bfsi

RBI FREE-AI Guidelines

RBI’s FREE-AI Framework: Key Highlights Summarised

RBI’s Push For Responsible AI In Financial Services

The Reserve Bank of India has released its Framework for Responsible and Ethical Enablement of AI (FREE-AI) at a time when the financial sector is moving rapidly from experimental deployments to mainstream adoption of artificial intelligence. For banks, insurers and non-banking financial companies, they now know that AI can no longer remain an ancillary tool. It is now central to the way institutions assess credit, monitor risks, and engage with customers, and it must be governed accordingly.

The framework lays down guiding principles and operational expectations that marry innovation with prudence. It acknowledges the efficiency and inclusion gains AI can unlock, while making clear that opacity, bias, and weak oversight could destabilise financial markets and corrode public trust. The RBI’s emphasis on board-level responsibility, structured model governance, and mandatory transparency obligations signals a regulatory shift, from permitting fragmented experimentation to demanding institution-wide accountability.

For the BFSI leadership, this is not merely a compliance update. It is a strategic inflexion point. Institutions that can integrate AI responsibly, embedding explainability, fairness and resilience into their models, stand to capture competitive advantage. Those who cannot may find themselves facing heightened supervisory scrutiny, reputational damage, and an erosion of customer confidence.

Opportunities Of AI In BFSI

For India’s financial sector, the RBI report is less about unveiling new possibilities and more about lending institutional weight to changes already underway. Artificial intelligence is no longer a speculative tool; it is shaping the way balance sheets are built, risks are priced, and customers are retained. The numbers are eye-catching; global estimates place potential banking productivity gains in the range of $200–340 billion a year, but the more telling developments are visible on the ground.

Take credit underwriting. Traditional scorecards that relied on income proofs and bureau history are being supplemented with data trails from GST filings, telecom usage, and even e-commerce behaviour. This is not simply innovation for its own sake. For lenders battling high acquisition costs and thin margins, alternate credit models mean access to new segments without compromising prudence. The inclusion dividend, bringing thin-file borrowers into the fold, is a by-product, though one with profound consequences for financial deepening.

Fraud detection is another front where AI is moving the needle. Global banks that have invested in AI-led validation tools report material reductions in false positives and payment rejections. In India, where digital transactions run into billions each month, even a modest improvement in accuracy translates into meaningful savings and, more importantly, sustained trust in digital channels.

Customer engagement is evolving as well. Multilingual voice bots, embedded in UPI or account aggregator frameworks, are starting to blur the lines between technology and financial literacy. The promise here is not just cost reduction through automation, but the creation of service models that feel accessible to a farmer in Vidarbha or a shopkeeper in Guwahati, clients who have historically been underserved by the formal system.

The report also nods to a larger structural opportunity: the alignment of AI with India’s digital public infrastructure. If Aadhaar and UPI represented the pipes of a new financial order, AI could well become the pressure valve, enabling real-time risk scoring, personalised nudges, and context-aware service delivery. For institutions, this is not a question of whether AI will matter, but how quickly they can adapt it to their existing frameworks without eroding safeguards.

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Risks And Challenges Of AI Highlighted By RBI

If the opportunity side of AI feels expansive, the risks outlined by the RBI are equally sobering. The report makes it clear that unchecked adoption could destabilise both firms and markets. This is not rhetorical caution; the vulnerabilities are real and already visible.

The first is model risk. AI systems often behave like black boxes, powerful in prediction, opaque in logic. A credit model that misclassifies a borrower, or a fraud system that repeatedly flags genuine payments, is not merely a technical glitch. It can mean reputational damage, regulatory penalties, and erosion of customer confidence. The RBI rightly notes that bias in training data or poorly calibrated algorithms can hard-wire discrimination into financial processes.

Operational risks follow close behind. AI reduces human error in many processes, but it also amplifies the cost of mistakes when they occur at scale. A single point of failure in a real-time payments environment could cascade through millions of transactions. Market stability itself is not immune: history remembers the “flash crash” of 2010, and algorithmic misfires in a more AI-saturated environment could prove even more destabilising.

Third-party dependency adds another layer. Most Indian banks and NBFCs lean heavily on external vendors for AI models, cloud services, and integration layers. That concentration risk leaves institutions exposed to interruptions, contractual blind spots, and even geopolitical vulnerabilities. The report is blunt on this: outsourcing AI without iron-clad governance is an open invitation to risk.

Cybersecurity risks are no less pressing. AI is a double-edged sword here: it strengthens defence, but it also lowers the cost and sophistication threshold for attackers. Deepfake fraud, AI-engineered phishing, and data-poisoning attacks are already hitting financial institutions globally. For a sector built on trust, the reputational consequences of one high-profile breach could be devastating.

And then there is the risk of inertia. The RBI points out that institutions which resist AI adoption may find themselves doubly vulnerable, unable to counter AI-driven fraud and left behind by more agile competitors. In a sector where margins are tightening, standing still is itself a risk strategy.

The FREE-AI Framework Explained

The RBI’s Committee has attempted something unusual in Indian regulatory practice: to codify a philosophy for AI adoption rather than issue narrow compliance checklists. The FREE-AI framework — short for Framework for Responsible and Ethical Enablement of AI — is built around seven “Sutras” and six strategic pillars. Taken together, they are intended to guide how regulated entities design, deploy and govern artificial intelligence.

At the heart of the framework lie the Seven Sutras — principles that set the moral and operational compass:

  • Trust is the foundation. AI systems must inspire confidence not only in their outcomes but also in their process.

  • People first. Human oversight and consumer interest cannot be sacrificed at the altar of efficiency.

  • Innovation over restraint. The regulator signals it does not want to stifle progress, provided safeguards are in place.

  • Fairness and equity. Models must avoid systemic bias that could exclude vulnerable groups.

  • Accountability. Responsibility must sit with identifiable decision-makers, not be diffused into algorithms.

  • Understandable by design. Black-box systems that cannot be explained will not withstand scrutiny.

  • Safety, resilience and sustainability. AI must be stress-tested for shocks, cyber threats and long-term viability.

To move these ideals into practice, the report maps them against six strategic pillars. Three are enablers of innovation, infrastructure, policy, and capacity, and three are risk mitigators, governance, protection, and assurance. Under these sit 26 specific recommendations: from the creation of shared infrastructure and financial-sector sandboxes to board-approved AI policies, mandatory audits, and consumer disclosure requirements.

What is notable is the tone of the framework. It does not treat risk controls as an afterthought but places them on equal footing with innovation. A tolerant approach is suggested for low-risk AI use cases, particularly those that advance financial inclusion, but higher-stakes deployments will be subject to tighter scrutiny. 

AI Adoption And Use Cases: What RBI’s Surveys Show

The RBI conducted two surveys in 2025 — one by the Department of Supervision covering 612 regulated entities and another by the FinTech Department covering 76 institutions with 55 CTO/CDO follow-ups. Together, they capture nearly 90% of the sector’s assets, making them a credible reflection of the state of play.

Adoption Levels

  • Overall adoption is thin: only 20.80% (127 of 612) entities reported using or building AI solutions.

  • Banks: larger commercial banks are more active, but adoption still centres on limited functions.

  • NBFCs: 27% of 171 surveyed have live or developing use cases.

  • Urban Co-operative Banks (UCBs): Tier-1 UCBs — none; Tier-2 and Tier-3 report usage in single digits.

  • ARCs: none reported adoption.

This confirms that AI penetration is still largely confined to bigger balance sheets with stronger tech capabilities.

Complexity Of Models

Most reported applications use rule-based systems or moderate machine learning models. More advanced architectures, deep learning, neural networks, or generative stacks, are rare in production. The comfort zone remains models that can be explained and slotted into legacy IT frameworks without destabilising compliance.

Infrastructure Choices

  • 35% of entities using AI host models on public cloud.

  • The balance prefers private cloud, hybrid, or on-premise deployments, reflecting ongoing caution around data control, privacy, and outsourcing risks.

Use Cases (583 Applications Reported)

The RBI categorised 583 distinct applications across the surveyed entities:

  • Customer support – 15.60%

  • Credit underwriting – 13.70%

  • Sales and marketing – 11.80%

  • Cybersecurity and fraud detection – 10.60%

  • Other emerging use cases – internal administration, coding assistants, HR workflows, and compliance automation are rising but not yet mainstream.

This distribution illustrates a preference for low-to-medium risk operational functions rather than core balance-sheet exposures.

Generative AI

Interest in generative AI is widespread but tentative. In the FinTech Department’s sample of 76, 67% of institutions said they were exploring at least one generative use case. Yet these were overwhelmingly internal pilots: knowledge assistants, report drafting, code generation. Customer-facing deployments remain scarce due to unease about data sensitivity, unpredictable outputs, and the absence of clear explainability mechanisms.

Governance And Control Mechanisms

Perhaps the most telling findings relate to safeguards. Adoption often happens without adequate governance:

  • Interpretability tools (e.g., SHAP, LIME): only 15% reported use.

  • Audit logs: 18%.

  • Bias and fairness validation: 35%, and mostly pre-deployment rather than continuous.

  • Human-in-the-loop oversight: 28%.

  • Bias mitigation protocols: 10%.

  • Periodic audits: 14%.

  • Model retraining: 37%, but ad hoc in many cases.

  • Drift monitoring: 21%.

  • Real-time performance monitoring: 14%.

Reading The Numbers

The survey findings point to a sector that is experimenting but not yet institutionalising AI. Adoption is selective, shallow, and uneven across segments. The concentration of activity in larger banks and NBFCs highlights both the opportunity and the risk: systemic players are experimenting at scale without consistent controls, while smaller institutions risk being left behind entirely.

Inclusion, Digital Public Infrastructure And Sector-Specific Models

The report is unequivocal about AI’s role in widening formal finance without diluting prudence. It points to alternate data—utility payments, mobile usage patterns, GST filings and e-commerce behaviour—as credible signals for underwriting thin-file or new-to-credit borrowers, particularly MSMEs and first-time users. This is not an argument for laxity; it is an argument for better signals, especially where bureau history is sparse.

Inclusion, however, is not only about scorecards. The report emphasises multilingual access and low-friction channels that meet users where they are. AI-powered chatbots for guidance and grievance redress, and voice-enabled banking in regional languages for the illiterate or semi-literate, are explicitly flagged as near-term, high-impact levers. The intent is straightforward: reduce the cognitive and linguistic barriers that keep millions from using formal services confidently.

A second plank is the convergence with Digital Public Infrastructure (DPI). India’s rails—Aadhaar, UPI and the Account Aggregator framework—are treated as the substrate on which AI can enable personalisation and real-time decisioning at a population scale. The report is explicit: conversational AI embedded into UPI, KYC strengthened through AI in tandem with Aadhaar, and context-aware service via Account Aggregator are practical upgrades, not distant aspirations. To avoid concentration advantages, the report also moots AI models offered as public goods so that smaller and regional players can participate meaningfully.

On the modelling side, the committee pushes beyond generic LLM enthusiasm and asks a pointed question: Should India develop indigenous, sector-specific foundation models for finance? The rationale is not industrial policy for its own sake; it is risk and fit. A model that does not reflect India’s linguistic and operational diversity risks urban-centric bias and poor performance in real-world Indian contexts. General-purpose models, trained largely on English and Western corpora, will not reliably handle India’s multilingual and domain-specific needs.

Accordingly, the report outlines two practical directions. First, Small Language Models (SLMs): narrow, task-bound models that are faster to train, cheaper to run, and easier to govern, particularly when fine-tuned from open-weight bases for specific financial tasks. Second, “Trinity” models built on Language-Task-Domain combinations—e.g., Marathi + Credit-risk FAQs + MSME finance, or Hindi + Regulatory summarisation + Rural microcredit—to ensure regulatory alignment, multilingual inclusion, and operational relevance while keeping compute budgets realistic. The report notes these systems can be built quickly with moderate resources—a pragmatic route for Indian institutions.

Finally, the report widens the lens to the near-horizon. Autonomous agent patterns (using protocols like MCP and agent-to-agent messaging) could shift finance from task automation to decision automation—for instance, an SME’s agent negotiating with multiple lender-agents for real-time offers and execution. The paper also flags privacy-enhancing technologies and federated learning for collaborative training without raw-data exchange—important for inclusion use cases where data fragmentation and privacy risks otherwise stall progress. 

Barriers And Governance Gaps

The surveys surface a consistent set of impediments that explain why adoption is shallow outside a handful of large institutions. Chief among them are the talent gap, high implementation costs, patchy access to quality training data, limited computing capacity, and legal uncertainty. Smaller players, already stretched on capex and compliance, asked for low-cost, secure environments to experiment before committing to production.

Beyond economics, the risk picture is clear. Institutions flagged data privacy, cybersecurity, governance shortcomings, and reputational exposure as the principal concerns. Many remain wary of pushing advanced models into live workflows because of opacity and unpredictability—and the governance demands that follow. The implication is obvious: the more consequential the decision (credit, fraud, claims), the higher the bar for control and audit.

On internal readiness, the gap is structural. Only about one-third of respondents—mostly large public-sector and private banks—reported any Board-level framework for AI oversight. Only about one-fourth said they have formal processes to mitigate AI-related incidents. In many institutions, AI risks are loosely folded into generic product approval routines rather than being managed through a dedicated risk vertical. Training and staff awareness are thin, limiting the organisation’s ability to handle evolving risks.

Data governance is fragmented. Most entities lack a dedicated policy for training AI models. Key lifecycle functions—data sourcing, preprocessing, bias detection and mitigation, privacy, storage and security—are scattered across IT and cybersecurity policies. Data lineage and traceability systems, essential for accountability and reliable models, are missing in many legacy estates. Access to domain-specific, high-quality structured data remains a persistent pain point.

Even where AI is in use, safeguards are uneven. Of the 127 adopters, only 15% reported using interpretability tools; 18% maintain audit logs; 35% perform bias/fairness validation, mostly at build-time rather than in production. Human-in-the-loop is present in 28%, but bias-mitigation protocols sit at 10%, and regular audits at 14%. Periodic retraining is reported by 37%, drift monitoring by 21%, and real-time performance monitoring by just 14%—figures that underscore why supervisors are pressing for stronger model lifecycle controls.

Capacity building is patchy. A few institutions have launched training programmes, industry partnerships and centres of excellence, but talent remains scarce and efforts are fragmented. Respondents also emphasised the need to raise customer awareness so that AI-enabled services are better understood and trusted at the front line.

