find these costs prohibitive, even though the long-term benefits outweigh the initial expenses. Gartner estimates that global spending on regulatory technology (RegTech) solutions is expected to exceed USD 200 billion by 2027, highlighting the financial weight of compliance digitisation.

Regulatory Ambiguity

Although regulators are increasingly supportive of continuous monitoring, explicit guidelines on perpetual KYC are still evolving. In markets such as India, Europe, and parts of Asia, ambiguity in regulatory requirements can create uncertainty for compliance teams, who are reluctant to move away from tried-and-tested periodic reviews without clear regulatory endorsement.

Cultural And Organisational Resistance

Transitioning to pKYC also requires a shift in mindset. Compliance teams accustomed to traditional methods may resist change due to the fear of job redundancy, lack of familiarity with technology, or scepticism about AI-driven decision-making. Overcoming this resistance requires careful change management and training.

Privacy And Data Protection Concerns

Continuous monitoring involves the collection and processing of large volumes of sensitive customer data. This raises concerns around data privacy and compliance with data protection laws such as the General Data Protection Regulation (GDPR) in Europe and the Digital Personal Data Protection Act (DPDPA) in India. Institutions must ensure that perpetual KYC systems incorporate robust encryption, consent mechanisms, and privacy safeguards.

Managing False Positives

Automated systems may sometimes generate excessive false positives, particularly during the early stages of deployment. This can overwhelm compliance officers and dilute focus on genuine risks. Fine-tuning machine learning models and refining rule-based engines are essential to reduce noise and ensure efficiency.

Benefits Of Perpetual KYC

Despite the hurdles to implementation, the advantages of adopting perpetual KYC far outweigh the challenges. For financial institutions and regulated entities, pKYC represents a forward-looking approach that delivers compliance efficiency, improved risk management, and better customer experiences.

Proactive Risk Management

Unlike periodic reviews that only capture risks at set intervals, perpetual KYC ensures real-time identification and mitigation of risks. If a client suddenly appears in adverse media or is added to a sanctions list, the system generates an alert instantly. This enables compliance teams to respond proactively, preventing financial crime before it escalates.

Regulatory Confidence

Perpetual KYC strengthens an institution’s position with regulators. Demonstrating the ability to maintain up-to-date customer profiles and ongoing monitoring reassures supervisory bodies that compliance frameworks are both robust and dynamic. This can significantly reduce the risk of penalties and reputational harm.

Operational Efficiency

Through automation, perpetual KYC reduces the need for repetitive manual reviews and paperwork. By connecting with multiple external databases via APIs, compliance teams can focus on high-value investigations rather than routine data checks. Studies have shown that institutions implementing automation within compliance can reduce operational costs by 20–30% over time.

Enhanced Customer Experience

Traditional KYC reviews often inconvenience customers, requiring repeated document submissions and unnecessary interactions. Perpetual KYC minimises these frictions by updating records silently in the background. Customers are only engaged when there is a significant change, creating a frictionless and customer-centric experience.

Scalability Across Geographies

For multinational firms, perpetual KYC provides a scalable model that adapts across regulatory landscapes. By integrating global watchlists, sanctions databases, and local compliance rules, organisations ensure they remain compliant across multiple jurisdictions simultaneously.

Better Fraud Prevention

Continuous monitoring, combined with AI-driven analytics, helps identify suspicious activity such as unusual transaction patterns or identity anomalies. This makes perpetual KYC an important component of fraud prevention frameworks, complementing anti-money laundering (AML) strategies.

Perpetual KYC In Practice: Use Cases and Industry Examples

Perpetual KYC (pKYC) is not just a theoretical concept; it is being increasingly adopted across industries where regulatory compliance, fraud prevention, and trust are critical. Below are examples of how different sectors are applying pKYC in practice.

Banking, Financial Services, And Insurance (BFSI)

The BFSI sector has been at the forefront of adopting perpetual KYC due to stringent anti-money laundering (AML) and counter-terrorism financing (CTF) requirements. For example, European banks implementing pKYC in line with the Fifth and Sixth Anti-Money Laundering Directives (AMLD) have reported a reduction in compliance costs by 15–20% while improving the timeliness of suspicious activity reporting. In India, the Reserve Bank of India (RBI) has also signalled its preference for ongoing monitoring frameworks to strengthen financial stability.

Fintech And Digital Payment Platforms

Fintech firms and payment service providers often handle large volumes of transactions daily, making them vulnerable to fraud and money laundering. A study by Deloitte highlighted that fintechs using continuous KYC models reduced fraudulent account openings by over 25% within the first year of adoption. By integrating real-time data feeds, fintechs can instantly validate customer identities and transaction behaviours without burdening users with constant re-verification requests.

Telecommunications

Telecom operators face risks of identity fraud, particularly in the issuance of SIM cards and digital services. Several operators in Asia and Africa have begun implementing perpetual KYC models to ensure that customer identities remain valid and compliant with regulatory frameworks. Continuous KYC has helped these companies reduce SIM-related fraud while aligning with government-mandated identity verification standards.

Corporate Vendor Onboarding And Third-Party Risk

Beyond individual customer checks, perpetual KYC is increasingly being applied in vendor and third-party onboarding. Corporates with complex supply chains use pKYC to monitor the compliance status of vendors continuously. For instance, procurement departments in manufacturing firms track whether their suppliers remain compliant with tax obligations, sanctions checks, and ESG (environmental, social, governance) standards. This proactive monitoring strengthens supply chain resilience.

Wealth Management And High-Net-Worth Clients

In wealth management, where clients often hold multiple accounts across jurisdictions, perpetual KYC ensures that any changes in their risk profile — such as political exposure, legal disputes, or offshore investments — are detected immediately. This helps private banks manage reputational risks and regulatory obligations more effectively.

Future Of Perpetual KYC: Trends And Technologies

As regulatory expectations tighten and the scale of financial crime continues to expand, perpetual KYC (pKYC) is set to evolve further, shaped by emerging technologies and changing compliance priorities. Institutions that embrace these advancements will not only strengthen compliance but also gain a competitive edge through efficiency and customer trust.

Artificial Intelligence And Machine Learning

AI and machine learning (ML) will continue to drive perpetual KYC by making risk assessments more predictive and less reactive. Instead of flagging anomalies after they occur, ML algorithms can forecast potential risks based on patterns in customer transactions, behavioural signals, and network relationships. According to McKinsey, the use of AI in compliance can improve detection accuracy by up to 50% while reducing false positives.

Blockchain And Distributed Ledgers

Blockchain technology has the potential to revolutionise customer due diligence by providing immutable, transparent, and easily shareable records. A decentralised KYC utility powered by blockchain would allow institutions to verify customer data once and use it across multiple entities securely, reducing duplication and enhancing trust. Countries such as Singapore and the UAE have already piloted blockchain-based KYC systems.

RegTech And API Ecosystems

The rise of regulatory technology (RegTech) has introduced a new wave of automation tools that integrate seamlessly with existing banking and compliance systems. Through APIs, perpetual KYC can plug into sanctions lists, government registries, and adverse media sources in real time. Juniper Research projects that global RegTech spending will surpass USD 200 billion by 2027, underscoring its role in shaping compliance futures.

Focus On ESG And Sustainability Risks

Regulators and investors are increasingly prioritising environmental, social, and governance (ESG) considerations. Perpetual KYC will extend beyond financial and compliance risks to include ESG risk monitoring. For instance, a vendor’s involvement in environmentally harmful practices or human rights violations could now form part of their ongoing risk profile.