Finally, the demand from the industry is explicit: 85% of deep-dive respondents asked for a formal regulatory framework, with guidance on privacy, algorithmic transparency, bias mitigation, use of external LLMs, cross-border data flows, and a proportional, risk-based approach that allows safe innovation while tightening controls where stakes are high. 

Regulatory Trajectory: Proportionality, Outsourcing, Consumer Disclosures

RBI’s stance remains technology-agnostic but expects AI to be governed within the existing lattice of IT, cyber, digital lending and outsourcing rules, with incremental AI-specific clarifications layered on top where needed.

Proportionality (what to expect): the Committee signals a consolidated issuance to stitch AI-specific expectations—disclosures, vendor due diligence on AI risks, and cyber safeguards—into current regulations, rather than creating a separate AI rulebook.

Outsourcing (clarity on scope):

  • If an RE embeds a third-party AI model inside its own process, treat it as internal use—the RE’s standard governance and risk controls apply.

  • If the RE outsources a service and the vendor uses AI to deliver it, that is outsourcing; contracts should explicitly cover AI-specific governance, risk mitigation, accountability and data confidentiality, including subcontractors.

Consumer protection (minimums): customers should know when they are dealing with AI, have a means to challenge AI-led outcomes, and access robust grievance redress. These expectations flow from existing consumer circulars and are to be read as applicable to AI.

Digital lending (auditability): AI-based credit assessments must be auditable, not black boxes; data collection must be minimal and consent-bound, including for DLAs/LSPs.

Cyber/IT (extend controls to AI): apply access control, audit trails, vulnerability assessment and monitoring to AI stacks, mindful of data poisoning and adversarial attacks.

In short: expect a risk-based consolidation of AI expectations across the existing rule set, explicit outsourcing language for vendor-delivered AI services, plain-English disclosures to customers, and auditable model decisions for high-stakes use cases.

Operational Safeguards: Policy, Monitoring, And Incident Reporting

RBI’s framework expects AI to be governed as a first-class risk. That means formal policy, live monitoring, clear fallbacks, and an incident regime that can withstand supervisory scrutiny.

Board-Approved AI Policy. Institutions should maintain a single, actionable policy that: inventories AI use cases and risk-tiers them; fixes roles and accountability up to Board/committee level; codifies the model lifecycle (design, data sourcing, validation, approval, change control, retirement); sets minimum documentation standards; and defines training for senior management through to frontline teams. The policy should also spell out third-party controls (due diligence, SLAs, subcontractor visibility, right to audit) and the cadence for periodic review.

Data And Documentation. Keep an auditable trail of what went into and came out of each model: data sources and legal basis (consent/minimisation), preprocessing steps, versioned training sets, feature lineage, hyperparameters, and inference-time logs where feasible. Retention should align with existing data and consumer regulations.

Pre-Deployment Testing. High-impact models should face structured validation: representativeness checks on datasets; back-testing and challenger comparisons; fairness/bias testing on protected cohorts; stability tests across segments and time; and adverse scenario tests (including attacks such as prompt injection, data poisoning, adversarial inputs, inversion/distillation where relevant). Approval gates and sign-offs should be recorded.

Production Monitoring. Treat AI as “always in observation”:

  • Performance and error-rate tracking with thresholds for alerts and human review.

  • Drift detection on data and outcomes; defined triggers for retraining or rollback.

  • Continuous fairness checks where decisions affect customer access, pricing, or claims.

  • Access controls, audit trails and tamper-evident logs for models and data.

  • Change management for any update to data, code, thresholds, or prompts—including roll-back plans.

Human-In-The-Loop And Explainability. For high-stakes calls (credit, claims, fraud flags, adverse onboarding outcomes), ensure a human override path and an explanation that can be shown to customers and auditors. Record when and why overrides occur.

Business Continuity For AI. Define safe-fail modes: a kill-switch, degraded service (e.g., revert to prior approved model or rules), and manual operations where required. Map these to specific processes (payments, lending, onboarding) so continuity steps are executable under time pressure.

Vendor Oversight (When AI Is In The Service Chain). Contracts should name AI-specific obligations: model governance standards, data segregation and confidentiality, geo/sovereignty constraints, transparency on sub-processors, audit rights, security posture, and incident notification timelines with evidence packs. Where a third-party model is embedded inside your own process, apply your internal controls as if it were built in-house.

Customer Safeguards. Provide plain-English disclosure when an interaction or decision is AI-enabled, outline how customers can contest outcomes, and route challenges to trained staff. Keep redress timelines and decision records auditable.

Incident Reporting (Annexure Lens). Prepare to log and report AI incidents using a consistent template. At minimum capture: use case and model details; trigger and time of detection; impacted customers/systems/financials; severity; root cause; immediate containment; longer-term remediation and prevention; and named contacts. Link incident thresholds to your monitoring triggers and BCP so escalation is automatic rather than ad hoc.

Enablers: Innovation Sandbox And Sector Collaboration

The report does not view responsible AI as a compliance burden alone; it proposes concrete enablers to help institutions adopt safely and at speed.

AI Innovation Sandbox. A supervised, time-bound environment where banks, NBFCs and fintech partners can test AI use cases with real-world constraints and clear guardrails. The intent is to de-risk early pilots, surface model and data issues before scale, and document learnings in a format that can be audited and reused.

Shared Infrastructure And Public Goods. Sector access to curated datasets, evaluation suites, and compute on fair terms—especially for smaller and regional players. The emphasis is on domain-relevant benchmarks (credit, fraud, AML, KYC) and lightweight, explainable models that can run economically and be governed by existing risk functions.

Sector-Specific Models And Tooling. Practical focus on small language models and narrow task models tuned to Indian finance (languages, products, processes). Tooling includes bias and drift tests, red-team playbooks for adversarial inputs, and out-of-the-box explainers suitable for customer-facing decisions.

Standard Templates And Policy Kits. Model cards, data lineage registers, change-control logs, and incident report formats that align with supervisory expectations. These reduce time to compliance and create comparable evidence across institutions.

Capacity And Knowledge-Sharing. Board and senior management briefings, communities of practice for CRO/CTO teams, and joint exercises on model failures and recovery. The goal is consistent judgement across firms on when to escalate, when to roll back, and how to evidence decisions.

Vendor And Outsourcing Hygiene. Clearer procurement language for AI components—governance standards, transparency on sub-processors, audit rights, geo/sovereignty constraints, and incident-notification obligations—so external capabilities can be used without importing opaque risks.

Alignment With National AI Safety Efforts. Testing, assurance, and benchmarking to be interoperable with the emerging national safety and standards ecosystem, so results from one setting can inform supervisory reviews across the sector.

How AuthBridge Helps BFSI Align With FREE-AI

RBI’s framework sets clear expectations: evidence, accountability, explainability, and recoverability. AuthBridge’s stack lines up well against that bar, helping institutions shift from pilots to governed production without losing speed.

What The Framework Expects vs What You Can Operationalise With AuthBridge

FREE-AI Expectation

What BFSI Needs In Practice

How AuthBridge Helps

Clear governance and auditability

A single source of truth for AI/KYC decisions; model/use-case inventory; change logs; evidence on tap for internal audit and supervisory review

Board-ready policy and register templates; decision records with time-stamped artefacts; exportable audit packs across KYC, onboarding and screening flows

Explainable outcomes for high-stakes calls

Human-review paths, reasons you can show a customer or examiner, and an override trail

Decision explainers for onboarding flags, AML hits and risk scores; maker-checker workflows; override capture with rationale

Data minimisation and consent

Verifiable consent, least-data processing, and traceable lineage from source to decision

Consent capture embedded in Video-KYC and digital forms; field-level lineage and retention controls aligned to your policy

Continuous monitoring and bias/drift checks

Live quality gates, alerting, retraining triggers, and back-testing

Performance dashboards, drift alerts, threshold tuning; challenger vs champion comparisons where applicable

Resilience and safe-fail

Fallbacks when models or sources misbehave; continuity during outages

Kill-switch to revert to approved rulesets; degraded modes and manual paths for onboarding and verification

Outsourcing hygiene

Contracts that name AI obligations; visibility into sub-processors; audit rights

Standard clauses, evidence packs, and vendor reporting formats that match RBI’s emphasis on accountability

Consumer safeguards

Disclosure when AI is in play; channels to contest outcomes; fast redress

Plain-English notices in flows; case escalation to trained reviewers; decision journals to support responses

Conclusion

The RBI’s FREE-AI framework marks a decisive shift in how artificial intelligence will be viewed in Indian finance: not as an optional add-on but as a regulated capability that demands the same rigour as credit, capital or liquidity management. For BFSI institutions, the task is twofold—embrace the efficiency and reach AI enables, while embedding the safeguards that preserve trust and systemic stability. Those that move early will not only stay compliant but will also earn the confidence of customers and regulators alike. With AuthBridge’s AI-driven verification, diligence and compliance solutions, the sector can operationalise these expectations today—turning regulatory alignment into a competitive advantage.

By Abhinandan, ago
AI in Bank Statement Analyser

The Impact Of AI In Bank Statement Analysis

The Importance Of Bank Statement Analysis

Have you wondered how important your Bank Statement can be? You can learn a lot about someone/a company by looking at their bank statement. It doesn’t just show how much they earn or what they spend, it quietly reveals patterns of trustworthiness, financial strain, lifestyle choices, and even integrity.

For lenders, insurers, gig platforms, and credit underwriting teams, this document has become one of the most valuable pieces of critical decision-making.

But here’s the problem. No two bank statements look the same. Some are downloaded as polished PDFs. Others arrive as scans, screenshots, or even photos taken in a hurry. They’re filled with acronyms, bank codes, fee entries, bounced transactions, and sometimes, clever manipulation. Reviewing these manually is tedious and inconsistent. And it breaks under pressure when you’re trying to process hundreds or thousands of applications a day.

This is where Artificial Intelligence (AI) has quietly made an impact like never before.

AI can read any format, in any layout, and turn it into clean, structured data. But more importantly, it makes sense of that data. It finds anomalies that a human might miss. It learns over time and spots signs of tampering, synthetic salaries, or income that doesn’t match the furnished information.

And it does all this in seconds.

If your business depends on knowing who to trust, whether you’re lending ₹10,000 or over ₹10 crore, then understanding how AI handles bank statement analysis is indispensable.

How AI Understands Bank Statements Like A Risk Analyst Would

A bank statement, when read correctly, is not just a ledger of deposits and withdrawals. It is a behavioural data set that shows financial discipline, income reliability, exposure to debt, and potential red flags. For decades, skilled underwriters have relied on their intuition to extract these insights. The challenge now is to do it at scale, without compromising judgment and accurate decision making.

Artificial Intelligence enables precisely that, by replicating how experienced analysts read statements.

The first layer of interpretation begins with data structuring. AI uses computer vision and contextual learning to convert unstructured statements into standardised tables, regardless of format or source. But beyond parsing, the important bit lies in identifying what the numbers mean.

AI models trained on financial behaviour can:

  • Identify whether an inflow is salary, a loan, or a one-time deposit.

  • Map EMI deductions to outstanding liabilities.

  • Quantify net monthly surplus or deficit.

  • Detect anomalies such as sudden spikes in income, altered balances, or round-tripped transactions.

It does this not by keyword detection, but by assessing transaction frequency, narrative context, metadata, and long-term balance trends. Income validation, bounce history, recurring obligations, and financial stress indicators can all be extracted within seconds, without requiring human intervention.

What makes this useful is not just accuracy, but consistency. Every profile is assessed using the same logic, removing subjectivity and reducing error rates. This standardisation becomes crucial for lenders, especially in unsecured credit, where traditional credit scores fall short.

The strength of AI is not that it reads faster, but that it reads comprehensively. It ensures that every entry is considered, every inconsistency is flagged, and every applicant is assessed based on actual financial behaviour.

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Where AI-Based Bank Statement Analysis Delivers The Most Impact

AI in bank statement analysis solves core business problems that financial institutions have struggled with for years. These include delayed decisions, operational bottlenecks, poor visibility into risk, and exposure to manipulated data.

The impact is the highest in cases where accuracy, speed, and scale are extremely important.

1. Lending and Credit Risk Assessment

For lenders, particularly those dealing in unsecured or short-term credit, there is a non-negotiable need for high reliability of stated income and repayment behaviour. AI enables lenders to check not just credit scores, but also get access to more nuanced, real-time insights from transactional behaviour.

A few key benefits:

  • Income classification: AI identifies regular salary credits, freelance income, or inconsistent gig payments across banks and formats.

  • EMI tracking: Ongoing loan commitments, including informal borrowings, are mapped against net disposable income.

  • Bounce and penalty detection: AI highlights dishonoured cheques or insufficient balance incidents, often missed in manual reviews.

  • Cash flow profiling: Monthly surplus, deficit, and balance trends are charted to evaluate repayment capacity more reliably than stated income.

2. Fraud Detection and Document Forensics

Tampering with bank statements is a common problem, particularly in areas where PDF uploads are accepted without source verification. AI-led systems are trained to detect:

  • Inconsistent fonts, spacing, or layout shifts that point to edits

  • Metadata mismatches or file generation anomalies

  • Repeated transaction IDs or misaligned account balance flows

Not only does AI highlight document-level manipulation, it also detects synthetic behaviour patterns, like inflated one-time credits to fake a high income or backdated entries to mimic salary history. This layer of intelligence allows fraud teams to act earlier, with stronger audit trails and fewer false positives.

3. Gig Economy and Blue-Collar Underwriting

In segments like logistics, delivery, and home services, traditional documents like Form 16 or credit bureau scores don’t exist or are outdated. Bank statements become the only reliable source of verification.

AI systems trained on these patterns can:

  • Read salary-like credits from platforms such as Swiggy, Zomato, or Ola

  • Assess income regularity even in cash-heavy or high-churn environments

  • Create risk bands based on observed transactional hygiene, not just KYC data

This expands the pool of underwritable applicants and supports financial inclusion at scale, without compromising on risk visibility.

4. SME and Self-Employed Profiles

For small business owners or self-employed individuals, balance sheets are often unavailable or unaudited. Here, AI-analysed bank statements function as cash flow statements, providing insights into:

  • Revenue streams

  • Seasonal income fluctuations

  • Vendor payments

  • Tax payments and GST-related outflows

This is especially valuable for NBFCs and digital lenders operating in Tier 2 and 3 cities, where documentation is limited, and credit demand is high.