Customer-Centric Compliance Models

Future iterations of perpetual KYC will balance compliance requirements with user experience. Biometric authentication, digital IDs, and consent-driven data flows will ensure that customers face minimal friction while institutions maintain compliance. This approach aligns with privacy laws such as GDPR and India’s DPDPA, which demand transparency and accountability in data use.

FAQ

Perpetual KYC (pKYC) is a continuous compliance model where customer information is updated and monitored in real time, rather than through periodic reviews. It leverages automation, AI, and data integrations to ensure risk profiles remain accurate at all times.

Traditional KYC reviews customer data at fixed intervals, which can leave long gaps where risks go undetected. Perpetual KYC, on the other hand, works on a trigger-based model that updates records whenever significant changes occur, such as sanctions listing, adverse media, or changes in ownership.

Perpetual KYC helps financial institutions reduce regulatory risks, detect suspicious activity earlier, and improve customer experience. It also demonstrates proactive compliance to regulators, which can reduce the likelihood of penalties or audits.

Perpetual KYC is powered by artificial intelligence, machine learning, automation, APIs, and in some cases, blockchain. These technologies allow for real-time monitoring of customer data across multiple external and internal sources.

Challenges include high implementation costs, integration of fragmented data sources, regulatory ambiguity, resistance to change within compliance teams, and managing false positives. Institutions must also balance continuous monitoring with strict data privacy obligations under laws like GDPR and DPDPA.

Although BFSI remains the primary adopter, industries such as fintech, telecom, manufacturing (vendor due diligence), and wealth management increasingly benefit from pKYC due to high exposure to regulatory and reputational risks.

Yes. By reducing the need for repeated document submissions and unnecessary outreach, pKYC creates a frictionless compliance process. Customers are only contacted when genuinely necessary, enhancing trust and satisfaction.

By Siddharth, ago
TS Product update 2025

AuthBridge Product Updates 2025: TruthScreen

With Broad AI becoming more prevalent than ever, giving rise to Generative AI-powered Agentic AI and other AI models, it is easy to say that fraud today is no longer confined to crude forgeries or obvious impersonations. AI-generated images, falsified/forged documents, and unreliable data trails have made businesses’ risks more sophisticated and severe than before. At the same time, customers expect instant approvals, regulators demand strict compliance, and operational teams cannot afford bottlenecks or repeated failures.

At AuthBridge, we have always believed that trust is built not by chance but by design. Every new service we launch, every update we roll out, is driven by one question: how do we make your verification workflows more secure, intelligent, and reliable without slowing you down?

This latest set of enhancements on TruthScreen does answer those questions precisely. These updates are designed to protect your business while enhancing your customer experience.

We’re constantly pushing the boundaries of identity verification and risk management technology, and we’re thrilled to share the latest updates designed to empower your business.

Fraud & Forgery Detection

Deepfake And AI-Generated Image Detection

One of the most significant threats to digital verification today comes from deepfakes and AI-generated images. These synthetic/morphed visuals can mimic real people so convincingly that a manual review or even a standard system may fail to spot them.

AuthBridge's Deepfake Detection tech

TruthScreen adds advanced computer vision algorithms to not just compare faces, but also analyse pixel-level patterns, shadows, and other subtle cues that AI often gets wrong. Cross-checking against natural human facial markers can flag suspicious images instantly, thanks to Generative Adversarial Network (GAN) technology. This result is then shared with the user as a match score between 0-1, with the values closer to 1 signifying a high probability of the image being AI-generated.

Document Forgery Detection

From tampered payslips to altered educational certificates, forged documents remain a standard gateway for fraud. Traditional checks based on legacy processes often catch obvious mistakes, but sophisticated forgers manipulate PDFs in ways that slip past the human eye.

PDF Forgery Detection Tech AuthBridge

TruthScreen’s new update applies document forensics combined with AuthBridge’s existing OCR (optical character recognition) tech. It scans the text and examines the digital “fingerprints” of a file, including metadata, fonts, edits, and compression artefacts, to detect whether a document has been manipulated.

Advanced Address Intelligence & Geo-Mapping

Address Augmentation

Addresses can be very complex — misspellings, incomplete entries, inconsistent address formats, or even fake submissions can slip through during onboarding. Left unchecked, these create headaches for compliance teams, delivery partners, and field verification executives.

Address Verification

TruthScreen’s updated Address Augmentation service fixes this by running the provided address through multiple trusted data sources and geocoding engines. It cleans, enriches, and standardises the input, then assigns a match score to show how confident the system is in the accuracy of that address.

DIGIPIN ↔ Address & Latitude/Longitude Conversions

With increased demand for precision in deliveries, India Post, earlier this year, took a major step forward by introducing DIGIPIN—an advanced 10-digit digital addressing system. TruthScreen’s latest update leverages the use of DIGIPIN to bridge addresses and geographic coordinates seamlessly. This is powered by reverse-geocoding AI pipelines that cross-check multiple mapping datasets to ensure precision.

  • Digipin to Address & Geo-coordinates: Converts a Digipin into a verified postal address and its exact latitude/longitude.

  • Address to Digipin & Geo-coordinates: Turns a written address into a unique Digipin and accurate map location.

  • Latitude/Longitude to Address & Digipin: Translates raw coordinates into a postal address and Digipin.

Identity Verification

PAN V2

The Permanent Account Number (PAN) verification is central to almost every risk check, from opening bank accounts to approving loans and screening employees. But legacy systems often produced inconsistent results, missed matches, false negatives, or timeouts, which slowed down onboarding.

TruthScreen’s PAN V2 update addresses these concerns by using improved data matching algorithms to cross-check PAN details with greater precision, while handling errors (such as minor typos or mismatched formats) more effectively. It also leverages optimised query handling and fallback processes to reduce drop-offs during high traffic.

Reliability Enhancements With Increased Service Uptimes

Fallback Vendor In Detailed RC Service (Online & Offline)

Vehicle-linked checks, such as RC verification, are crucial for lending, insurance, logistics, and mobility businesses. But what happens if the primary verification provider experiences downtime? Traditionally, that translates to delays, failed applications, and unhappy customers.

vehicle details sample
vehicle details sample 2
RC details sample
RC details sample 2
RC sample

If the primary provider fails, TruthScreen’s fallback vendor mechanism for Detailed RC services automatically reroutes the request to an alternate vendor. This “always-on” logic ensures the verification doesn’t stop when your business needs it most.

Fallback Mechanism In PAN And PAN–Aadhaar Seeding

The same resilience now extends to PAN verification and PAN–Aadhaar seeding. Both services come with a built-in fallback process, meaning if one provider path fails, the system retries through another — automatically and seamlessly.

Truthscreen PAN Sample report

This is powered by advanced deep learning algorithms, employing queueing systems and multi-path routing, ensuring every request finds its way to a working endpoint without manual intervention.

Conclusion

With these enhancements, TruthScreen strengthens its role as the backbone of secure and seamless verification. By combining fraud and forgery detection, smarter address intelligence, sharper identity verification, and rock-solid fallback mechanisms, the platform empowers businesses to stay ahead of evolving risks while keeping customer journeys smooth. For BFSI, fintech, e-commerce, staffing, logistics, and beyond, these updates mean one thing above all: greater confidence that every decision is built on trust.

By Abhinandan, ago
ICR-Blog-image

Intelligent Character Recognition (ICR) 2025: A Comprehensive Guide

Introduction

As the digital economy expands, the need for accurate, efficient, and compliant data processing has never been greater. Traditional Optical Character Recognition (OCR), though useful, often struggles with the complexity of handwritten, cursive, or semi-structured documents. This is where Intelligent Character Recognition (ICR) has emerged as a transformative technology.