Advantages Of AI Bank Statement Analyser

In lending, risk management, and compliance, time and accuracy are everything. For decades, financial institutions have relied on manual processes to sift through bank statements, identify risks, and make key decisions. The problem, however, is that this method doesn’t scale, and it misses valuable data that could be used to make more informed, faster decisions.

This is where AI comes in handy.

Speed and Scalability Without Sacrificing Quality

As businesses scale, so do the demands on their underwriting teams. Processing bank statements manually can be time-consuming, often requiring multiple staff members to cross-check the same information. AI removes these bottlenecks. It can process thousands of bank statements at once, maintaining accuracy and consistency in every document.

This level of efficiency means faster decision-making, which is crucial when dealing with high volumes, such as during loan approvals, credit risk assessments, or compliance verifications. What might have taken hours with a manual team can now be achieved in minutes, without compromising on quality.

Improved Accuracy and Reduced Human Error

The complexity and variability of bank statements can make them prone to human error. Whether it’s an overlooked transaction, an incorrectly flagged anomaly, or an unreadable entry, these mistakes can lead to significant issues down the line.

AI in bank statement analysis mitigates these risks by being objectively consistent. It processes every statement using the same parameters, applying rigorous algorithms to detect inconsistencies, potential fraud, or unusual patterns that might otherwise be missed. For financial institutions, this reduces risk by increasing the accuracy of each analysis, which is particularly crucial when evaluating creditworthiness or assessing exposure.

Enhanced Risk Detection and Fraud Prevention

In today’s fast-moving digital landscape, fraud is evolving rapidly. Manipulated bank statements are one of the most common methods of fraud, especially when it comes to synthetic identities or artificially inflated incomes.

AI detects these discrepancies by analysing every aspect of the statement, from the metadata and formatting of the document to the transactional patterns. The ability to spot discrepancies, even subtle ones, ensures early detection of fraud before it escalates. This is invaluable in a landscape where preventing fraud before it happens is far more cost-effective than trying to recover losses afterwards.

Building Smarter, More Inclusive Credit Models

AI doesn’t just assess risk based on traditional financial indicators, such as credit scores or reported income. It also considers behavioural signals, such as spending patterns, cash flow cycles, and payment history, to build a more nuanced understanding of an individual’s or business’s financial health. This is particularly beneficial for underserved segments, such as gig workers or small businesses, who may not have access to traditional forms of credit reporting.

By incorporating these behavioural insights, AI enables businesses to make better, more informed lending decisions, even for individuals without a traditional credit history.

Get Faster, Smarter, and Accurate AI-powered Bank Statement Analysis With AuthBridge

The benefits of AI in bank statement analysis are undeniable. From improving decision-making speed to enhancing accuracy and detecting fraud, it’s clear that this technology is transforming how financial institutions handle large volumes of complex documents. However, the real challenge lies in implementing this technology effectively and ensuring it integrates seamlessly into your existing workflows.

At AuthBridge, we’ve developed a powerful AI-driven solution that takes the complexity out of bank statement analysis. Our tool parses data from the documents, providing financial institutions with deep insights and actionable data. Our solution enables smarter, faster decisions that drive business growth while reducing operational costs.

Key Features of AuthBridge’s Bank Statement Analyser:

  • High Accuracy & Precision: We ensure that every detail of a bank statement is captured and analysed correctly, eliminating human error and improving data integrity.

  • Customisable & Scalable: Whether you’re handling 10 statements or 10,000, our solution scales effortlessly, offering custom configurations to fit your unique business requirements.

  • Fraud Detection: Detects inconsistencies, metadata mismatches, and suspicious patterns that indicate potential fraud, all while improving operational efficiency.

  • Integration-Ready: Easily integrates with your existing systems to streamline operations, from loan approvals to compliance checks, without disrupting your current workflow.

Conclusion

AI has already begun revolutionising bank statement analysis. What once was a manual, slow, and error-prone process is now a fast, accurate, and automated decision-making tool that businesses and financial institutions can rely on. The next step is to integrate this technology into your operations, and AuthBridge’s Bank Statement Analyser is the ideal solution to help you do just that.

By Abhinandan, ago
CPV in Banking

Importance Of Contact Point Verification (CPV) In Banking

Introduction To Contact Point Verification In Banking

Contact Point Verification (CPV) is a key step in banking operations, focused on confirming that the communication channels provided by customers. This includes checking for active and authentic mobile numbers, email addresses or postal addresses. By validating these contact points, banks make sure that important alerts such as transaction notifications, OTPs for Internet banking and statements reach the right recipient without delay or interception.

A strong CPV process strengthens security across multiple touchpoints. For Internet-banking log-ins and fund transfers, an OTP sent to a verified number or e-mail ensures that only the genuine customer can approve high-value transactions. In customer onboarding, instant confirmation of email addresses prevents mistyped or fraudulent entries from entering the system. Even routine communications, like sending monthly statements or promotional offers, benefit from CPV. Banks avoid the costs and reputational risks of bounced emails or messages sent to inactive numbers.

Moreover, CPV contributes to operational efficiency. Automated checks, such as carrier lookups to verify number status or SMTP pings to test e-mail server availability, can be completed in minutes. This significantly reduces manual follow-up. When automated channels fail, voice-call or letter-dispatch methods ensure no customer is left unverified. This multi-channel approach enhances the customer experience by minimising onboarding friction. It also reduces the resource burden on call centres and branch staff.

Core Methods And Best Practices For CPV In Banking

In banking, Contact Point Verification relies on a multi-channel strategy to ensure that customer communication details are both valid and in active use. Automated mechanisms, such as carrier lookups and SMTP handshakes, quickly filter out invalid entries. One-time passwords (OTPs) sent via SMS or e-mail provide a near-instant confirmation of possession. While interactive voice response (IVR) calls serve as a secondary digital protection. Where digital channels fail, a manual agent call or postal confirmation letter bridges the gap, ensuring that even customers in low-connectivity regions can complete verification.

A hallmark of an effective CPV programme is its fallback logic: if an SMS OTP isn’t delivered, the system should automatically trigger an IVR prompt or e-mail link without manual intervention. This continuity reduces customer effort and cuts down support overhead. Moreover, all verification attempts and outcomes should be logged in real time to create an audit trail capable of withstanding regulatory scrutiny and forensic review.

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Banks aiming for excellence in CPV adopt several best practices:

  • Time-Bound Automated Checks: Carrier and SMTP checks are executed within seconds, flagging invalid entries before consuming OTP resources.

  • Dynamic Fallback Rules: The system should escalate only once per failed channel, e.g., one SMS attempt, one IVR attempt, then route persistent failures to a human agent for resolution.

  • Consent Management: Before dispatching any OTP or call, explicit customer consent must be captured and stored by data protection regulations.

  • Periodic Re-Verification: High-risk or dormant accounts should undergo CPV at defined intervals, typically every 12–24 months, to ensure contact information remains current.

Method

Check Performed

Data Captured

Carrier Lookup

Is the mobile number active and valid?

Live/deactivated status, network operator

SMTP Handshake

Does the e-mail server accept incoming connections?

Bounce responses, server latency

SMS OTP

Does the user receive and submit the code correctly?

OTP send time, validation success/failure

IVR Prompt

Does the automated call connect and confirm user?

Call logs, DTMF or voice confirmation result

Manual Agent Call

Can a human agent reach and verify the contact?

Agent notes, final disposition

Postal Letter Dispatch

Does physical mail reach the stated address?

Delivery confirmation or returned mail flag

Regulatory Framework And RBI Guidelines For CPV

The Reserve Bank of India embeds Contact Point Verification into its KYC and CDD norms across these key scenarios:

  • Periodic KYC Updation: When a customer updates only their postal address, the new address must be verified through positive confirmation within two months, by means such as an address-verification letter, contact point verification, deliverables, etc.

  • Sole Proprietorship Documentary Exception: If a sole proprietor cannot furnish two activity-proof documents, the bank may accept one, but only after it undertakes contact point verification … to establish the existence of such firm and satisfy itself that the business activity has been verified from the address of the proprietary concern.

  • Enhanced Due Diligence for Remote Onboarding: Before allowing operations in a non-face-to-face account, banks must confirm the customer’s current address via positive confirmation methods, with CPV listed alongside letters and other deliverables. 

Practical Use Cases And Benefits Of CPV In Banking

Contact Point Verification delivers multiple advantages across a wide range of banking operations, enhancing security, efficiency and compliance.

1. Secure Onboarding and Account Activation

When a new customer applies for a savings or current account, whether in branch or via digital channels, CPV prevents fraudulent or erroneous enrolments. By confirming mobile numbers and e-mail addresses in real time, banks ensure that onboarding credentials (such as Internet-banking log-ins or debit-card PINs) reach bona fide applicants only. This not only reduces the incidence of “dead” or fraudulent accounts but also diminishes manual rework.

2. Safe Transaction Authorisations

High-value fund transfers and bill payments depend on one-time passwords delivered to verified channels. CPV underpins transaction security by ensuring that OTPs cannot be intercepted via stale or spoofed numbers. 

3. Dormancy Reactivation and Periodic Re-Verification

Many customers fall into dormancy, typically after 12 – 24 months of inactivity, raising the risk of unauthorised reactivation. CPV applied at the point of dormancy reactivation (sending OTPs or verification calls) confirms that contact details remain under the customer’s control. 

4. Regulatory Audit and Compliance Reporting

CPV generates a rich audit trail: every carrier-lookup response, OTP dispatch, IVR call log and agent-confirmation note is timestamped and stored. This comprehensive record helps banks demonstrate compliance with KYC Directions and Data Protection norms during inspections. 

Conclusion

In a nutshell, Contact Point Verification is what keeps banking both safe and straightforward: by quickly checking that your phone number, email or address is yours, whether through a simple OTP, a quick automated call or a brief manual check, banks stop fraudsters in their tracks, avoid endless back-and-forth during sign-up, and stay on the right side of RBI rules. It’s a small step that makes a big difference, building customer trust and setting the stage for banking that’s as seamless as it is secure.

By Abhinandan, ago
aml-inbanking-blog-image

AML In Banking: Trends, Challenges And The Road Ahead

Introduction

Money laundering remains one of the most pressing threats to the global financial ecosystem. As illicit funds flow through legitimate financial institutions, banks increasingly find themselves on the front lines of the battle against financial crime. According to the United Nations Office on Drugs and Crime (UNODC), between 2% and 5% of global GDP, roughly $800 billion to $2 trillion laundered every year. These staggering figures underscore the critical role of Anti-Money Laundering (AML) efforts in the banking sector.

AML in banking refers to a suite of laws, policies, technologies, and internal practices designed to detect, prevent, and report suspicious financial activity. With digital banking and cross-border transactions on the rise, traditional methods of AML enforcement are proving insufficient. In response, financial institutions are turning to advanced analytics, artificial intelligence (AI), and regulatory technology (RegTech) to stay ahead of evolving threats.

The need for robust AML frameworks has never been more urgent. Global watchdogs such as the Financial Action Task Force (FATF) and national regulators are intensifying scrutiny, issuing heavy penalties for non-compliance. In 2022 alone, financial institutions across the globe faced over $5 billion in AML-related fines, highlighting the real financial and reputational risks involved.

The Evolution Of AML In Banking

Anti-Money Laundering regulations have evolved significantly over the past few decades, transitioning from basic record-keeping requirements to sophisticated risk-based frameworks integrated with cutting-edge technology. In India, the evolution of AML practices can be traced back to the enactment of the Prevention of Money Laundering Act (PMLA) in 2002. This legislation laid the groundwork for modern AML protocols, empowering regulatory bodies to tackle financial crimes more proactively.

The Reserve Bank of India (RBI) further strengthened compliance by issuing guidelines for banks and financial institutions to implement robust Know Your Customer (KYC) procedures. Over time, these mandates expanded to include transaction monitoring, suspicious activity reporting (SAR), and the creation of internal AML cells within banks. The RBI’s push towards digitisation has only accelerated this evolution.

Globally, AML enforcement gained momentum with the establishment of the FATF in 1989, followed by widespread adoption of its recommendations. In India, FATF’s mutual evaluations have driven the banking sector to align closely with global standards. The introduction of the Financial Intelligence Unit – India (FIU-IND) has also been pivotal in enabling the collection and analysis of financial data related to money laundering.

With the advent of fintech and increasing reliance on digital payment systems such as UPI, NEFT, and mobile wallets, the complexity of financial ecosystems in India has deepened. This shift has led to a new era of AML, where banks are no longer simply watchdogs—they are data-driven sentinels relying on real-time surveillance, behaviour analytics, and machine learning models to detect financial crime.

Key Challenges In AML For Banks

  • High Transaction Volumes:
    Banks must monitor millions of transactions daily, making it difficult to detect suspicious patterns in real time.

  • False Positives in Monitoring:
    Rule-based systems often generate excessive alerts, most of which are false positives—wasting time and resources on manual reviews.

  • Fragmented Data Systems:
    Customer and transaction data are often siloed across departments, preventing a unified risk view and effective monitoring.

  • Evolving Laundering Techniques:
    Criminals exploit cryptocurrencies, shell companies, and complex layering methods that traditional AML systems struggle to track.

  • Balancing Compliance and Customer Experience:
    Banks must enforce strong AML measures without creating friction for legitimate customers expecting fast and seamless service.

Regulatory Expectations And Compliance Frameworks In 2025

As financial crime grows more complex, regulatory authorities worldwide are stepping up expectations from banks to ensure robust AML compliance. The focus has shifted from mere policy adherence to demonstrable, outcome-based risk management.

Below are the key regulatory expectations shaping the AML landscape in 2025:

  • Risk-Based Approach (RBA):
    Regulators now demand that AML programmes be tailored to the specific risk exposure of a financial institution. This includes customer risk profiling, transaction risk scoring, and sectoral risk evaluation. One-size-fits-all compliance is no longer acceptable.

  • Enhanced Due Diligence (EDD):
    Institutions are expected to conduct EDD for high-risk customers such as politically exposed persons (PEPs), offshore entities, and businesses operating in high-risk jurisdictions. This involves collecting more detailed documentation and ongoing monitoring of account activity.

  • Real-Time Transaction Monitoring:
    Regulatory bodies are emphasising the need for continuous, real-time transaction monitoring using AI-powered systems, rather than relying solely on post-facto reviews. This ensures timely reporting of suspicious activities.

  • Robust Record-Keeping & Audit Trails:
    Financial institutions must maintain digital audit trails and comprehensive records of all customer interactions, transactions, and compliance reviews for a minimum of five years, as per FATF and local jurisdictional standards.

  • Integrated KYC-AML Compliance:
    Regulators are pushing for tighter integration between Know Your Customer (KYC) and AML functions. KYC data should feed directly into AML decision-making systems to enable more accurate risk assessments and fraud detection.

  • Automated Suspicious Activity Reporting (SAR):
    Compliance teams must implement automated SAR generation and filing mechanisms that align with local formats (e.g., STRs in India). Delays or manual handling of such reports could result in hefty penalties.

  • Third-Party & Vendor Risk Management:
    AML regulations now extend to third-party due diligence, requiring financial institutions to assess the risk profiles of vendors and partners, especially in outsourcing arrangements for KYC, collections, or onboarding.

  • Cross-Border Compliance Alignment:
    For banks operating in multiple geographies, there is a growing need to harmonise their AML processes with both local and international regulatory frameworks (e.g., EU’s AMLD6, USA’s Bank Secrecy Act, India’s PMLA).

These frameworks are not just compliance mandates—they reflect a broader shift towards accountability, transparency, and proactive financial crime prevention.

Future Trends In AML For Banks

As financial crime continues to evolve, AML strategies must advance in parallel. The future of Anti-Money Laundering in banking will be defined by agility, automation, and intelligence. Financial institutions are no longer reactive entities; they are expected to predict and pre-empt risks before they escalate. Below are the key trends poised to shape AML practices in the years ahead:

  • Agentic AI and Autonomous Compliance Systems
    Agentic AI, which enables systems to act independently to complete tasks, is set to redefine AML operations. From initiating verification checks to closing compliance loops, autonomous agents will minimise human intervention while accelerating resolution times and boosting accuracy.

  • Holistic Identity Resolution
    AML efforts will increasingly depend on unified identity frameworks that consolidate data from multiple sources—HRMS, onboarding platforms, digital IDS, and external databases—into a single, verifiable customer profile. This helps in identifying risk at both the individual and network levels.

  • Behavioural Biometrics and Advanced Risk Scoring
    Financial institutions will begin leveraging behavioural analytics, such as typing patterns, device usage, and navigation behaviour, to build predictive risk scores. These scores will complement traditional KYC data to uncover anomalies early in the transaction lifecycle.

  • Global Data Collaboration and Utility Models
    To combat transnational money laundering, regulators and banks will embrace collaborative platforms and shared intelligence frameworks. The adoption of KYC utilities, centralised AML databases, and real-time information exchange will gain momentum.

  • RegTech-Driven AML Orchestration
    Regulatory Technology (RegTech) will enable end-to-end orchestration of AML compliance—right from data capture and screening to real-time reporting and audit readiness. API-first, cloud-native platforms will become the gold standard in compliance infrastructure.

  • Sustainability-Linked AML Risk Assessments
    ESG (Environmental, Social and Governance) considerations are beginning to influence AML strategy. Banks will start integrating ESG risk factors into AML assessments, particularly for industries linked to environmental crime, human trafficking, or corruption.

  • Zero-Trust Architecture for AML Systems
    With increasing cybersecurity threats, AML platforms will be built using zero-trust principles—ensuring every access point, user, and dataset is authenticated, authorised, and monitored at all times.

These trends collectively point to a future where AML is intelligent, automated, and deeply integrated into every layer of banking infrastructure. For banks willing to adapt, the opportunity lies not just in compliance—but in gaining a strategic edge.

Conclusion

Anti-Money Laundering is no longer just a regulatory obligation—it is a cornerstone of institutional integrity and risk management. In an age of real-time transactions, global digital banking, and sophisticated criminal networks, AML must evolve from reactive compliance to proactive defence.

Banks today are faced with an unprecedented dual challenge: safeguarding against financial crime while ensuring seamless customer experiences. The only viable path forward is through innovation—leveraging AI, automation, and integrated compliance frameworks that offer both agility and accountability.

Regulatory expectations will continue to rise, and penalties for non-compliance will grow increasingly severe. But for banks that choose to invest in modern, data-driven AML systems, the benefits go beyond regulatory safety. They gain reputational trust, operational efficiency, and the ability to stay one step ahead in a constantly shifting financial landscape.

By Siddharth, ago
Banking Amendment Laws 2025

Banking Laws (Amendment) Act, 2025: All Key Highlights

On 15th April 2025, the Banking Laws (Amendment) Act, 2025 received the assent of the President, marking a watershed moment in India’s banking history. This amendment significantly changes several foundational banking statutes, including the Reserve Bank of India Act, 1934, the Banking Regulation Act, 1949, the State Bank of India Act, 1955, and the Banking Companies (Acquisition and Transfer of Undertakings) Acts of 1970 and 1980.

The amendments are part of an ongoing effort to streamline and modernise the regulatory framework governing India’s banking sector. The changes address a range of issues, from the handling of unclaimed deposits to the governance of banking institutions, aiming to enhance operational efficiency, transparency, and regulatory oversight.

These revisions come at a time when India’s banking sector is undergoing digital transformation, and the need for updated and stronger laws has never been greater. As the economy becomes more digitally connected, ensuring that banking laws adapt to meet new challenges is crucial for maintaining stability and fostering growth.

Key Highlights Of The Banking Laws (Amendment) Act, 2025

The Banking Laws (Amendment) Act, 2025, brings forward several significant amendments aimed at refining and modernising India’s banking landscape. The changes affect various critical acts, including the Reserve Bank of India Act, 1934, the Banking Regulation Act, 1949, the State Bank of India Act, 1955, and the Banking Companies (Acquisition and Transfer of Undertakings) Acts of 1970 and 1980. Below is an overview of the amendments.

1. Amendment to the Reserve Bank of India Act, 1934

  • Fortnight Definition:
    • The definition of “fortnight” has been updated to mean the period from the 1st to the 15th day of each calendar month, or from the 16th to the last day of the month. This clarification will standardise timelines for operational activities, enhancing consistency across financial operations.
  • Operational Timelines:
    • The amendment replaces the term “alternate Friday” with “last day of each fortnight”, streamlining how banking operations are scheduled. This update also changes the previous reference to “seven days” for operational timelines, reducing it to “five days” for certain compliance activities, improving operational efficiency.

2. Amendment to the Banking Regulation Act, 1949

  • Minimum Capital Requirement:
    • The minimum capital required for certain banking activities has been increased significantly from five lakhs of rupees to two crore rupees or an amount notified by the Central Government in the Official Gazette.
  • Directorial Tenure in Cooperative Banks:
    • The amendment revises the tenure for directors of cooperative banks. Directors can now serve up to ten years, extending the previous limit of eight years. This is aimed at fostering stability in management at cooperative banks.
  • Nomination Changes:
    • Multiple Nominees:
      • The Act now allows up to four nominees to be nominated for a single account or deposit. If more than one nominee is chosen, the proportion of the share for each nominee must be specified.
      • In the event of a nominee’s death, the nomination for that individual becomes invalid, and the remaining shares will be redistributed according to the remaining valid nominees.
    • Successive and Simultaneous Nominations:
      • The Act distinguishes between successive and simultaneous nominations.
      • Successive nominations will take effect in a specified order, starting with the first nominee. If the first nominee is no longer available, the next in line will take precedence, and so on.
      • Simultaneous nominations require that the proportionate share of the amount be stated explicitly. Each of the nominees’ shares will be paid out in the proportions specified by the account holder.
    • If the account holder does not specify proportions, the nomination will be rendered invalid.
    • Nomination for Locker Holders:
      • When it comes to lockers, the Act now allows up to four nominees for a single locker. The proportion of access to the locker’s contents can be specified for each nominee. In case the locker holder dies, the nominees will gain access according to the order of priority.

3. Amendment to the State Bank of India Act, 1955

  • Unclaimed Funds and Dividends:
    • In line with the reforms, the State Bank of India Act, 1955 requires that unclaimed dividends, unpaid money, and unclaimed shares be transferred to the Investor Education and Protection Fund (IEPF) after seven years.
    • This ensures better accountability and ensures that dormant funds are handled in a transparent manner. Shareholders can claim their unpaid dividends or funds from the IEPF.
  • Auditor Remuneration:
    • The Act has been amended to align with the Companies Act, 2013, with the State Bank now required to fix auditor remuneration according to the guidelines of the modern regulatory framework.

4. Amendment to the Banking Companies (Acquisition and Transfer of Undertakings) Act, 1970 and 1980

  • Unclaimed Funds:
    • Similar to the provisions in the State Bank of India Act, unclaimed funds from acquired banks will now be transferred to the Investor Education and Protection Fund after seven years.
  • Simplified Dividend Procedures:
    • Unpaid dividends, shares, and other forms of unpaid money must be transferred to the IEPF, ensuring that dormant assets are properly managed and that no assets remain unaccounted for.

5. Nomination and Inheritance Changes

  • Multiple Nominees (Up to Four):
    • A critical change introduced is the maximum number of nominees allowed. The law now permits the nomination of up to four individuals, either successively or simultaneously.
    • For successive nominations, the order of priority must be clear. The first nominee will be given precedence, followed by the second nominee if the first one passes away, and so on.
    • For simultaneous nominations, the proportions of the total amount each nominee is entitled to must be clearly stated. If this proportion is not specified, the nomination will be considered invalid.
  • Locker Nomination Provisions:
    • In the case of locker holders, a depositor can nominate up to four individuals. The proportion of the locker’s contents assigned to each nominee must be stated explicitly. If a nominee passes away before accessing the locker, the rights to that portion will lapse, and the remaining nominees will take precedence.
    • The nomination rules for lockers mirror those for deposits, ensuring clarity in the event of the locker holder’s death.
  • Changes to Nomination Inheritance:
    • In case of multiple nominees, the priority follows a clear order of succession:
      • The first nominee’s right is activated if they survive the account holder(s).
      • If the first nominee passes away, the second nominee’s rights will come into play, followed by the third, and so on. This systematic order eliminates confusion over the rights of the nominees and ensures clarity regarding the inheritance of banking assets.

6. Other Key Amendments

  • Operational Days and Terms:
    • The amendment also introduces changes in operational days: references to alternate Fridays have been replaced with the last day of the fortnight, ensuring consistency in banking practices.
  • Cooperative Bank Management:
    • The amendment permits directors of central cooperative banks to be elected to the boards of state cooperative banks where they are members, enhancing governance and cooperation between institutions.
  • Simplification of Procedures:
    • There are several provisions aimed at simplifying operational and procedural requirements for banks, particularly in relation to unclaimed funds and handling shares, ensuring smoother transactions and compliance with modern financial regulations.

When Will The New Banking Law Amendments Come Into Effect?

The Banking Laws (Amendment) Act, 2025, is set to be implemented in phases. While the Act received Presidential assent on 15th April 2025, its provisions will come into force on a date to be notified by the Central Government.

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As stated in the Act, different provisions of the amendment will come into force on different dates. This means that while some provisions will take effect immediately, others may be implemented over time, based on the requirements and readiness of the regulatory authorities, financial institutions, and businesses involved.

It is important to note that once the provisions come into force, any reference in the Act to its commencement will refer to the specific dates when each provision is activated.

What Does This Mean for Banks and Consumers?

For banks, the implementation of the Act will require them to update their operational procedures to reflect the changes in nomination rules, fund management, and governance structures. Banks will need to ensure that their systems and customer interactions align with the new provisions, such as the acceptance of multiple nominees and the transfer of unclaimed funds to the Investor Education and Protection Fund (IEPF).

For consumers, this phased implementation means they will need to stay informed about the changes, especially regarding nominee designations, unclaimed funds, and any updates to their banking accounts or lockers. Consumers should expect communication from their banks regarding these changes and may be required to update their account details to comply with the new rules.

The Central Government will issue a notification in the Official Gazette specifying the exact dates for the commencement of these provisions. Once the notifications are issued, the banking sector will be fully equipped to implement the changes as per the new legal framework.

To ensure you’re fully prepared for these changes, it’s crucial to:

  • Review your banking accounts: Check the nomination details, ensure you have named sufficient nominees, and update your personal information if needed.

  • Stay informed: Keep an eye out for notifications from your bank regarding implementation dates and necessary actions on your part.

  • Engage with your bank: If you have any questions about how the amendments will affect your accounts, do not hesitate to reach out to your financial institution for clarity.

Conclusion

The Banking Laws (Amendment) Act, 2025, is a clear sign that India’s banking sector is evolving to meet modern challenges and global standards. By understanding and adapting to these new laws, you can ensure that your financial dealings remain secure, efficient, and compliant.

By Abhinandan, ago
KYC-spam-blog-image

How Do KYC Frauds Happen? Tips To Prevent Getting Scammed

Recent Cases Of KYC Frauds In India

With India getting increasingly digital, KYC (Know Your Customer) scams have seen a significant uptick, with fraudsters increasingly targeting individuals through never-before-seen tactics. These scams not only damage your financial security but also put your identity at risk. In recent months, numerous cases have surfaced in which victims lost significant amounts of money due to these fraudulent activities.

In one such recent case, a woman in Delhi lost ₹47 lakh after falling victim to a KYC scam via a WhatsApp call. The scammer posed as a bank official, convincing the woman to provide personal information under the guise of completing a mandatory KYC update. Unfortunately, these scams often go unnoticed until it’s too late.

Another incident reported the tragic loss of a retired teacher’s life savings due to a similar cyber fraud. The fraudster impersonated a bank representative, claiming that the teacher’s account would be suspended unless immediate KYC verification was carried out. Similarly, a techie working with one of India’s leading Government organisations lost ₹13 lakh after updating his KYC for a bank through a fraudulent link. 

How Do KYC Scams Happen?

KYC (Know Your Customer) scams are frauds where scammers exploit the identity verification process to steal personal information or money. These scams have become increasingly sophisticated, leveraging technology and psychological tactics to deceive victims.​

1. Phishing and Social Engineering

Scammers often impersonate bank representatives or government officials, contacting individuals via phone, email, or SMS. They create a sense of urgency, claiming that the victim’s account will be suspended unless immediate KYC verification is completed. To resolve the issue, victims are asked to provide personal details or click on malicious links, leading to fake websites designed to harvest information. 