ICR builds upon OCR by using advanced machine learning (ML) and neural network models to not only recognise printed text but also interpret handwritten inputs and evolving writing styles. In industries such as banking, financial services, insurance, healthcare, and e-commerce, where millions of customer onboarding and verification documents are processed daily, ICR reduces manual intervention, accelerates turnaround times, and strengthens compliance frameworks.

The significance of ICR in 2025 lies in its ability to bridge the gap between legacy documentation and digital-first compliance ecosystems. According to a report by MarketsandMarkets, the document analysis and recognition market is projected to grow at a CAGR of 13.8% between 2023 and 2028, fuelled by regulatory digitisation drives, data-heavy onboarding processes, and the need for fraud prevention.

At AuthBridge, ICR is not merely a productivity tool but a cornerstone of intelligent verification workflows. By integrating ICR into its AI-driven platforms, AuthBridge enables clients to extract critical data from handwritten forms, application documents, and scanned records with high accuracy, audit readiness, and seamless integration into compliance pipelines.

What Is Intelligent Character Recognition (ICR) And How Does It Work?

Intelligent Character Recognition (ICR) is an advanced form of text recognition technology designed to read and digitise handwritten characters, symbols, and free-form text. Unlike traditional Optical Character Recognition (OCR), which is best suited to printed and structured documents, ICR employs artificial intelligence (AI), machine learning (ML), and neural networks to interpret the complexity of human handwriting.

At its core, ICR works by breaking down handwritten text into smaller components such as strokes and curves, which are then matched against a continuously expanding dataset of writing styles. This adaptive nature allows the technology to “learn” over time, improving accuracy with every new data point processed. Modern ICR systems can handle diverse languages, writing speeds, and even cursive inputs, making them invaluable in regions where handwritten documentation remains prevalent.

The functional process typically involves:

  1. Scanning and Pre-Processing: The handwritten document is digitised through high-resolution scanning or mobile capture. Noise reduction and image enhancement techniques are applied to prepare the text for recognition.

  2. Character Recognition: AI models trained on thousands of writing styles interpret each symbol or letter, even in unstructured formats.

  3. Contextual Analysis: Beyond individual characters, ICR uses natural language processing (NLP) to understand context, reducing errors in similar-looking characters (for example, distinguishing “0” from “O”).

  4. Data Integration: The extracted data is then validated, structured, and pushed into digital systems such as compliance dashboards, customer onboarding workflows, or enterprise databases.

In 2025, ICR is particularly critical for industries transitioning to regulatory digitisation. For example, in India, where forms like PAN, Aadhaar updates, or medical records often involve handwritten inputs, ICR ensures data is accurately extracted, cross-verified, and securely stored without the latency of manual reviews.

At AuthBridge, ICR is integrated within broader verification ecosystems, ensuring that extracted data undergoes real-time validation against government registries, financial databases, and proprietary risk engines. This dual-layer approach—intelligent extraction plus instant verification—helps enterprises stay compliant while reducing fraud risks.

Applications Of ICR In Today’s Digital Ecosystem

The relevance of Intelligent Character Recognition (ICR) has expanded far beyond basic document digitisation. In 2025, ICR is central to enabling scalable, compliant, and fraud-resistant digital ecosystems, particularly in industries where the volume of handwritten or semi-structured documents remains significant.

Banking And Financial Services

Financial institutions handle millions of handwritten documents such as loan applications, KYC forms, and bank statements. According to the Reserve Bank of India (RBI), nearly 30% of all customer onboarding forms in semi-urban and rural branches still involve handwritten inputs. By integrating ICR into their workflows, banks can reduce onboarding times by up to 60%, while ensuring accuracy in extracting critical data such as account numbers, addresses, and signatures.

For fintechs, ICR enhances digital lending by extracting income and employment details from bank statements or handwritten payslips, enabling faster credit risk assessments.

Healthcare

In healthcare, where patient safety and compliance are paramount, ICR plays a crucial role in digitising handwritten prescriptions, insurance claim forms, and diagnostic reports. A study by IBM Watson Health (2024) noted that data-entry errors in manual claims processing account for nearly $20 billion in annual losses globally. With ICR, hospitals and insurers can reduce manual workloads, ensure faster claims settlement, and improve patient record accuracy.

E-Commerce And Retail

Vendor onboarding and logistics in e-commerce often involve handwritten invoices, delivery receipts, or return notes. ICR enables seamless digitisation of such unstructured data, helping marketplaces maintain compliance with taxation and GST norms in India, while improving operational efficiency.

Government And Public Services

Governments continue to rely on physical forms for services such as tax filings, voter enrolment, and public health schemes. In India, despite aggressive digitisation drives, millions of Aadhaar updates and public scheme applications are still handwritten. By embedding ICR, agencies can ensure faster service delivery and reduce the risk of fraudulent entries.

Insurance

Insurance companies globally process vast amounts of handwritten claim forms and supporting documents. According to the OECD (2023), insurance fraud accounts for 10% of all claims filed worldwide, with many stemming from poor data verification. ICR integrated with real-time validation platforms, such as AuthBridge’s APIs, helps insurers verify authenticity at scale.

Benefits Of Intelligent Character Recognition (ICR)

The true value of Intelligent Character Recognition (ICR) lies in its ability to transform manual, error-prone processes into streamlined, automated workflows that support both compliance and operational efficiency. For enterprises operating in highly regulated industries, the benefits extend beyond speed to encompass accuracy, cost savings, and fraud prevention.

Enhanced Accuracy And Efficiency

ICR employs AI and neural networks that improve accuracy with each new dataset processed. Unlike OCR, which is static, ICR learns continuously, reducing misinterpretation of complex handwriting. In large-scale operations, this translates into significant time savings. For example, an AuthBridge pilot with a leading NBFC reduced document processing times by 55% when ICR was integrated into its loan application workflows.

Strengthened Compliance

Regulatory frameworks such as RBI’s KYC Master Directions, HIPAA in healthcare, and India’s DPDPA require that customer data is collected, stored, and retrievable in audit-ready formats. By digitising handwritten records and ensuring clean data input, ICR helps institutions maintain compliance without relying on costly manual audits.

Fraud Prevention

Fraud often exploits gaps in manual verification. Handwritten documents such as forged application forms or altered claim documents are common sources of fraud. ICR integrated with AuthBridge’s verification APIs enables instant cross-checking of extracted data against authoritative registries (e.g., PAN, Aadhaar, GSTIN), flagging anomalies before they escalate.

Cost Reduction

By reducing reliance on manual labour, enterprises can significantly cut operational costs. According to Deloitte (2023), automation in document processing delivers an average cost saving of 25–35% annually. For industries like BFSI and insurance, where volumes are high, these savings can be substantial.

Improved Customer Experience

ICR accelerates onboarding, claims settlement, and service requests, all of which directly improve customer satisfaction. Faster turnaround builds trust, particularly in digital-first ecosystems, where customers expect speed and accuracy in equal measure.

📊 Table: Comparative Impact Of ICR Vs Traditional OCR

Metric

OCR

ICR

Accuracy (Handwriting)

60–70%

85–95% (improves with learning)

Compliance Readiness

Limited

High – structured, audit-friendly

Fraud Detection

Minimal

Integrated with real-time APIs

Processing Speed

Moderate

Faster with AI-driven learning

Cost Savings

Low (manual review required)

High (reduced manual intervention)

Challenges Of Implementing ICR And How They Can Be Overcome

While Intelligent Character Recognition (ICR) offers remarkable benefits, its adoption is not without challenges. Enterprises across BFSI, healthcare, and e-commerce often face barriers related to accuracy, cost, and integration with existing systems. However, these hurdles can be effectively addressed with the right technology partner.

Accuracy Limitations In Complex Handwriting

ICR systems, though adaptive, may struggle with highly cursive or poor-quality handwriting. For instance, documents scanned at low resolution or filled in hastily can lead to reduced accuracy. This poses risks in industries such as insurance, where a misread digit in a claim amount could cause significant delays.

AuthBridge’s Approach: By combining ICR with multi-layered validation—such as cross-referencing extracted data with official registries (PAN, Aadhaar, GSTIN)—AuthBridge ensures errors are flagged and corrected before they impact downstream processes.

Data Privacy And Security Concerns

Healthcare and BFSI sectors deal with sensitive personal information. Transferring handwritten forms into digital repositories raises compliance concerns under HIPAA, GDPR, and India’s DPDPA.

AuthBridge’s Approach: All ICR-enabled workflows are deployed on ISO 27001 and SOC 2-certified environments, with encrypted data pipelines and strict retention controls, ensuring compliance with both domestic and global regulations.

Integration With Legacy Systems

Many organisations continue to use outdated legacy systems for document management. Integrating advanced ICR solutions into such environments can be costly and complex.

AuthBridge’s Approach: Through its iBridge™ platform, AuthBridge provides API-first integration capabilities, enabling clients to embed ICR seamlessly into existing onboarding, claims, or compliance workflows without major infrastructure overhauls.

Cost Of Implementation

Small and mid-sized enterprises often hesitate due to perceived high costs of adopting ICR technology.

AuthBridge’s Approach: With pay-per-use models and scalable deployment options, AuthBridge makes ICR accessible for businesses of all sizes, ensuring cost efficiency without compromising on accuracy or compliance.

The Future Of ICR In Digital Verification (2025 And Beyond)

As industries accelerate towards digital-first ecosystems, the role of Intelligent Character Recognition (ICR) is set to evolve further, becoming a cornerstone of compliance and fraud prevention frameworks. The convergence of AI, machine learning, and natural language processing (NLP) is expected to enhance ICR accuracy and adaptability, particularly in complex, multilingual environments such as India.

AI-Driven Continuous Learning

Future ICR systems will incorporate self-improving neural networks, allowing them to learn from every document processed. This means accuracy levels of over 98% are expected in the next few years, even for cursive or inconsistent handwriting.

Multilingual And Regional Adoption

In markets like India, where handwritten forms span dozens of regional languages, ICR will expand beyond English and Hindi to support vernacular scripts. This will be particularly important for government schemes, healthcare enrolments, and rural banking initiatives.

Fraud Detection And Risk Intelligence

By integrating with risk intelligence engines like AuthBridge’s, ICR will go beyond data extraction to act as a fraud detection tool. For example, forged cheques, altered prescriptions, or manipulated invoices can be identified at source when ICR outputs are cross-verified against trusted registries.

Regulatory Adoption

Governments and regulators are already pushing for digitisation of legacy records. In India, the DPDPA (2023) emphasises secure, consent-driven digitisation of personal data, while regulators like the RBI mandate audit-ready onboarding records. ICR will play a vital role in ensuring compliance with these mandates by bridging the paper-to-digital divide.

Integration With Broader AI Ecosystems

ICR will no longer remain a standalone tool. Instead, it will integrate into holistic AI ecosystems that combine document verification, face matching, liveness detection, and AML screening. AuthBridge is already moving in this direction, ensuring that ICR is not just about digitisation but about creating trusted digital identities.

Conclusion And Key Takeaways

The journey from paper-based processes to digital-first ecosystems cannot be complete without technologies that bridge the gap between the physical and digital worlds. Intelligent Character Recognition (ICR) stands at the heart of this transition, enabling enterprises to convert handwritten, unstructured data into actionable, verified, and audit-ready information.

For industries such as banking, insurance, healthcare, and e-commerce, ICR is more than an operational tool—it is a strategic enabler of compliance, fraud prevention, and efficiency. By automating what was once manual and error-prone, organisations can reduce costs, improve customer experience, and strengthen their regulatory standing.

At AuthBridge, ICR is not deployed in isolation but as part of a comprehensive digital verification ecosystem. Whether it is onboarding a customer in rural India, verifying handwritten prescriptions in healthcare, or ensuring compliance in insurance claim settlements, AuthBridge’s ICR-enabled solutions provide speed, security, and trust at scale.

As we look ahead, the future of ICR lies in AI-driven learning, multilingual adaptability, and integration into broader digital identity verification frameworks. Enterprises that adopt ICR today will not only stay ahead of compliance mandates but also build the foundation of resilient, customer-centric digital ecosystems.

FAQ

Intelligent Character Recognition (ICR) is an advanced technology that interprets and digitises handwritten characters and unstructured text using AI and machine learning. Unlike Optical Character Recognition (OCR), ICR learns from evolving writing styles, making it suitable for industries where handwritten documentation remains common.

OCR is effective at recognising printed, structured text, but it struggles with handwriting and cursive inputs. ICR builds upon OCR by using neural networks and adaptive algorithms that continuously learn, improving recognition accuracy over time.

ICR is particularly valuable in banking, insurance, healthcare, e-commerce, and government services. For example:

  • Banks use ICR to process handwritten KYC forms and loan applications.

  • Healthcare providers digitise prescriptions and claims.

  • Insurers use it for faster claims processing and fraud detection.

  • Governments rely on it for public scheme enrolments and record digitisation.

ICR ensures that data captured from handwritten forms is accurate, structured, and audit-ready. AuthBridge integrates ICR with its compliance workflows, enabling real-time validation against government registries (e.g., PAN, Aadhaar, GSTIN) and aligning with regulations like RBI KYC Master Directions, HIPAA, GDPR, and India’s DPDPA.

Yes. Fraud often originates from manipulated handwritten documents, such as altered application forms or insurance claims. AuthBridge’s ICR-enabled solutions cross-check extracted data against authoritative databases and global watchlists, flagging anomalies before they escalate.

AuthBridge leverages ICR as part of its end-to-end digital verification ecosystem, integrated into platforms such as iBridge™, and GroundCheck.ai. This ensures that digitised data is not only recognised but also validated, risk-scored, and compliant with regulatory standards.

In India, where handwritten documents are still common in banking, healthcare, and government schemes, ICR will play a critical role in bridging the gap between paper records and digital compliance. The future lies in multilingual ICR systems capable of reading vernacular scripts, making services more inclusive and efficient.

By Siddharth, ago
Top-7-Customer-Onboarding-Solutions-In-India-blog-image

Top 7 Customer Onboarding Solutions In India

What Is Customer Onboarding?

Customer onboarding guides a new customer from the point of sign-up to the moment they see value in your product or service. Effective onboarding is critical in regulated sectors like banking, insurance, and fintech, including identity checks, document verification, and compliance with KYC and AML regulations.

Done well, onboarding builds trust, shortens time to value, and reduces drop-offs. Done poorly, it can cause frustration and churn before the relationship begins.

Key Points To Remember In Customer Onboarding

  • Compliance comes first – In India, customer onboarding must meet regulatory requirements like e-KYC, Video KYC, CKYC registry checks, AML, and sanctions screening.
  • Frictionless experience – Customers expect fast, digital-first experiences: pre-filled forms, mobile-friendly design, and minimal document re-submission.
  • Trust and security – Liveness detection, consent capture, and secure storage are essential to protect the business and the customer.
  • Time to value (TTV) – The sooner a customer experiences value, the more likely they are to stay. Automated workflows and guided onboarding reduce delays.
  • Analytics and tracking – Drop-off rates, completion times, and error rates must be measured to improve continually.