2. Fake Websites and Clone Pages

Fraudsters create fake websites that closely resemble official bank or financial institution pages. Unsuspecting individuals may land on these sites through deceptive links and are prompted to enter sensitive information. Once submitted, the data is collected by the scammers for malicious use. 

3. Impersonation and Fake Documentation

Scammers may use stolen or fabricated identification documents to create fake accounts. This type of KYC fraud is prevalent in digital platforms, where identity verification may not involve physical presence. The impersonation of official entities, such as the Telecom Regulatory Authority of India (TRAI), has also been reported, with fraudsters making fraudulent calls to citizens, threatening mobile number disconnection unless personal information is provided.

4. AI-Driven Deepfake Scams

With advancements in technology, scammers are now employing AI-driven deepfake techniques to mimic the voices and appearances of trusted individuals. This technology is used to create convincing fraudulent communications, making it harder for victims to distinguish between genuine and fake interactions. Nowadays, scammers are leveraging AI to execute sophisticated schemes, including deepfake technology and spoofing, leading to major financial losses. 

5. Fake KYC Requests via Communication Platforms

Scammers exploit communication platforms like WhatsApp to send fake KYC requests. They may pose as bank officials or government representatives, asking individuals to update their KYC details through links provided in the messages. These links usually ask you to download some malicious files, which can then be used by scammers to retrieve all your personal information.

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Tips To Prevent Getting Scammed By KYC Frauds

1. Verify All Communication Through Official Channels

Scammers often initiate contact by calling or messaging individuals pretending to be from a bank or government agency. It’s essential to verify the authenticity of these communications before sharing any personal information.

  • What you should do: If you receive an unsolicited message or phone call requesting your KYC details, always independently verify by contacting the institution directly using official contact details available on their website or from your official statements.
  • How to contact: Visit your bank’s website or use the contact number found on official documents to confirm if the communication was legitimate.

2. Use Aadhaar-Based eKYC and Official Tools

The Indian government has implemented several secure digital identity verification tools, such as Aadhaar eKYC and Digilocker, for secure document sharing and identity verification. These methods are safe and reliable ways to carry out KYC without exposing personal data to potential fraudsters.

  • What you should do: If you’re asked to update your KYC, opt for Aadhaar-based eKYC or use the Digilocker service to share documents. Always ensure that you’re using official government portals.

3. Enable Two-Factor Authentication (2FA) Everywhere

Two-factor authentication provides an additional layer of protection by requiring a second form of identity verification when logging into an account, such as a one-time password (OTP).

  • What you should do: Enable 2FA on all bank accounts and financial services to protect your accounts from being accessed by unauthorized parties. Most financial institutions support 2FA for login and transaction confirmation.

4. Monitor Your Financial Accounts Regularly

Keeping track of your financial transactions is one of the most effective ways to detect suspicious activity early.

  • What you should do: Set up real-time alerts for any transactions made on your accounts. Review your monthly statements and account activities for any discrepancies. If you notice unfamiliar transactions, report them immediately.

5. Report Suspicious Activities and Communication Immediately

If you receive any suspicious communication or believe you’ve been targeted by a scam, prompt action can help minimise potential damage. Reporting such activities to the relevant authorities ensures they can investigate and prevent future fraud.

  • What you should do: Use the National Cyber Crime Reporting Portal (https://cybercrime.gov.in/) or call the Cyber Crime Helpline (1930) to report any suspicious activities. 

6. Be Cautious Of Phishing Links

Phishing attacks often trick individuals into visiting fraudulent websites that mimic official bank portals. These websites attempt to steal personal data, including login credentials and KYC information.

  • What you should do: Never click on links from unsolicited emails or messages asking you to update your KYC. Always manually type the web address into your browser or use official mobile banking apps for updates.

7. Use Secure Connections And Verified Websites

Always ensure that you are using a secure internet connection when submitting personal or sensitive information. Look for the “https://” and a padlock symbol in your browser’s address bar to ensure you’re on a secure, encrypted website.

  • What you should do: Before entering personal data, double-check the URL and ensure it is the official site of the institution. Avoid entering any personal information on public Wi-Fi or unsecured networks.

8. Educate Family And Friends On KYC Scams

Many victims of KYC scams are unaware of how such frauds operate, especially vulnerable groups like elderly individuals. Spreading awareness among friends and family can reduce the risk of them falling victim to scams.

  • What you should do: Educate family members, particularly senior citizens, about the signs of fraudulent KYC scams. Encourage them to report any suspicious activity to their bank and authorities immediately.

9. Install Antivirus Software And Keep Devices Updated

Keeping your devices secure is fundamental to avoiding malware and phishing scams. Fraudsters use infected devices to steal personal data, so protecting your smartphone or computer is vital.

  • What you should do: Install reputable antivirus software on your devices and ensure they are updated regularly. Check for software updates for your operating system, as these often patch security vulnerabilities that scammers can exploit.

10. Understand the Legal Steps for Reporting Fraud

If you fall victim to KYC fraud or encounter suspicious activity, knowing the proper legal steps to take is essential. The Indian government has dedicated resources for reporting fraud, and quick action can help you recover losses and prevent further damage.

  • What you should do:
    • Report incidents through the Cyber Crime Reporting Portal or call the Cyber Crime Helpline (1930) for immediate assistance.
    • Use the Chakshu Facility on the Sanchar Saathi Portal to report fraudulent calls and messages related to telecom services.
    • File a complaint directly with your bank’s fraud department if your account has been compromised.

Conclusion

KYC scams are increasingly sophisticated, but you can protect your personal and financial information with the right precautions. Always verify the authenticity of unsolicited communications, use official channels for updating KYC, and enable two-factor authentication for added security. Regularly monitor your accounts for any suspicious activity, and report anything unusual promptly.

By Abhinandan, ago
Alternative Investment Platforms

Enhanced Due Diligence For Alternative Investment Platforms

Understanding The Needs Of Alternative Investment Platforms

In India, the alternative investment sector is fast growing, with investors looking for diverse and often high-risk, high-return investment opportunities. Whether they focus on real estate, P2P lending, or structured debt products, these companies operate in an environment that requires constant vigilance and stringent regulatory compliance. The regulatory environment is becoming more complex, with increased emphasis on transparency, risk management, and operational efficiency.

For such companies, ensuring a strong compliance framework, validating the credibility of partners and clients, and reducing exposure to fraud and other financial risks are essential. This is where a trusted partner like AuthBridge, India’s leading provider of background verification (BGV) and due diligence services, can make a significant difference. AuthBridge’s services help mitigate the risk inherent in the alternative investment sector by providing comprehensive verification solutions tailored to their unique needs.

Importance Of Thorough Due Diligence In Alternative Investments

Firms investing in high-stakes opportunities often face the risk that the companies they back could run into trouble down the line, potentially defaulting or encountering financial distress. This is why a thorough due diligence process is so important, especially when it comes to onboarding new investors or entering into partnerships with companies where the stakes are high.

Alternative Investment Funds (AIFs) often take on complex, high-risk ventures. Many of the firms in which AIFs invest might not always be established, large corporations; they could be smaller, growing companies, or those operating in volatile sectors. These companies may have promising potential, but they also come with inherent risks—risks that often only become apparent later in the investment cycle. This makes having a solid verification process crucial.

For instance, when a firm decides to invest in a relatively unknown startup or a new real estate development, it can be difficult to predict the future trajectory of that investment. Companies might be in their early stages of development, with limited financial history or an unpredictable cash flow. Even well-established companies can face a downturn or an unexpected issue that could lead to default. This is where comprehensive due diligence comes into play. By thoroughly vetting the investors and companies involved in the deal, firms can identify potential red flags early and protect their interests.

The process goes beyond simple financial checks. It involves a deeper dive into the company’s operations, the people behind it, and even its legal and regulatory standing. Examining the background of individuals in senior management positions, understanding the company’s debt structure, and assessing any previous financial troubles are just as important as checking basic financial credentials. If these checks aren’t thorough, the firm risks backing an investment that may become a default later down the line.

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Ensuring Regulatory Compliance And Minimising Risks

For alternative investment platforms, ensuring compliance with local regulations is non-negotiable. Failing to do so could expose a firm to heavy fines, legal disputes, or a tarnished reputation, which is why integrating thorough compliance checks into the investor onboarding process is essential.

Compliance with Anti-Money Laundering (AML) and Know Your Customer (KYC) regulations is key. In addition, ensuring that investors and partners adhere to the legal requirements of both domestic and international markets helps to maintain a clean financial record and avoid any risk of inadvertently becoming involved in illicit activities.

Due diligence, when coupled with these compliance measures, ensures that firms not only meet legal requirements but also adhere to the highest ethical standards. By verifying every aspect of a potential investor’s background, a firm can confirm that they are operating within the bounds of the law while also protecting its own business from future legal complications. This is particularly important when managing high-risk investments where the potential for financial and reputational loss is greater.

Compliance officers and legal advisors also play a vital part in establishing and maintaining these processes, ensuring that every investment, every investor, and every partner is subject to the same rigorous checks. 

Maintaining Long-Term Investor Relationships

In the alternative investment space, relationships often involve long-term commitments and, as such, maintaining trust with investors is crucial. For many, trust is built on transparency and the assurance that their investments are being handled by a firm that conducts thorough checks and balances. Investors need to feel confident that the process is transparent, the due diligence is rigorous, and their money is being managed in the safest way possible.

One of the most significant challenges for alternative investment firms is building a system that provides this level of assurance to investors—especially when dealing with new investors who might not have an established relationship with the firm. As these companies onboard new clients or partners, ensuring that every individual is thoroughly vetted not only reduces the risk of fraud but also strengthens the relationship between the firm and its investors. The more secure investors feel about the processes in place, the more likely they are to invest—and reinvest—in the future.

In a sector where trust is a non-negotiable, firms that take the time to verify their investors’ and partners’ backgrounds demonstrate a commitment to transparency and a willingness to put their clients’ needs first. For investors, particularly high-net-worth individuals (HNWIs), the reassurance that every detail has been thoroughly checked provides peace of mind and fosters confidence in the firm. This confidence is what encourages them to remain committed for the long haul, investing more capital and recommending the firm to others.

As a firm grows and expands, ensuring that this level of diligence continues across all new client relationships is essential. It’s not enough to just check the boxes for regulatory compliance; investors need to feel that they are working with a business that values their trust and is committed to safeguarding their investments over time. A streamlined, transparent onboarding process that involves thorough background verification of every new investor not only protects the firm but also creates lasting relationships built on trust, which is the foundation of any successful business.

How AuthBridge Supports Trust-Building For Alternative Investments Platforms

In a landscape where due diligence is crucial for safeguarding investments and maintaining trust, having a reliable partner to streamline these processes becomes invaluable. AuthBridge plays a vital role in helping alternative investment firms navigate the complexities of background verification and compliance. By integrating robust verification tools, they assist in ensuring that every new investor or partner is thoroughly vetted, reducing the risk of future complications.

For investment firms, AuthBridge’s background verification services go beyond just the basics. By offering a comprehensive suite of checks—including KYC, AML compliance, employment verification, and credit checks—AuthBridge ensures that all parties involved are not only trustworthy but also financially reliable. This makes the onboarding process smoother, quicker, and, most importantly, more secure, which is a key concern for alternative investment companies looking to build long-term investor relationships.

Moreover, the integration of AML and KYC compliance tools provided by AuthBridge is critical for firms managing high-risk investments. These checks not only help in reducing the chances of fraud but also ensure that companies are adhering to stringent regulatory frameworks. 

By working with AuthBridge, alternative investment firms can focus more on what they do best—identifying lucrative opportunities and growing their business—while ensuring that the foundational aspects of due diligence and compliance are taken care of with efficiency and accuracy. 

Conclusion 

In the alternative investment sector, where the stakes are high and trust is paramount, thorough due diligence and reliable background verification are key to success. AuthBridge supports investment firms by providing comprehensive verification services that ensure every investor and partner is thoroughly vetted, reducing risks and maintaining compliance. By partnering with AuthBridge, firms can focus on growing their business with the confidence that their investments are secure, transparent, and aligned with the highest standards of integrity. This not only strengthens investor relationships but also lays a solid foundation for long-term growth and success in a complex and fast-paced market.

By Abhinandan, ago
Customer Risk Assessment

What Is Customer Risk Assessment?

Customer risk assessment is important to the banking sector’s approach to protecting its operations and ensuring compliance with regulatory requirements. It involves evaluating the potential risks associated with individual customers to prevent financial crimes such as money laundering, fraud, and terrorist financing. Banks and other financial institutions must assess the risk profile of each customer to determine the level of scrutiny and monitoring required.

The process has become increasingly critical due to the increasing complexity of financial transactions and the regulatory pressures placed on institutions to prevent illegal activities. Effective customer risk assessment not only helps financial institutions mitigate these risks but also protects their reputation, ensures regulatory compliance, and contributes to a more secure banking environment.

What Is Customer Risk Assessment In Compliance?

Customer risk assessment plays a key role in ensuring that financial institutions meet the stringent requirements set out by regulators. Compliance with anti-money laundering (AML) and counter-terrorist financing (CTF) regulations is mandatory for banks and other financial organisations. A robust risk assessment framework not only helps prevent illegal activities but also protects the institution from hefty fines, reputational damage, and potential legal repercussions.

Financial regulators such as the Financial Conduct Authority (FCA) in the UK require banks to assess the risk associated with each customer, considering factors like the customer’s location, the nature of their business, and their transaction patterns. This is where Know Your Customer (KYC) procedures come into play, as they provide the necessary data for a thorough risk assessment. Through this process, banks can identify whether a customer poses a low, medium, or high risk.

For high-risk customers, more stringent monitoring and due diligence are required. This could include enhanced due diligence (EDD), where banks investigate deeper into the customer’s financial history and sources of wealth. On the other hand, customers deemed low-risk may undergo less frequent checks, allowing the bank to focus its resources where they are needed most.

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Methods Of Conducting Customer Risk Assessments

Conducting a customer risk assessment involves several steps that enable banks to categorise their customers based on the level of risk they present. These methods can vary depending on the size and complexity of the financial institution, but generally, the process follows a systematic approach. Here are some common methods used in conducting customer risk assessments.