How To Choose Customer Onboarding Software In India

When evaluating platforms, businesses should consider the following:

  • Regulatory coverage
    Seek support for Aadhaar-based e-KYC (where applicable), PAN verification, GSTIN checks, Video KYC, and AML/sanctions screening.
  • Workflow flexibility
    Ensure the software can handle straight-through processing as well as exception handling. Project-style templates and client portals are often required.
  • Integration ecosystem
    A strong onboarding platform integrates with CRMs, core banking or insurance systems, payment gateways, and e-signing tools.
  • Scalability and security
    Cloud-native solutions with ISO or SOC certifications, data residency compliance, and strong encryption practices are critical.
  • Customer experience features
    Guided flows, multilingual support, mobile responsiveness, and automated reminders enhance adoption.
  • Commercial clarity
    Understand whether pricing is per API call, per user, or per project, and check for add-on costs like storage or premium connectors.
Talk to sales - AuthBridge

7 Best Customer Onboarding Solutions In India

Customer onboarding is no longer just a box-ticking exercise. It has become a critical differentiator for businesses in India, especially in regulated industries like banking, insurance, and fintech. Choosing the right onboarding platform can mean the difference between a seamless, compliant journey and one riddled with delays, drop-offs, and risks.

Below are seven of the best customer onboarding solutions available in India today, in no particular order:

1. AuthBridge

AuthBridge offers one of India’s most comprehensive onboarding platforms, designed to balance regulatory compliance with a smooth customer experience. The company combines digital identity verification, document management, due diligence, and automation at scale.

Key Capabilities:

  • Digital KYC & Video KYC (V-CIP):
    Real-time facial recognition, liveness detection, OCR, and geo-tagging. Video-based KYC is designed to cut turnaround times by up to 90% and reduce costs by as much as 70%.

  • AML & Risk Screening:
    Anti-Money Laundering checks, adverse media monitoring, and reputation screening through proprietary databases like Vault and Negative Image Search.

  • Third-Party Onboarding (OnboardX):
    A dedicated platform for onboarding vendors, distributors, gig workers, and other third parties with multi-channel initiation, progress monitoring, and due diligence powered by over a billion proprietary records.

  • Document Execution (SignDrive):
    Digital signing workflows that eliminate the friction of physical paperwork, with secure, auditable e-signatures.

  • Financial Data Intelligence:
    Bank Statement Analyser for automated classification of income, expenses, and potential fraud indicators, helping insurers and lenders speed up underwriting.

  • Insurance-Specific Accelerators:
    Tailored solutions for insurers, including real-time policyholder verification and Pre-Issuance Verification Calls (PIVC), with AI-led calls reducing PIVC turnaround times by up to 80%.

  • Integration & APIs:
    Plug-and-play APIs for PAN, Aadhaar DigiLocker, GSTIN and other verifications, plus integrations with HRMS, CRMs, and ERPs.

2. TrackWizz

TrackWizz focuses heavily on regulated financial sectors, offering an integrated suite for client lifecycle management.

Services Offered:

  • Central KYC (CKYC) submission and management.

  • AML and sanctions screening with transaction monitoring.

  • Automated onboarding workflows for high-net-worth and institutional clients.

  • Insider trading compliance and regulatory reporting (FATCA, CRS).

3. KYC Hub

KYC Hub is a global onboarding platform with solutions built for compliance-heavy markets, including India.

Services Offered:

  • Automated Digital KYC and Video KYC.

  • Perpetual KYC with ongoing risk assessment.

  • AML screening, fraud prevention, and dynamic risk scoring.

  • Document verification powered by AI and APIs.

  • Customisable workflows to adapt to business requirements.

4. Salesforce Financial Services Cloud

Salesforce provides a powerful onboarding module within its Financial Services Cloud, which is trusted globally and adapted for Indian institutions.

Services Offered:

  • Digital client onboarding with guided journeys.

  • Automated document collection and e-signatures.

  • CRM integration to unify customer data during onboarding.

  • Workflow automation for account origination and compliance checks.

5. Newgen Software

Newgen delivers AI-driven customer onboarding solutions designed for banks and financial institutions.

Services Offered:

  • End-to-end digital account opening (deposits and loans).

  • Video KYC for remote onboarding.

  • AI and ML-driven risk assessment for faster approvals.

  • Account maintenance automation, including re-KYC and updates.

6. OnRamp

OnRamp is built for businesses looking to provide structured and transparent onboarding experiences.

Services Offered:

  • A customer-facing portal for clear visibility of steps.

  • Internal project dashboards for teams to manage tasks and timelines.

  • Ready-to-use templates and playbooks to accelerate onboarding.

7. FlowForma

FlowForma is a no-code workflow automation tool that helps enterprises digitise their onboarding journeys.

Services Offered:

  • Customisable onboarding workflows with dynamic forms.

  • Deep integration with Microsoft 365 applications.

  • AI Copilot supports building and managing workflows.

  • Mobile-ready experiences for distributed teams.

Conclusion

For enterprises that value both compliance and customer experience, AuthBridge offers a proven, future-ready solution. Other platforms such as TrackWizz, KYC Hub, Salesforce, Newgen, OnRamp, and FlowForma also deliver strong capabilities, each excelling in specific domains. The choice ultimately depends on your industry, scale, and integration needs.

Businesses that adopt the proper solution now will win customer trust faster and build long-term resilience in an increasingly regulated market.

By Abhinandan, ago
Creadit-Risk-Assessment-blog-image

Credit Risk Assessment Using Banking, Bureau & Behavioural Signals | 2025 Guide

Introduction: Why Multi-Signal Risk Models Outperform Traditional Scoring

Credit risk assessment has long been dominated by bureau scores and repayment histories, yet these conventional approaches increasingly show their limitations in fast-moving markets. Bureau data, while valuable, often suffers from latency; a borrower’s financial position may have shifted weeks or even months before the update appears in a credit report. This lag creates blind spots that can expose lenders to unexpected defaults, especially in segments such as small businesses or first-time borrowers with thin credit files.

By contrast, multi-signal models—which integrate banking data, bureau reports, and behavioural indicators—offer a far more dynamic and predictive view of creditworthiness. Banking data provides granular insights into income patterns, cash-flow stability, and discretionary spending, while bureau data contextualises repayment discipline and exposure to other debts. Behavioural signals, such as digital repayment behaviour, device usage, and even response times to loan offers, add a further dimension of predictive power.

Evidence supports this shift. According to an Experian study (2023), lenders that adopted alternative and behavioural data in conjunction with bureau scores improved default prediction accuracy by 20–25% compared with traditional models alone. Similarly, the Reserve Bank of India’s Financial Stability Report highlights the growing role of transaction-level banking data in reducing non-performing assets, particularly among retail borrowers.

The implications are clear: financial institutions that rely solely on traditional credit scores risk under-serving good borrowers while over-exposing themselves to high-risk applicants. By combining multiple signals, lenders can not only price risk more accurately but also expand access to credit responsibly, a vital goal in economies pushing for financial inclusion.

Banking Data as a Window into Cash Flow and Liquidity

Banking Data as a Window into Cash Flow and Liquidity​

Banking data provides one of the most reliable, real-time perspectives on a borrower’s financial health. Unlike bureau scores, which are retrospective, bank statements reveal actual cash flows, income consistency, and expenditure behaviour. This makes them indispensable in assessing repayment capacity, particularly for small businesses, gig workers, or individuals with limited credit history.

The Reserve Bank of India’s Report on Trend and Progress of Banking in India (2023) highlighted that over 45% of retail borrowers in India are new-to-credit, meaning they often lack bureau records. For such borrowers, transaction-level banking data offers a substitute indicator of stability. Regular salary credits, predictable utility payments, and prudent discretionary spending are stronger predictors of repayment discipline than a mere score.

Globally, the reliance on bank data is accelerating. A World Bank survey (2023) found that 70% of financial institutions in emerging markets use some form of alternative banking data for underwriting. Similarly, PwC’s Future of Banking report suggests that lenders using automated bank statement analysers achieve up to 30% faster credit decisions, while also reducing non-performing loan ratios.

Behavioural Signals – The Emerging Frontier In Credit Risk Assessment

Beyond banking and bureau data, behavioural signals are rapidly gaining recognition as a critical layer in credit risk assessment. These signals capture how individuals interact with financial systems, digital platforms, and even loan processes, offering a real-time and context-rich view of risk. Unlike static bureau records, behavioural data reflects patterns that can change daily, making it particularly valuable for predicting short-term defaults and identifying early warning signs.

According to McKinsey’s Global Banking Review (2023), institutions that incorporated behavioural data—such as payment timeliness, transaction irregularities, and digital engagement—achieved a 35% improvement in early delinquency detection. Similarly, the OECD (2022) reported that lenders leveraging mobile and digital behavioural metrics in developing economies reduced credit losses by up to 20%, while also expanding access to first-time borrowers.

Behavioural signals include:

  • Repayment Behaviour: How promptly borrowers settle utility bills, EMIs, or digital wallet dues. Persistent delays, even in small-value payments, may indicate future default risk.
  • Platform Engagement: Frequency of logins, response times to loan offers, and digital application abandonment rates. For example, borrowers who repeatedly abandon loan applications midway may reflect indecisiveness or hidden financial stress.
  • Device & Geolocation Patterns: Consistency of device usage and transaction geography. Sudden shifts—such as a borrower’s account being accessed from unusual locations—can flag fraud or instability.

Building A Unified Credit Risk Model – Integrating Banking, Bureau and Behavioural Signals

A modern credit engine unifies three complementary data strata—banking, bureau and behavioural—inside one governed pipeline. The goal is to estimate Probability of Default (PD) accurately, tie it to Loss Given Default (LGD) and Exposure at Default (EAD), and translate the result into risk-based pricing, limits and line management, while meeting audit and regulatory expectations (e.g., IFRS 9/Ind AS 109, model risk governance, consented data use).

1) Data Ingestion & Governance

A consent-first architecture pulls:

  • Banking data (salary credits, cash-flow stability, EMI ratios, returned instruments) via secure, consented channels (e.g., Account Aggregator frameworks).

  • Bureau files (tradelines, utilisation, arrears, delinquency vintage, recent enquiries).

  • Behavioural exhaust (bill-payment punctuality, application journey patterns, device integrity, geo consistency).
     Every feed is versioned with lineage, access controls, PII minimisation and retention windows, enabling full traceability for audits and customer rights management.

2) Feature Engineering (Illustrative)

  • Banking features: income variance, net surplus/obligation ratio, EMI-to-income, bounce frequency, seasonality of inflows.

  • Bureau features: worst-ever DPD, vintage since last delinquency, utilisation buckets, enquiry intensity, obligor concentration.

  • Behavioural features: on-time payment streaks (utilities/wallets), session drop-offs, device stability score, velocity anomalies.
     Transformations include binning/WOE, outlier winsorisation, and monotonic constraints for scorecards.

3) Modelling & Calibration

Blend interpretable baselines (logistic regression/scorecards) with boosted trees (XGBoost/LightGBM) for non-linear lift. Calibrate PDs via Platt/Isotonic methods, validate with AUC/KS/GINI, and monitor PSI/CSI for population drift. Use reject inference where appropriate to debias selection effects from past policies.

4) Decisioning: From PD To Price/Limit

At decision time, combine PD with portfolio-level LGD/EAD and operating costs to set price and limits.

Formula: 12-month ECL = PD × LGD × EAD

Table A — Illustrative ECL & Risk Premium (per ₹100,000 exposure)

Borrower

PD

LGD

EAD (₹)

12-month ECL (₹)

Minimum Risk Premium to Cover ECL (bps)*

A (stable cash-flows, low utilisation)

1.5%

45%

100,000

675

67.5 bps

B (volatile inflows, high utilisation)

6.0%

45%

100,000

2,700

270 bps

*Illustrative; excludes cost of funds, OPEX, capital charge and target RoE.

Table B — Signal-To-Outcome Mapping (sample)

Signal Family

High-Impact Features

Typical Directionality

Decision Use

Banking

Net surplus, bounce count, EMI/Income

Better surplus ↓ PD; more bounces ↑ PD

Price down for strong cash-flow; tighten limits otherwise

Bureau

Utilisation, arrears vintage, enquiries

High utilisation/enquiries ↑ PD

Set conservative limits; require co-app/extra docs

Behavioural

Bill-pay punctuality, device stability

On-time streaks ↓ PD; device churn ↑ PD

Fast-track thin-file approvals; flag fraud/stability risk

5) Back-Testing, Overrides & Challenger Policy

Run out-of-time validation and cohort back-tests (by vintage, geography, segment). Define policy overrides (e.g., cap approval where utilisation>90% even if model score is good). Maintain champion–challenger strategies: the challenger model must show statistically significant uplift (e.g., KS +5 points) while holding fairness thresholds.

6) Production Monitoring & Fairness

Deploy scorecards with threshold alerts (default rate, approval rate, bad-rate by decile), bias checks (statistical parity, equal opportunity), and concept drift monitors. Maintain model cards documenting data sources, performance, stability, and known limitations. Align customer communications with explainability (reason codes) and adverse-action requirements.

The AuthBridge Advantage in Credit Risk Assessment

As credit ecosystems grow more complex, lenders need partners that can bring together trusted data, AI-powered insights, and seamless compliance frameworks. AuthBridge plays this role by delivering a unified risk infrastructure that empowers banks, NBFCs, and fintechs to adopt multi-signal credit models with speed and confidence.

1. Banking Data Insights

With tools like the Bank Statement Analyser, AuthBridge translates raw banking transactions into actionable credit signals. Salary credits, inflow stability, EMI ratios, and spending trends are extracted automatically, helping lenders move from manual assessment to real-time cash-flow based underwriting. This directly strengthens Probability of Default (PD) estimates while accelerating turnaround times.

2. Bureau And Identity Verification

AuthBridge integrates directly with credit bureau APIs and government registries to perform instant KYC, PAN, GSTIN, and CIN validation. By unifying bureau history with regulatory identity checks, the platform ensures faster, compliant, and accurate onboarding, reducing the operational friction that often slows lending workflows.

3. Behavioural And Alternative Signals

Solutions such as GroundCheck.ai and video-based KYC modules enrich credit profiles with behavioural insights. From geolocation stability and device integrity to responsiveness in consent journeys, these signals are vital for evaluating thin-file and new-to-credit borrowers. AuthBridge’s AI-driven risk engines also flag anomalies—such as forged documents or inconsistent responses—before they escalate into defaults.

4. Unified Platform and Ongoing Monitoring

Through AI Powered Platform by AuthBridge a centralized platform that ingests, analyses, and monitors risk data across the borrower lifecycle. Risk dashboards provide early alerts for lapses such as missed GST filings, litigation appearances, or expired licences. All workflows are audit-ready and aligned with ISO 27001, SOC 2, and DPDPA standards, ensuring both compliance and data security.