1. Know Your Customer (KYC) and Customer Due Diligence (CDD)

At the heart of customer risk assessment lies KYC, which mandates that financial institutions verify the identity of their customers. KYC procedures typically involve collecting key details, such as a customer’s full name, date of birth, address, occupation, and source of funds. This is usually done at the time of onboarding a new client or when a customer’s risk profile needs to be reassessed.

Following KYC, Customer Due Diligence (CDD) is carried out to assess the potential risks associated with the customer. CDD involves examining the nature of the customer’s business activities, the sources of their funds, and their overall financial history. If the customer is deemed to present a higher level of risk, more in-depth procedures like Enhanced Due Diligence (EDD) may be required.

2. Transaction Monitoring

Ongoing monitoring of a customer’s transactions is another crucial element of risk assessment. Banks use sophisticated software tools to track transactions in real time and identify any patterns that deviate from the customer’s normal behaviour. For instance, if a customer begins to make unusually large transfers or engages in cross-border transactions, this could raise a red flag.

Automated transaction monitoring systems use predefined rules to highlight suspicious activities, which are then flagged for review by compliance teams. Regular transaction monitoring allows banks to adjust their risk profiles in response to any changes in customer behaviour and mitigate risks proactively.

3. Risk Scoring and Profiling

Risk scoring involves assigning a numerical value to a customer’s risk level based on various factors, such as their geographical location, industry, transaction history, and personal or corporate background. Each of these factors is weighted to determine an overall risk score. Customers with higher scores are considered to pose a greater risk, and thus, they may be subject to more frequent checks and additional due diligence.

Risk scoring helps financial institutions prioritise their resources effectively, focusing on higher-risk customers while ensuring that lower-risk customers continue to receive standard levels of monitoring.

The Importance Of Customer Risk Assessment For Banks And Customers

Customer risk assessment is vital not only for the protection of financial institutions but also for maintaining a secure and transparent financial system for customers. Both the bank and the customer stand to benefit from an effective risk assessment process, which ensures compliance with regulations and reduces the potential for financial crimes.

For Banks:

For banks, the primary importance of conducting customer risk assessments lies in regulatory compliance. As financial institutions are under increasing scrutiny from regulators, particularly around anti-money laundering (AML) and counter-terrorist financing (CTF), maintaining a rigorous customer risk assessment process helps banks avoid penalties and reputational damage.

Another key benefit is risk mitigation. By assessing the risk level of each customer, banks can better protect themselves from fraud, money laundering, and other illicit activities that could lead to financial loss. Banks also benefit from the efficient allocation of resources, as high-risk customers require more attention, while low-risk customers can be managed with less intervention.

Moreover, conducting a thorough risk assessment also helps build trust with regulators, stakeholders, and customers. A bank that demonstrates a commitment to protecting against financial crimes and adhering to regulatory standards is more likely to establish credibility and maintain a solid reputation.

For Customers:

While customer risk assessments are primarily designed to protect the financial institution, they also have benefits for the customers themselves. An effective risk assessment system helps reduce the likelihood of fraud or other financial crimes, ensuring that a customer’s assets and personal information are protected.

Moreover, customers who undergo a thorough risk assessment are likelier to experience smoother banking services. Financial institutions use this data to personalise their services, ensuring that the right products and services are offered to the right customers based on their risk profile.

Additionally, customers who are subject to enhanced due diligence might find that they are monitored more closely, but this monitoring helps identify any fraudulent activity or security threats before they escalate, ultimately contributing to the overall safety of the customer’s financial interests.

In essence, customer risk assessment serves as a foundational tool for ensuring a safe and compliant banking environment, benefiting both the institution and its clientele by maintaining the integrity of financial systems.

Issues With Customer Risk Assessment

While customer risk assessment is an essential process for ensuring compliance and mitigating risks in banking, it is not without its challenges. Financial institutions face several obstacles in conducting effective and accurate risk assessments, and overcoming these challenges requires a combination of technology, skilled personnel, and well-defined processes.

1. Data Quality and Availability

One of the primary challenges in customer risk assessment is ensuring the accuracy and completeness of the data used for risk profiling. Financial institutions rely heavily on the information provided by customers during the onboarding process. However, if this data is inaccurate, incomplete, or outdated, it can lead to misclassification of risk levels, resulting in poor decision-making. Moreover, obtaining relevant and trustworthy data from customers, especially those in high-risk regions or industries, can be a complex and time-consuming task.

To mitigate this challenge, banks need to implement robust data verification methods, including third-party data sources and digital verification technologies, to ensure the quality and reliability of the information they use for assessments.

2. Regulatory Complexity

Banks must navigate a complex landscape of ever-evolving regulations when conducting customer risk assessments. Regulations related to anti-money laundering (AML), counter-terrorist financing (CTF), and other financial crimes vary by jurisdiction and can change frequently. Financial institutions must keep pace with these regulatory changes to ensure they remain compliant.

For example, different countries have varying standards for what constitutes “high-risk” activities or individuals, which can complicate cross-border customer risk assessments. Compliance teams must stay updated on regulatory changes and adapt their processes accordingly to avoid potential penalties.

3. Balancing Customer Experience with Security

Financial institutions face the ongoing challenge of balancing security measures with the customer experience. While thorough risk assessments and enhanced due diligence procedures are essential for protecting both the bank and its customers, these processes can sometimes lead to friction in customer interactions. Customers may become frustrated with lengthy onboarding processes, multiple verification steps, or delays caused by heightened scrutiny.

To address this, banks must invest in customer-centric solutions that allow for a smooth, efficient onboarding experience while still adhering to security and regulatory requirements. Technologies such as automated verification, biometric authentication, and machine learning can help streamline the process without sacrificing security.

4. Resource Constraints

Customer risk assessments, especially those involving enhanced due diligence, can be resource-intensive. Smaller financial institutions or those with limited resources may struggle to dedicate the necessary staff, time, and technology to conduct thorough assessments for every customer, particularly when dealing with a large volume of clients.

To overcome this, many banks are turning to automated solutions and artificial intelligence (AI) to assist in customer risk assessments. These tools can quickly analyse large datasets and flag high-risk individuals or transactions, allowing banks to prioritise their resources effectively.

5. Upcoming Threats

The ever changing nature of financial crimes presents another challenge. Criminals are continuously adapting their methods to exploit vulnerabilities in banking systems, meaning that banks must remain vigilant in updating their risk assessment strategies. New technologies, such as digital currencies or peer-to-peer payment platforms, can introduce additional risks that banks must account for in their assessments.

To stay ahead of emerging threats, banks must integrate advanced risk assessment tools that can adapt to new types of financial crime and help identify suspicious activities in real time.

Customer Risk Assessment In Banking Future

As the financial services industry continues to evolve, so too must the methods used to assess customer risk. Advances in technology, increased regulatory pressure, and the rise of new financial products and services are reshaping how banks and other financial institutions approach risk assessment. In the future, we are likely to see significant shifts in both the tools and strategies used for customer risk profiling.

1. Integration of Artificial Intelligence (AI) and Machine Learning (ML)

Artificial intelligence (AI) and machine learning (ML) are already playing a significant role in the banking industry, and their impact on customer risk assessment is expected to grow. AI can help automate and accelerate the risk assessment process by analysing vast amounts of data to detect patterns, identify potential risks, and predict customer behaviours.

For instance, ML algorithms can be trained to recognise subtle indicators of fraudulent activities that might go unnoticed by traditional methods. These technologies enable banks to move towards predictive risk assessment, where the focus is on forecasting potential threats based on historical data, rather than reacting to incidents after they occur. This shift promises to enhance the accuracy and efficiency of risk assessments, reducing the likelihood of fraud while providing a better experience for customers.

2. Increased Use of Biometric Authentication

Biometric authentication, such as facial recognition, fingerprint scanning, and voice recognition, is expected to become more widespread in customer onboarding and risk assessment processes. By linking customer identification with biometrics, banks can enhance the accuracy of customer verification while reducing the risk of identity theft and fraud.

As biometric technologies become more sophisticated, they will allow for seamless and secure verification processes that offer greater convenience for customers. The integration of biometrics into risk assessments will also help institutions identify and mitigate risks associated with identity theft and fraudulent account openings more efficiently.

3. Enhanced Regulatory Technology (RegTech)

The rise of RegTech is revolutionising how financial institutions comply with regulations and conduct customer risk assessments. RegTech platforms use cutting-edge technologies such as AI, data analytics, and cloud computing to help banks streamline compliance processes, enhance risk detection, and monitor customer activities in real-time.

These tools can assist banks in staying compliant with regulatory requirements by automating routine compliance tasks, improving data accuracy, and ensuring that all necessary due diligence measures are taken. In the future, RegTech solutions will continue to play a central role in simplifying the risk assessment process while ensuring that banks remain agile in a rapidly changing regulatory landscape.

4. Cross-Border Risk Assessment Integration

As financial institutions continue to expand their global reach, the need for cross-border risk assessments will increase. Banks will need to adopt more robust, automated systems that can analyse customer data across multiple jurisdictions, taking into account the varying regulatory standards and risk factors in different regions.

With the rise of globalisation and the expansion of digital banking, financial institutions will increasingly need to collaborate with international partners and regulators to ensure that their risk assessment frameworks are effective and consistent across borders.

5. Increased Customer Transparency and Control

In the future, customers may have more control and transparency over how their data is used in risk assessments. With the growing emphasis on data privacy and protection, financial institutions may need to provide more clarity regarding how customer information is collected, stored, and used for risk profiling.

Customers may also be able to access and update their risk profiles, ensuring that the information used in the risk assessment process is accurate and up to date. This increased transparency can help build trust between customers and financial institutions, ultimately leading to a more positive banking experience.

Conclusion

The landscape of customer risk assessment in banking is evolving rapidly, driven by technological advancements, regulatory changes, and shifting customer expectations. Banks must stay ahead of these changes to effectively manage the risks associated with their customers while ensuring compliance and protecting their reputation.

By integrating advanced technologies such as AI, machine learning, and biometric authentication, financial institutions can enhance the accuracy and efficiency of their risk assessments, offering a more secure and seamless experience for both banks and their customers. With the continued growth of global financial services and the introduction of new technologies, customer risk assessment will remain a cornerstone of banking practices for years to come.

By Abhinandan, ago
Credit Underwriting in India

Credit Underwriting In India: All You Need To Know

Whether you’re a finance professional, a recent graduate stepping into the world of banking, or simply someone curious about how loans get approved, credit underwriting is something you’ll encounter often. It’s the process lenders use to decide whether someone qualifies for a loan, how much they can borrow, and at what interest rate.

In India, credit underwriting has changed significantly over the years. Earlier, banks and financial institutions relied solely on salary slips, bank statements, and credit scores. But today, lenders assess everything from transaction history and spending patterns to even digital footprints in some cases. With the rise of fintech companies and AI-driven risk models, loan approvals are faster but more complex than before.

So, how does credit underwriting work? What do lenders look at before approving a loan? And how have regulations and technology shaped the process?

This blog breaks it all down in a simple yet insightful way, helping you understand what goes on in the world of credit underwriting.

What Is Credit Underwriting?

Credit underwriting is the process lenders use to evaluate whether a borrower is financially capable of repaying a loan. It’s an important step in lending, ensuring that banks, NBFCs, and digital lenders don’t take on unnecessary risk while also making credit accessible to eligible borrowers.

This can be thought of as a financial background check. When you apply for a loan—whether it’s a home loan, personal loan, or business loan—the lender doesn’t just hand over the money. Instead, they dig into your financial history, analyse your ability to repay, and assess the likelihood of default.

Traditionally, underwriting was a manual process. Loan officers would sift through documents, verify income sources, and determine creditworthiness based on set parameters. But today, thanks to AI and data analytics, underwriting has become faster, more data-driven, and even predictive.

The goal of credit underwriting is simple: to balance risk and reward. Lenders want to approve as many loans as possible to grow their business, but they also need to be cautious and ensure that they are lending to individuals and businesses that can repay on time.

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How Lenders Evaluate Borrowers: Key Factors In Credit Underwriting

Lenders don’t just approve or reject a loan application based on a single factor. Instead, they take a holistic view of a borrower’s financial profile to determine whether granting credit is a safe and viable decision. From checking credit scores to assessing spending behaviour, modern underwriting is a blend of traditional and tech-driven risk evaluation.

Here’s a closer look at the factors that influence credit underwriting decisions in India.

1. Credit Score and Repayment Behaviour

The credit score is one of the first things a lender examines when assessing a borrower’s creditworthiness. In India, credit scores are issued by major credit bureaus such as CIBIL, Equifax, Experian, and CRIF High Mark, based on a borrower’s financial history.

A higher credit score (typically 750 and above) indicates responsible credit usage and timely repayments, leading to:

  • Faster loan approvals
  • Lower interest rates
  • Higher loan amounts

However, a poor credit score (below 650) can result in:

  • Loan rejections
  • Higher interest rates
  • Stricter repayment terms

Beyond the score itself, lenders also analyse a borrower’s repayment behaviour. Consistently missed EMIs, frequent delays, or past defaults raise red flags, making it harder to secure new loans.

2. Income Stability and Source of Earnings

Lenders assess whether a borrower has a stable source of income to ensure consistent repayment ability. This factor is particularly crucial for unsecured loans (such as personal loans) where there is no collateral backing the loan.

What lenders check:

  • For salaried individuals: Employer reputation, job tenure, and monthly salary. Those working in government jobs or well-established private firms often get loans more easily.
  • For self-employed individuals: Business stability, annual turnover, profit margins, and financial records such as tax returns and GST filings.
  • For freelancers/gig workers: Some lenders now consider alternative income sources such as contract work, rental income, and even digital earnings.

A steady and predictable income increases the chances of loan approval, whereas irregular earnings or job instability may result in a higher interest rate or outright rejection.

3. Debt-to-Income Ratio (DTI) – How Much Debt Is Too Much?

Even if a borrower has a good income, lenders check how much of it is already committed to existing debt obligations. This is measured using the Debt-to-Income (DTI) ratio, which is calculated as:

DTI= (Total Monthly Debt Payments / Total Monthly Income) ×100

For example, if someone earns ₹1,00,000 per month but already pays ₹50,000 in EMIs, their DTI ratio is 50%.

Why does this matter?

  • A DTI below 40% is considered safe, meaning the borrower can manage additional loan repayments.
  • A DTI above 50% signals financial strain, making lenders hesitant to approve new credit.