5. Real-World Impact

For instance, a mid-sized Indian NBFC integrated AuthBridge’s Bank Statement Analyser + Bureau APIs + Video KYC to streamline SME loan underwriting. The results were significant:

  • Loan approval cycles shrank by 30% (from five days to under 48 hours).

Approval rates for thin-file SMEs rose by 20%, driven by richer data signals.

Conclusion And Key Takeaways

Credit risk assessment is undergoing a profound transformation. Where once bureau scores dominated decision-making, today’s most resilient lenders are those who integrate banking data, bureau insights, and behavioural signals into a unified model. This multi-signal approach not only sharpens the accuracy of default prediction but also opens the door to financial inclusion for millions of new-to-credit individuals and small businesses.

Data from global benchmarks reinforces this shift. The World Bank notes that 40% of adults in emerging markets remain excluded from bureau-based systems, while PwC and Experian report that lenders using blended data models achieve 20–30% improvements in predictive power and a 25% reduction in non-performing assets. These gains are not just theoretical—they directly improve profitability, risk-adjusted returns, and customer trust.

For financial institutions, the imperative is clear:

  1. Banking data provides a live lens into liquidity and cash-flow health.

  2. Bureau data anchors long-term repayment discipline and exposure.

  3. Behavioural signals capture the short-term dynamics that reveal early stress or stability.

Together, these streams create a 360-degree view of the borrower. And with partners like AuthBridge, lenders can operationalise this approach at scale—combining AI-driven analysis, compliance-first frameworks, and seamless automation to transform credit decisioning.

In an era of rising competition and regulatory scrutiny, the ability to measure, monitor, and manage credit risk with precision is no longer optional—it is the cornerstone of sustainable growth. Institutions that embrace this multi-signal, technology-enabled paradigm will not only protect themselves from defaults but also unlock opportunities to serve broader markets responsibly.

By Siddharth, ago
56th GST Council Meeting Highlights

56th GST Council Meeting: Items That Got Cheaper, Expensive, & Exempt From GST

The 56th GST Council Meeting was held in New Delhi on 3 September 2025, chaired by the Union Minister of Finance & Corporate Affairs, Smt. Nirmala Sitharaman attended by state and union territory finance ministers. The meeting unveiled one of the most sweeping changes to India’s indirect tax framework since GST was launched in 2017.

The GST Council approved a “Simple Tax” structure comprising two main slabs — 5% (merit rate) and 18% (standard rate) — and a 40% de-merit slab for a select group of goods and services such as luxury vehicles, sin goods, and online gaming.

56th GST council meeting
Source: PIB.gov.in

The new GST rates apply from 22 September 2025 for services and most goods, with exceptions for certain tobacco products (pan masala, gutkha, cigarettes, chewing tobacco, unmanufactured tobacco and bidi), which will continue at existing rates until compensation cess loans are repaid.

Items That Got Cheaper

Dairy And Food Staples

Item

Old Rate

New Rate

Ultra High Temperature (UHT) milk

5%

Nil

Paneer/Chhena (pre-packaged)

5%

Nil

Condensed milk

12%

5%

Butter, ghee, dairy spreads

12%

5%

Cheese

12%

5%

Pizza bread, chapati, roti, khakhra

5%

Nil

Paratha, parotta and other Indian breads

18%

Nil

Fruits, Nuts And Vegetables

  • Dried nuts: Brazil nuts, almonds, hazelnuts, pistachios, macadamia, kola, pine nuts – 12% → 5%

  • Dried fruits: Dates, figs, mangoes, avocados, guavas, mangosteens – 12% → 5%

  • Dried citrus fruits: Oranges, lemons, mandarins, grapefruit, limes – 12% → 5%

  • Other dried fruits & mixtures – 12% → 5%

  • Preserved vegetables & fruits (tomatoes, mushrooms, truffles, jams, marmalades, purees, juices, sugar-preserved fruits, coconut water) – 12% → 5%

Cereals, Confectionery And Bakery

Item

Old Rate

New Rate

Malt

18%

5%

Starches & inulin

12%

5%

Pasta, noodles, macaroni, couscous

12%

5%

Breakfast cereals, flakes

18%

5%

Bakery goods (biscuits, cakes, pastries)

12%/18%

5%

Namkeens, mixtures, bhujia

12%

5%

Diabetic foods

12%

5%

Sugar, syrups, confectionery

12–18%

5%

Cocoa products, chocolates

18%

5%

Beverages

  • Drinking water in 20-litre jars – 12% → 5%

  • Plant-based milk beverages – 18% → 5%

  • Soya milk drinks – 12% → 5%

  • Fruit-pulp/juice drinks – 12% → 5%

  • Beverages containing milk – 12% → 5%

  • Ice cream and edible ice – 18% → 5%

Tea, Coffee And Condiments

  • Coffee extracts, concentrates – 18% → 5%

  • Tea extracts – 18% → 5%

  • Roasted chicory – 12% → 5%

  • Yeasts, baking powders – 12% → 5%

  • Sauces, condiments, curry pastes, salad dressings – 12% → 5%

  • Soups and broths – 18% → 5%

Personal Care And Household

  • Hair oil, shampoo – 18% → 5%

  • Toothpaste, toothbrushes, dental floss – 18% → 5%

  • Tooth powder – 12% → 5%

  • Talcum/face powder – 18% → 5%

  • Toilet soap bars – 18% → 5%

  • Candles, safety matches – 12% → 5%

  • Feeding bottles, nipples – 12% → 5%

  • Baby diapers & liners – 12% → 5%

  • Kitchenware/tableware (steel, aluminium, copper, ceramics, wood) – 12% → 5%

  • Umbrellas, sewing machines & needles – 12% → 5%

  • Bicycles & parts – 12% → 5%

  • Bamboo/cane furniture – 12% → 5%

  • Hurricane & kerosene lamps – 12% → 5%

Education And Stationery

  • Erasers – 5% → Nil

  • Pencils, crayons, chalks – 12% → Nil

  • Pencil sharpeners – 12% → Nil

  • Exercise, lab, graph notebooks – 12% → Nil

  • Uncoated paper for notebooks – 12% → Nil

  • Maps, globes – 12% → Nil

  • Stationery sets, cartons, pulp trays, biodegradable bags – 12% → 5%

  • Wood pulps – 12% → 5%

Textiles And Apparel

  • Man-made fibres – 18% → 5%

  • Man-made yarn – 12% → 5%

  • Technical textiles, non-wovens, coated fabrics – 12% → 5%

  • Carpets & floor coverings – 12% → 5%

  • Quilted textile products ≤ ₹2,500 – 12% → 5%

Agriculture And Fertilisers

  • Micronutrients under FCO – 12% → 5%

  • Gibberellic acid – 12% → 5%

  • Bio-pesticides (Trichoderma, Pseudomonas, NPV, Neem, Cymbopogon) – 12% → 5%

  • Sulphuric acid, nitric acid, ammonia – 18% → 5%

  • Diesel engines ≤15HP, hand pumps – 12% → 5%

  • Hydraulic pumps for tractors – 18% → 5%

  • Agricultural machinery (soil prep, harvesting, threshing, balers, mowers, composters) – 12% → 5%

Energy And Appliances

  • Solar cookers, solar heaters, PV cells, wind & waste-to-energy devices – 12% → 5%

  • Air-conditioners, dishwashers – 28% → 18%

  • Televisions, monitors, projectors, set-top boxes – 28% → 18%

  • Composting machines – 12% → 5%

  • Walkie-talkies for defence/police – 12% → 5%

Automobiles And Transport

  • Small cars (≤1200cc petrol, ≤1500cc diesel, ≤4000mm length) – 28% → 18%

  • Hybrids within small-car specs – 28% → 18%

  • Motorcycles ≤350cc – 28% → 18%

  • Three-wheelers, goods vehicles, ambulances – 28% → 18%

  • Auto seats, auto parts – 28%/varied → 18% uniform

  • Tractor parts, tyres – 18% → 5%

  • Bicycles & parts – 12% → 5%

Health And Medical

  • 33 life-saving drugs – 12% → Nil

  • 3 critical drugs – 5% → Nil

  • All other drugs – 12% → 5%

  • Medical devices (surgical, dental, veterinary, diagnostic) – 18% → 5%

  • Diagnostic kits & reagents – 12% → 5%

  • Medical oxygen, hydrogen peroxide (medicinal) – 12% → 5%

  • Antisera, glands, biologics – 12% → 5%

  • Rubber gloves – 12% → 5%

  • Spectacles, frames, lenses – 12% → 5%

Construction And Raw Materials

  • Cement – 28% → 18%

  • Marble, granite blocks – 12% → 5%

Handicrafts, Leather, Wood, Coir

  • Handicrafts of wood, stone, glass, metal, ceramic, toys, statues – 12% → 5%

  • Leather intermediates – 12% → 5%

  • Wood & cork articles – 12% → 5%

  • Coir products (excl. mattresses) – 12% → 5%

  • Live horses – 12% → 5%

Defence

  • Tanks & armoured fighting vehicles – 12% → 5%

Items That Get Costlier

Beverages

  • Carbonated fruit drinks – 28% → 40%

  • Caffeinated beverages – 28% → 40%

  • Aerated waters with sugar – 28% → 40%

  • Other non-alcoholic beverages – 18% → 40%

Tobacco Products

  • Cigars, cigarettes, cigarillos – 28% → 40%

  • Other manufactured tobacco products – 28% → 40%

  • Unmanufactured tobacco (other than leaves) – 28% → 40%

  • Nicotine inhalation products – 28% → 40%

  • Bidis – 28% → 18%

  • Bidi wrapper leaves, Indian katha – 18% → 5%

Automobiles & Luxury

  • Large cars, SUVs (beyond small-car specs) – 28% → 40%

  • Hybrids above small-car specs – 28% → 40%

  • Motorcycles >350cc – 28% → 40%

  • Yachts & pleasure craft – 28% → 40%

  • Aircraft for personal use – 28% → 40%

Energy & Resources

  • Coal, lignite, peat – 5% → 18%

Paper & Textiles

  • Certain kraft/coated papers, pulps – 12% → 18%

  • Apparel & made-ups >₹2,500 – 12% → 18%

  • Quilts >₹2,500 – 12% → 18%

Menthol

  • Non-natural menthol & derivatives – 12% → 18%

Services

  • Air travel (non-economy) – 12% → 18%

  • Passenger transport by motor vehicle (ITC option) – 12% → 18%

  • Goods transport agency (ITC option) – 12% → 18%

  • Pipeline transport (crude, gas, fuels) – 12% → 18%

  • Renting motor vehicles (with operator, ITC option) – 12% → 18%

  • Works contracts (≥75% earthwork for Govt) – 12% → 18%

  • Sub-contracts for above works – 12% → 18%

  • Offshore oil & gas works – 12% → 18%

  • Casinos, race clubs, betting, online gaming – 28% → 40%

Items Exempt From GST

Food & Education

  • UHT milk, paneer, roti, parotta, pizza bread – Nil

  • Erasers, pencils, crayons, sharpeners, notebooks, school paper, maps, globes – Nil

Health & Insurance

  • 33 life-saving drugs – Nil

  • 3 critical medicines – Nil

  • All individual life insurance policies & reinsurance – Exempt

  • All individual health insurance policies & reinsurance – Exempt

Defence & Imports

  • Specified defence imports (aircraft, drones, simulators, missiles, sonobuoys, batteries, spares) – Nil

  • Dutiable articles for personal use – 28% → 18%

  • Drugs & medicines for personal use – 12% → 5%

Services That Got Cheaper

  • Hotel accommodation ≤ ₹7,500/night – 12% → 5% (without ITC)

  • Beauty & wellness (gyms, salons, barbers, yoga) – 18% → 5% (without ITC)

  • Job work (textiles, apparel, footwear, pharma, leather, umbrellas, bricks, printing) – 12% → 5%

  • Goods transport by rail/containers – 12% → 5%

  • Leasing of railway rolling stock – 12% → 5%

  • Renting of goods carriages – 12% → 5%

  • Third-party insurance of goods carriages – 12% → 5%

  • CETP (effluent treatment) – 12% → 5%

  • CBWTF (biomedical waste) – 12% → 5%

  • Cinema tickets ≤ ₹100 – 12% → 5%

Compliance And Trade Facilitation

  • RSP valuation for pan masala & tobacco.

  • GSTAT: appeals by Sept 2025, hearings by Dec 2025, backlog cut-off June 2026.

  • Refunds: 90% provisional refunds for inverted duty; export refund thresholds removed.

  • Registration: simplified for low-risk applicants & ECO suppliers.

  • Place of supply: intermediary services shifted to the recipient’s location.

  • Post-sale discounts: clarified with ITC reversal by recipients.

  • Restaurants: standalone can’t opt for 18% with ITC.

  • E-commerce local deliveries: notified under Section 9(5).

Conclusion

This is the full authoritative reference to the 56th GST Council meeting decisions. It captures every rate change (upward, downward, or Nil), plus compliance reforms. For consumers, it means cheaper essentials, health, education and mobility. For businesses, it brings clarity, faster refunds and easier compliance. For the exchequer, higher rates on luxury and sin goods strike a balance with public health and fiscal needs.

By Abhinandan, ago

Stay Informed

Keep yourself updated with the latest innovations in BGV & Authentication Technology from India’s leading Background Verification Company

Sign up for our newsletter

Solutions

White-collar Verification
Blue-collar Verification
KYC Solutions
AML Solution
Digital Address Verification
Risk Management
Digital Underwriting
All Solutions →

Checks

Employment Verification
Education Verification
Address Verification
Pan Card Verification
Udyog Aadhaar Verification
GST Verification

Products

iBRIDGE
OnboardX
TruthScreen
SignDrive
AuthLead
WorkAttest
Vault
CorpVeda
View All Products →

Resources

Blogs
Customer Stories
Whitepapers

View all Resources →

Company

About Us

Careers
Newsroom

Investor Relations

Help Center

For Clients
For Candidates
Youtube Facebook Linkedin

AuthBridge is the #1 Authentication Company.
Copyright 2025 AuthBridge, All Rights Reserved.

Request A Demo
Youtube Facebook Linkedin

Hi! Let’s Schedule Your Call.

To begin, Tell us a bit about “yourself”

The most noteworthy aspects of our collaboration has been the ability to seamlessly onboard partners from all corners of India, for which our TAT has been reduced from multiple weeks to a few hours now.

- Mr. Satyasiva Sundar Ruutray
Vice President, F&A Commercial,
Greenlam

Thank You

We have sent your download in your email.

Case Study Download

Want to Verify More Tin Numbers?

Want to Verify More Pan Numbers?

Want to Verify More UAN Numbers?

Want to Verify More Pan Dob ?

Want to Verify More Aadhar Numbers?

Want to Check More Udyam Registration/Reference Numbers?

Want to Verify More GST Numbers?