Lenders prefer borrowers with a lower DTI because it reduces the risk of over-leveraging, which could lead to missed payments or defaults.

4. Type of Loan and Security Provided – Secured vs Unsecured Lending

Not all loans are assessed equally. The underwriting process varies depending on whether the loan is secured (backed by collateral) or unsecured (granted purely based on creditworthiness).

  • Secured Loans (Home Loans, Auto Loans, Gold Loans, etc.) – Since the lender has an asset as security, credit risk is lower. Even borrowers with moderate credit scores may qualify if the collateral holds sufficient value.
  • Unsecured Loans (Personal Loans, Credit Cards, Business Loans, etc.) – These loans are riskier for lenders, leading to stricter credit evaluations and higher interest rates for applicants with weaker financial profiles.

For business loans, lenders also assess company performance, industry risks, and financial stability before making a lending decision.

5. Alternative Data And AI-Based Underwriting

With the rise of digital lending, many lenders now go beyond traditional credit scores and use alternative data to evaluate creditworthiness.

This includes:

  1. Utility bill payments – A borrower who consistently pays electricity, mobile, and rent bills on time may be considered financially responsible.
  2. Spending habits – Lenders analyse banking transactions to see how much a borrower saves, invests, or spends each month.
  3. Digital footprints – Some AI-based models assess online transactions, subscriptions, and even shopping patterns to predict financial behaviour.

For borrowers without a formal credit history (such as young professionals or gig workers), these alternative credit models offer a fairer assessment, allowing them to access loans even if they don’t have a CIBIL score.

6. Compliance with RBI Regulations

Lenders must also ensure that their underwriting process follows the Reserve Bank of India’s (RBI) regulations, which are frequently updated to improve financial stability.

Recent RBI measures include:

  • Stricter underwriting for unsecured loans to prevent excessive risk-taking.
  • AI and credit risk model guidelines to ensure fair lending decisions.
  • Mandatory credit reporting every 15 days to improve borrower transparency.

For P2P lending platforms and fintech lenders, RBI has imposed additional checks to protect borrowers from predatory lending practices and ensure transparency in loan disbursals.

How Borrowers Can Improve Their Creditworthiness

Understanding these factors can help borrowers improve their chances of securing a loan with favourable terms. Some simple yet effective steps to improve credit score include:

  • Maintaining a high credit score by paying EMIs and credit card bills on time.
  • Keeping the Debt-to-Income ratio below 40% to ensure financial stability.
  • Demonstrating income stability, whether through steady employment or consistent business earnings.
  • Building a credit history by using small credit products like secured credit cards or buy-now-pay-later (BNPL) services responsibly.

How Technology Is Changing Credit Underwriting In India

The way lenders assess borrowers has changed dramatically over the past decade. What was once a slow, manual process dependent on paperwork and human judgment is now faster, data-driven, and automated. Thanks to advancements in AI, alternative data, and automation, credit underwriting is becoming more efficient, accurate, and accessible.

Let’s break down the biggest changes.

AI Is Replacing Manual Credit Assessment

Traditionally, loan approvals involved human underwriters reviewing salary slips, bank statements, and credit history. This process was time-consuming and often biased towards borrowers with well-documented incomes.

Today, AI-driven underwriting models can:

  • Assess credit risk instantly by analysing thousands of data points.
  • Detect fraud by identifying document inconsistencies.
  • Predict repayment behaviour using advanced algorithms.

Lenders no longer rely only on credit scores—they now use AI models to predict future financial behaviour based on transaction history, spending patterns, and even digital payments.

Borrowers Without A Credit Score Can Now Get Loans

One of the biggest problems in India’s lending ecosystem has always been the lack of formal credit histories. Millions of people—especially gig workers, small business owners, and young professionals—struggle to get loans because they don’t have a CIBIL score.

To solve this, many lenders are now using alternative credit scoring models, which take into account:

  • Utility bill payments (electricity, mobile, rent)
  • Spending and saving patterns from bank accounts
  • Digital payment transactions (UPI, wallets, BNPL services)

This approach has made credit more inclusive, allowing first-time borrowers to access loans without relying on traditional credit reports.

Loans Are Getting Approved Faster With Automated Underwriting

In the past, loan approvals could take days or even weeks because banks had to manually verify documents and assess risk. Today, many lenders have moved to automated underwriting systems, where AI handles the entire decision-making process.

Here’s how automated underwriting works:

  • Borrowers apply online, and their financial data is instantly retrieved.
  • AI analyses income, spending behaviour, and creditworthiness.
  • Loan approval (or rejection) happens within minutes, with minimal human involvement.

For personal loans, credit cards, and small-ticket financing, many fintech lenders now offer real-time approvals, making borrowing easier and faster.

Video KYC And Digital Onboarding Have Replaced Paperwork

With RBI pushing for digital banking, loan applications no longer require physical paperwork. Instead, lenders now use:

  • Aadhaar-based e-KYC for instant identity verification.
  • Video KYC to complete onboarding remotely.
  • AI-driven document verification to detect fraud and forged details.

These changes have reduced operational costs for lenders and made borrowing seamless for customers, particularly in rural and semi-urban areas.

Blockchain Could Make Credit Histories More Transparent

Although still in the early stages, blockchain technology has the potential to make credit underwriting more secure and tamper-proof. If widely adopted, it could:

  • Store borrower credit histories on a decentralised network, preventing fraud.
  • Allow borrowers to own and share their financial data securely with lenders.
  • Reduce dependency on centralised credit bureaus and speed up loan approvals.

While blockchain-based lending hasn’t become mainstream yet, it’s expected to play a larger role in the future of trust-based digital credit models.

Technology is reshaping the lending landscape, and borrowers need to understand how it impacts them.

If you’re applying for a loan, this means:

  • Faster loan approvals (often within minutes).
  • More accurate risk assessments, reducing unfair rejections.
  • Better access to credit, even for those without a CIBIL score.

Challenges And Limitations In Credit Underwriting

While credit underwriting has become faster and more data-driven, it is far from perfect. Lenders still face challenges in accurately assessing risk, ensuring fair loan approvals, and preventing fraud. On the other hand, borrowers often struggle with inconsistent lending criteria, outdated credit models, and transparency issues.

Let’s explore some of the biggest challenges and limitations that affect credit underwriting in India today.

1. Incomplete Credit Histories Still Impact Borrowers

Despite technological advancements, millions of Indians still struggle to get loans due to a lack of formal credit history. This issue is most common among:

  • First-time borrowers (students, young professionals).
  • Gig workers and freelancers with irregular incomes.
  • Small business owners who do not have well-documented financials.

Even though alternative credit scoring methods (such as analysing utility bill payments and digital transactions) are gaining traction, most banks and NBFCs still rely heavily on traditional credit scores. This means many deserving borrowers get rejected simply because they don’t fit into conventional risk models.

2. Over-reliance On Credit Scores Can Be Misleading

A high credit score does not always mean a borrower is financially responsible, and a low score does not always mean they are risky. Traditional credit scoring models have limitations, such as:

  • Not accounting for sudden financial improvements (e.g., a borrower may have struggled in the past but is now earning well).
  • Failing to consider alternate income sources (many people earn from side businesses, investments, or freelance work that doesn’t reflect in official income records).
  • Overlooking contextual factors (a missed EMI due to an emergency should not be weighed the same as habitual defaults).

This rigid scoring system often leads to unfair loan rejections, particularly for self-employed individuals and informal sector workers.

3. Inconsistent Lending Policies Across Lenders

There is no standard underwriting model followed across the lending industry. Each bank, NBFC, and fintech lender has its risk assessment framework, leading to inconsistencies in loan approvals.

For example:

  • One lender might approve a loan for a borrower with a 680 credit score, while another might reject them outright.
  • Some banks have strict income criteria, whereas digital lenders consider transaction behaviour instead.
  • Loan terms (interest rates, tenure, and fees) can vary widely for the same borrower based on the lender’s internal policies.

This lack of uniformity makes it difficult for borrowers to understand what they qualify for and why they were rejected.

4. Rising Loan Fraud And Identity Theft

With more lenders shifting to digital underwriting, fraudsters are finding new ways to manipulate the system. Some common fraud risks include:

  • Fake financial documents – Fraudulent salary slips, fake bank statements, and forged tax returns.
  • Identity theft – Using stolen Aadhaar and PAN details to apply for loans.
  • Loan stacking – Borrowers taking multiple loans from different lenders simultaneously before their credit reports update.

Although AI and data analytics help detect fraud patterns, many lenders still rely on traditional verification methods, making them vulnerable to sophisticated fraud schemes.

5. Bias In AI-Based Underwriting Models

AI and machine learning have made credit underwriting faster and more efficient, but they also come with risks—especially bias in decision-making.

  • AI models are trained on historical lending data, which means if past lending decisions were biased (e.g., rejecting self-employed borrowers more often), the AI might continue reinforcing those biases.
  • Some AI-driven underwriting systems lack transparency, making it difficult for borrowers to challenge loan rejections.
  • Borrowers from lower-income groups or rural areas may be unfairly categorised as high-risk, simply because they don’t have enough digital financial data.

Without proper regulation, AI-based lending can become just as unfair as traditional underwriting, if not worse.

6. Regulatory Uncertainty And Changing RBI Guidelines

The Reserve Bank of India (RBI) frequently updates lending regulations to prevent excessive risk-taking and consumer exploitation. While these changes are necessary, they create challenges for lenders who must constantly adapt their underwriting models.

Some recent regulatory shifts that have impacted underwriting include:

  • Stricter personal loan guidelines to prevent over-lending.
  • Mandatory fortnightly credit reporting to reduce risk from multiple loans.
  • Tighter regulations for digital lenders and BNPL (Buy Now, Pay Later) providers to protect borrowers.

While these changes improve financial stability, they also make it harder for lenders to create a consistent underwriting framework, especially fintech startups that rely on digital credit models.

Conclusion

Credit underwriting in India has evolved significantly, shifting from manual paperwork-based approvals to AI-driven, data-driven decision-making. Today, lenders use a mix of traditional credit scores, alternative data sources, and AI-based risk models to assess borrowers. While these advancements have made loan approvals faster and more accessible, challenges such as credit exclusions, fraud risks, and regulatory uncertainty still persist.

For borrowers, understanding how underwriting works can help improve creditworthiness and increase loan approval chances. Meanwhile, for lenders, embracing transparency, standardised risk models, and fair lending practices will be key to ensuring a sustainable lending ecosystem.

By Abhinandan, ago
Union Budget 2025-2026

Union Budget 2025-26: Key Highlights And Updates

Introduction

The Union Budget 2025-26, presented by the Honourable Finance Minister Smt. Nirmala Sitharaman marks a significant moment in India’s economic sojurn. With the theme of “Sabka Vikas” (inclusive growth), this budget is crafted to address the aspirations of a diverse population, spanning from middle-class households to large corporations. Quoting the renowned Telugu poet Gurajada Appa Rao, “A country is not just its soil; a country is its people,” the Finance Minister highlighted the government’s commitment to people-centric policies.

This budget reflects India’s ambition to accelerate towards Viksit Bharat (a developed India) by focusing on fiscal consolidation, economic resilience, and sustainable development. It highlights four key drivers of growth—Agriculture, Micro, Small and Medium Enterprises (MSMEs), Investments, and Exports—all aimed at fostering an environment that nurtures economic expansion and social welfare.

Key Tax Reforms And Implications For Individuals And Businesses

The Union Budget 2025-26 introduces significant tax reforms aimed at simplifying the tax structure, promoting voluntary compliance, and easing the financial burden on both individuals and businesses. With a clear focus on enhancing disposable income and fostering a business-friendly environment, the new tax proposals are designed to stimulate consumption, savings, and investment across the economy.

1. Income Tax Reforms

One of the most notable announcements in this budget is the revised income tax regime, which brings substantial relief to the middle class. The government has introduced a progressive tax structure where individuals with an annual income of up to ₹12 lakh will not be liable to pay any income tax, thanks to the new slabs and a standard deduction of ₹75,000. 

Tax slabs
Image Source: PIB.gov.in

This effectively means that salaried individuals earning up to ₹12.75 lakh annually will pay zero income tax, putting more money directly into the hands of millions of Indians.

Revised Income Tax Slabs (New Tax Regime):

Annual Income (₹)

Rate of Tax

0 – 4,00,000

NIL

4,00,001 – 8,00,000

5%

8,00,001 – 12,00,000

10%

12,00,001 – 16,00,000

15%

16,00,001 – 20,00,000

20%

20,00,001 – 24,00,000

25%

Above 24,00,000

30%

This new tax regime, in essence, will simplify compliance, making it easier for taxpayers to file returns without the complexities of multiple exemptions and deductions.

2. TDS and TCS Rationalisation

The budget proposes several changes to the Tax Deducted at Source (TDS) and Tax Collected at Source (TCS) provisions to streamline tax collection and reduce compliance burdens:

  • TDS on Rent: The threshold for TDS on rental income has been increased from ₹2.4 lakh to ₹6 lakh per annum. This is a significant relief for individuals and small businesses, as it reduces the administrative hassle of managing TDS for smaller rental incomes.
  • Senior Citizens’ Interest Income: The limit for a tax deduction on interest income for senior citizens has been doubled from ₹50,000 to ₹1 lakh, providing additional tax relief to retirees and encouraging savings in fixed-income instruments.
  • Decriminalisation of TDS/TCS Delays: In a progressive move, the budget has decriminalised delays in the payment of both TDS and TCS. This aligns with the government’s broader agenda of reducing the fear of prosecution for minor compliance delays, fostering a more taxpayer-friendly environment.
Smt. Nirmala Sitharaman addressing a Post Budget Press Conference at National Media Centre, in New Delhi on February 01, 2025
Smt. Nirmala Sitharaman addressing a Post Budget Press Conference at National Media Centre, in New Delhi on February 01, 2025

3. Simplified Tax Compliance and Voluntary Disclosures

To promote voluntary compliance, the government has extended the time limit for filing updated income tax returns from the current two years to four years. This provides taxpayers with a longer window to correct errors or omissions in their original filings without facing severe penalties. Over 90 lakh taxpayers have already benefited from this provision in the past, reflecting its success in encouraging honest disclosures.

Additionally, the Vivad Se Vishwas Scheme, aimed at resolving tax disputes, has seen strong participation with nearly 33,000 taxpayers availing of its benefits. The continuation and expansion of such schemes highlight the government’s focus on reducing litigation and increasing trust between taxpayers and the administration.

4. Corporate Tax and Business-Friendly Initiatives

For businesses, especially start-ups and MSMEs, the budget offers a range of incentives designed to promote growth and investment:

  • Presumptive Taxation for Non-Residents: A new presumptive taxation regime has been introduced for non-resident entities providing services to Indian companies, particularly in the electronics manufacturing sector. This move simplifies tax calculations and encourages foreign businesses to invest in India.
  • Extension of Start-Up Benefits: The eligibility period for start-ups to avail of tax exemptions has been extended by five years, providing much-needed support to India’s vibrant start-up ecosystem. This extension is expected to encourage entrepreneurship and innovation across sectors.
  • Incentives for Sovereign Wealth and Pension Funds: To boost infrastructure investment, the budget has extended the deadline for investments in sovereign wealth funds and pension funds by five more years, until 31st March 2030. This move is likely to attract long-term capital into critical infrastructure projects.

5. Relief on Customs Duties and Import Tariffs

The budget also proposes several changes to customs duties to promote domestic manufacturing and reduce dependency on imports:

  • Exemption on Lifesaving Drugs: Basic Customs Duty (BCD) has been exempted from 36 lifesaving drugs used to treat cancer, rare diseases, and chronic conditions. This will make essential medicines more affordable for patients.
  • Boost to EV and Battery Manufacturing: To support the electric vehicle ecosystem, BCD exemptions have been extended to capital goods used for EV and mobile battery manufacturing. This is expected to reduce production costs and promote the adoption of clean energy technologies.
  • Duty Rationalisation for Exports: BCD has been reduced from 30% to 5% on frozen fish paste and from 15% to 5% on fish hydrolysate, supporting the seafood export industry and enhancing competitiveness in global markets.
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Sector-Specific Highlights: Agriculture, MSMEs, Investment, And Exports

The Union Budget 2025-26 strategically identifies four key engines of growth—Agriculture, Micro, Small and Medium Enterprises (MSMEs), Investments, and Exports—as the pillars driving India’s journey towards Viksit Bharat (a developed India). This section provides a comprehensive analysis of the sector-specific initiatives that reflect the government’s commitment to inclusive development, economic resilience, and global competitiveness.

1. Agriculture: Strengthening the Backbone of the Economy

Agriculture remains the cornerstone of India’s economy, employing nearly half of the workforce. Recognising its critical role, the budget introduces several transformative schemes aimed at increasing productivity, ensuring food security, and improving farmers’ incomes.

Key Initiatives:

  • Prime Minister Dhan-Dhaanya Krishi Yojana:
    This flagship programme will cover 100 districts identified as having low agricultural productivity. It focuses on crop diversification, post-harvest storage, irrigation improvement, and ensuring the availability of both short- and long-term credit facilities. The partnership with state governments will facilitate region-specific strategies to enhance agricultural resilience.
  • Mission for Aatmanirbharta in Pulses:
    A six-year mission focusing on key pulses—Tur, Urad, and Masoor—has been announced to achieve self-sufficiency in pulse production. Central agencies like NAFED and NCCF will procure these pulses from farmers for the next four years, ensuring stable market prices and income security.
  • Kisan Credit Card (KCC) Expansion:
    The loan limit under the KCC scheme has been increased from ₹3 lakh to ₹5 lakh, with a modified interest subvention scheme. This will enhance credit accessibility for small and marginal farmers, supporting agricultural investments and modernisation.
  • Comprehensive Programme for Fruits and Vegetables:
    To address post-harvest losses and improve value chains, the government has launched initiatives focusing on the fruit and vegetable sectors, alongside a National Mission on High-Yielding Seeds and a Five-Year Mission for Cotton Productivity.

2. MSMEs

MSMEs contribute significantly to India’s GDP, employment generation, and exports. Recognising their potential, the budget outlines a robust framework to enhance credit access, promote technological upgrades, and support entrepreneurial ventures.

Key Initiatives:

  • Enhanced Credit Guarantee Cover:
    The credit guarantee limit for MSMEs has been doubled from ₹5 crore to ₹10 crore, making it easier for small businesses to secure loans at favourable terms. This move aims to boost business expansion, particularly in the post-pandemic recovery phase.
  • New Scheme for First-Time Entrepreneurs:
    A dedicated scheme targeting 5 lakh women, Scheduled Castes, and Scheduled Tribes entrepreneurs will provide term loans of up to ₹2 crore over the next five years. This initiative is designed to promote inclusivity in entrepreneurship and support start-ups from underrepresented communities.
  • National Manufacturing Mission:
    Covering small, medium, and large industries, this mission aims to strengthen the ‘Make in India’ initiative. It focuses on enhancing manufacturing capabilities, encouraging technological innovation, and integrating Indian businesses into global supply chains.
  • Toy Manufacturing Promotion:
    In a bid to reduce dependency on imports, the government will support domestic toy manufacturers through subsidies and skill development programmes, reinforcing the ‘Made in India’ brand in global markets.

3. Investment

Investment is the cornerstone of sustainable economic growth. The budget outlines a multi-pronged strategy focusing on infrastructure development, human capital enhancement, and technological innovation to create a robust investment ecosystem.

Key Initiatives:

  • Atal Tinkering Labs:
    The budget proposes setting up 50,000 Atal Tinkering Labs in government schools over the next five years. These labs will foster a culture of innovation and scientific curiosity among students, preparing the next generation for emerging industries.
  • Centre of Excellence in Artificial Intelligence (AI):
    With an outlay of ₹500 crore, the government will establish an AI centre focused on education. This initiative aims to integrate advanced AI technologies into learning environments, enhancing digital literacy and research capabilities.
  • Urban Challenge Fund:
    A significant allocation of ₹1 lakh crore has been made for the ‘Cities as Growth Hubs’ programme. This fund will support urban redevelopment, improve water and sanitation infrastructure, and promote sustainable urbanisation.
  • Private Sector-Led R&D Initiatives:
    The budget allocates ₹20,000 crore for private sector-driven research, development, and innovation. This move aims to foster collaboration between academia, industry, and government, driving breakthroughs in technology, healthcare, and clean energy.
  • BharatNet for Digital Connectivity:
    To bridge the digital divide, broadband connectivity will be provided to all government secondary schools and primary health centres in rural areas, ensuring equitable access to digital resources.

4. Exports

Exports play a vital role in boosting foreign exchange reserves, creating jobs, and strengthening India’s position in the global economy. The budget outlines several measures to promote exports and integrate Indian businesses with international markets.

Key Initiatives:

  • Export Promotion Mission:
    A unified Export Promotion Mission will be launched, jointly driven by the Ministries of Commerce, MSME, and Finance. This mission will focus on helping MSMEs tap into global markets through financial assistance, capacity building, and marketing support.
  • BharatTradeNet (BTN):
    A new digital public infrastructure platform, BharatTradeNet, will be established to streamline international trade documentation and provide financing solutions. This will reduce red tape, enhance transparency, and improve the ease of doing business for exporters.
  • Infrastructure Upgradation for Exports:
    The budget proposes upgrading air cargo infrastructure, including facilities for high-value perishable horticulture produce. This will improve supply chain efficiency and reduce transit times for perishable goods.
  • Support for Domestic Electronics Manufacturing:
    To capitalise on Industry 4.0 opportunities, the government will support the domestic electronics industry through incentives, infrastructure development, and R&D support.

Reforms For Growth

Reforms are positioned as the fuel that powers these four growth engines. The budget continues the government’s focus on ease of doing business, regulatory simplification, and fiscal prudence.

  • Jan Vishwas Bill 2.0:
    The bill aims to decriminalise over 100 provisions in various laws, reducing legal hurdles for businesses and encouraging entrepreneurship.
  • Foreign Direct Investment (FDI) Liberalisation:
    The FDI limit in the insurance sector has been raised from 74% to 100%, aimed at attracting foreign capital and promoting growth in the financial services sector.
  • Light-Touch Regulatory Framework:
    A high-level committee will be established to review non-financial sector regulations, with recommendations expected within a year. This framework aims to balance regulatory oversight with the need for business agility.
  • Investment Friendliness Index:
    To encourage healthy competition among states, an Investment Friendliness Index will be launched in 2025. This index will evaluate states based on ease of doing business, infrastructure, and investment policies.

Fiscal Consolidation And Budgetary Estimates

The budget reaffirms the government’s commitment to fiscal discipline, with a clear roadmap to reduce the fiscal deficit and maintain macroeconomic stability.

  • Fiscal Deficit Targets:
    The fiscal deficit for FY 2024-25 is estimated at 4.8% of GDP, with a target to bring it down to 4.4% in FY 2025-26. This reflects a balanced approach towards growth and fiscal prudence.
  • Revised Estimates for 2024-25:
    • Total Receipts (Excluding Borrowings): ₹31.47 lakh crore
    • Net Tax Receipts: ₹25.57 lakh crore
    • Total Expenditure: ₹47.16 lakh crore
    • Capital Expenditure: ₹10.18 lakh crore
  • Budget Estimates for 2025-26:
    • Total Receipts (Excluding Borrowings): ₹34.96 lakh crore
    • Net Tax Receipts: ₹28.37 lakh crore
    • Total Expenditure: ₹50.65 lakh crore

Key Takeaways From The 2025 Union Budget

  1. Middle-Class Relief: Significant tax cuts, zero tax liability for incomes up to ₹12 lakh, and increased deductions for senior citizens.
  2. Boost to MSMEs: Enhanced credit guarantees, support for first-time entrepreneurs, and initiatives to promote domestic manufacturing.
  3. Agricultural Reforms: Increased Kisan Credit limits, focus on pulses self-sufficiency, and comprehensive rural development programmes.
  4. Investment in Innovation: Allocations for AI, R&D, urban development, and digital connectivity to drive India’s technological growth.
  5. Ease of Doing Business: Decriminalisation of minor tax offences, simplified compliance, and promotion of voluntary disclosures.
  6. Exports & Global Integration: Support for MSME exports, infrastructure upgrades for air cargo, and reduction of customs duties on key commodities.

Conclusion

The Union Budget 2025-26 at its core, reflects a balanced approach—providing substantial tax relief to the middle class, fostering entrepreneurship through MSME support, strengthening the agricultural backbone, and fuelling investments in infrastructure, technology, and innovation. The recognition of **four growth engines—Agriculture, MSMEs, Investments, and Exports—**demonstrates the government’s strategic vision to diversify economic drivers and ensure resilience against global uncertainties.

FAQs around Union Budget 2025-2026

New Income Tax Slabs for 2025-26 (New Regime):

  • Up to ₹4 lakh – 0% (No tax)
  • ₹4 lakh to ₹8 lakh – 5%
  • ₹8 lakh to ₹12 lakh – 10%
  • ₹12 lakh to ₹16 lakh – 15%
  • ₹16 lakh to ₹20 lakh – 20%
  • ₹20 lakh to ₹24 lakh – 25%
  • Above ₹24 lakh – 30%

Standard Deduction: Increased to ₹75,000.
Tax-Free Income Limit: Up to ₹12.75 lakh after deductions.

Yes, the Union Budget 2025-26 announced an increase of 6,500 seats in Indian Institutes of Technology (IITs), focusing on those established after 2014.

The Union Budget 2025-26 focused on tax reforms, infrastructure, and inclusive growth. Income tax slabs have been revised with no tax up to ₹4 lakh and a 30% rate above ₹24 lakh, while the standard deduction is increased to ₹75,000, making income up to ₹12.75 lakh tax-free. The budget allocates ₹11.2 trillion for capital expenditure, targets a fiscal deficit of 4.4% of GDP, and projects 10.1% nominal GDP growth. Key announcements include 6,500 new IIT seats, 10,000 medical seats, a ₹10,000 crore startup fund, increased FDI in insurance (100%), and missions to boost pulses and cotton production. Defense gets ₹6.81 lakh crore, with added focus on MSME support, renewable energy, and middle-class relief.

In the Union Budget 2025-26, several items have become cheaper due to customs duty reductions. Lifesaving drugs, including 36 critical medicines, are now exempt from basic customs duties. Motorcycles have lower duties, with 40% for engines up to 1600cc and 30% for larger ones. Export duty on crust leather has been removed, and duties on jewelry and platinum findings are reduced to 20% and 5%, respectively. Additionally, imported furniture and key electronic components, including mobile parts, have seen duty cuts, making them more affordable.

In the Union Budget 2025-26, there are no changes to the old tax regime in terms of tax slabs or deductions. However, taxpayers under the new regime benefit from increased tax rebates. A key update is the rebate for incomes up to ₹12 lakh, ensuring zero tax liability after deductions. For salaried individuals, the standard deduction has increased to ₹75,000, effectively making income up to ₹12.75 lakh tax-free. These changes apply only to the new regime, while the old regime remains unchanged.

Income up to ₹12 lakh is effectively tax-free under the new tax regime due to a combination of revised tax slabs and an enhanced rebate. Here’s how it works:

  1. Revised Tax Slabs:

    • 0–4 lakh: 0% (no tax)
    • 4–8 lakh: 5%
    • 8–12 lakh: 10%

  2. Full Rebate: A tax rebate has been introduced for income up to ₹12 lakh. This means even if tax is calculated based on the slabs, the rebate cancels out the tax liability, making it effectively zero.

  3. Standard Deduction: For salaried individuals, an additional ₹75,000 standard deduction applies, increasing the effective tax-free limit to ₹12.75 lakh.

This rebate ensures that while tax is technically computed, it’s offset completely, resulting in zero tax payable for income up to ₹12 lakh.

In the Union Budget 2025-26, the standard deduction has been increased from ₹50,000 to ₹75,000 for salaried individuals and pensioners. This increase effectively raises the tax-free income threshold, making income up to ₹12.75 lakh tax-free under the new tax regime when combined with the revised tax slabs and the rebate for incomes up to ₹12 lakh.

The standard deduction is a fixed amount that salaried individuals and pensioners can deduct from their gross income, reducing their taxable income without needing to provide specific expense proofs. In the Union Budget 2025-26, the standard deduction has been increased from ₹50,000 to ₹75,000. This means if your total income is, say, ₹13 lakh, you can subtract ₹75,000 from it, making your taxable income ₹12.25 lakh. This helps lower your overall tax liability, especially under the new tax regime.

By Abhinandan, ago

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