Risk management has always been the cornerstone of banking and financial services. For the longest time, banks managed credit, compliance, and fraud risks through manual assessments, strict rule-based systems, and historical data analysis. While these methods helped protect banks, the processes had their own constraints, including limited data visibility, process bottlenecks, and the inability to predict real-time threats.

Today, artificial intelligence (AI) is bringing a transformative change in how banks approach risk management, resulting in improved fraud detection and more accurate creditworthiness assessments. By handling vast amounts of both structured and unstructured data, AI changes risk management from a reactive function into an agile, intelligence-driven framework capable of adapting to modern financial ecosystems.

Smarter Credit Decisions through Explainable AI

Earlier, assessing an applicant’s eligibility for credit was dependent on financial statements, credit scores, and historical repayment patterns. This approach often left blind spots, especially in uncertain conditions or when new types of borrowers entered the system.

AI is now enabling banks and NBFCs to use alternative sources of data to evaluate such borrowers. These include digital payment histories, utility and telecom bills, rental payment history, social media signals, public records, payroll data, e-commerce behavior, BNPL repayment, and even cash flow patterns. By combining these unconventional indicators with traditional credit metrics, AI creates a far richer borrower profile, improving access to credit while minimising default risks.

This approach is particularly valuable for segments such as New-to-Bank (NTB), New-to-Credit (NTC), and the unserved market (e.g., young professionals, gig workers, recent immigrants), where limited or no bureau history renders traditional models inadequate. Fintech lenders and several banks have begun leveraging Account Aggregator frameworks to securely access consent-based financial data, expanding credit access without increasing risk exposure.

According to a 2024 World Bank Group report, AI-driven analytics leverage alternative data to identify subtle borrower behavior patterns that traditional systems often overlook, thereby enhancing both financial inclusion and stability. For instance, microfinance institutions and fintech lenders are already using AI models trained on behavioral and transactional data to underwrite small-ticket loans for gig workers and MSMEs.

Nevertheless, the growing complexity of AI algorithms demands transparency. As regulators increasingly insist that AI processes and outcomes be reasonably understood by banks, the use of Explainable AI (XAI) is becoming a standard practice. Technologies like SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) enable banks to go beyond simple risk assessments. It also clarifies the reasoning behind the outcomes, making AI models more transparent and trustworthy. This transparency enables institutions to strike a balance between efficiency and accountability.

Automating the Compliance Burden

High pressure continues to exist for banks to remain compliant in a landscape that is continually evolving and expanding across areas such as anti-money laundering (AML), customer verification (KYC), privacy laws, and real-time transaction monitoring. Conventionally, compliance teams had to go through piles of paperwork and manual checklists, which often resulted in delays and errors. Additionally, adapting to rapid regulatory changes within a short timeframe for faster Go-to-Market and compliance adherence remains a significant challenge for lending institutions worldwide.

AI is now lifting this burden by automating core compliance workflows. For example, AI programs can help banks implement KYC procedures using computer vision to verify customer identities and detect document inconsistencies or signs of fraud. Similarly, AI fortifies AML processes by flagging unusual accounts or behaviors associated with money laundering, such as the movement of identical currency amounts between disparate accounts. AI models also ensure that the processes used by financial institutions are up-to-date with the latest guidelines and directives released by central banks and regional authorities, including government guidelines and court orders.

Furthermore, technologies like Natural Language Processing (NLP) can read thousands of documents, contracts, and filings at once, instantly highlighting irregularities or regulatory gaps. Leading banks are deploying AI-powered systems to monitor millions of financial transactions for identifying and flagging any suspicious activities. These tools help compliance officers filter out routine cases while seroing in on higher-risk scenarios such as suspicious cross-border transfers. Additionally, predictive compliance analytics are enabling institutions to anticipate potential regulatory breaches or operational risks before they occur. Such systems help banks maintain real-time regulatory alignment, a critical advantage as new laws around AI governance, data privacy (such as GDPR and the DPDP Act), and ethical AI continue to evolve.

Building on this momentum, banks are evolving toward a “continuous compliance” model, where AI systems not only monitor transactions but also track and interpret regulatory changes in real time. By using generative AI and knowledge graphs, institutions can automatically map updates, whether from Reserve Bank of India’s (RBI) data localisation norms, Securities and Exchange Board of India’s (SEBI) cybersecurity guidelines, or global frameworks like Financial Action Task Force (FATF) and General Data Protection Regulation (GDPR), to their internal policies. This integration enables more proactive, consistent, and audit-ready compliance, aligning with the pace of regulatory change.

The Proactive Fight Against Evolving Fraud

As digital banking adoption grows, fraudsters have become more sophisticated. Most banks continue to rely on traditional rules-based systems to detect fraudulent transactions. And while 59% of respondents do find this approach effective, many agree that it isn’t sufficient on its own and must be combined with other detection methods. Rules-based models alone often fall short when dealing with today’s complex and evolving scam tactics, such as:

• First-party fraud: While rules-based systems fail because the applicant uses a legitimate identity, AI predicts fraud by studying behavioural patterns and hidden intent to flag “bust-out” schemes early.
• Deepfake fraud: While traditional models focus only a successful login and authorised payment, AI detects fraud by identifying digital artifacts in synthetic media and anomalous user behavior during the session.
• Transactional laundering: Whereas rules are blind to micro-transactions that stay below static thresholds, AI exposes the scheme by recognising the sophisticated, coordinated pattern across thousands of accounts.

Banks close this gap by leveraging AI-first fraud detection systems that learn and adapt. These systems are proactive, analysing millions of transactions simultaneously, identifying hidden patterns, and unveiling unusual activities that people might miss. AI cross-references identity data, telecom data, social network data, and application behaviour to detect improbable identity constructs. It also assesses geolocation consistency, behavioural biometrics, and graph analytics to detect linked accounts, potential mule networks, and hidden relationships. Trained AI models can generate real-time behavioural scoring of payments and context-aware risk scoring for each transaction, automatically adding friction or challenge when anomalies are detected.

Leading regulators observed that AI fraud models can decrease undetected fraud cases by more than half. Banks are harnessing AI to generate dynamic risk scores for every transaction, enabling them to block fraudulent activity in real-time, often before the customer is even aware of it.

Navigating with AI

The integration of AI is well underway, with global financial institutions forming strategic partnerships with tech firms to modernise their risk assessment frameworks. Stock exchanges are also actively partnering with specialised technology vendors to adopt AI tools for credit checks, forecasting, and compliance. This shift indicates that AI is transitioning from an extra add-on to a core operational component.

Despite the clear benefits, banks and financial institutions face challenges in using AI.

• Data and model bias: Data is often locked in and spread across legacy systems, making it difficult to build accurate models. In addition, if the historical data is biased, AI can amplify unfair outcomes.
• New threat areas: Emerging forms of risk, such as deepfake documents, synthetic identities, or voice cloning fraud, are designed to bypass biometric authentication and necessitate the development of new, advanced safeguards.
• Regulatory hurdles: Regulators are struggling with AI’s ‘black box’ problem. They demand clear transparency and explainability from AI. For instance, if an AI model rejects a loan application, the bank should be able to provide a logical explanation for the decision.
• Operational complexities: Integrating AI into legacy IT systems, upskilling current staff, data engineering, and continuously monitoring AI performance require heavy investments. Without a robust governance framework, rushing the adoption of AI can lead to operational failures and severe reputational damage.
• Customer trust and acceptance issues: Decisions driven by AI continue to raise user concerns about their fairness and transparency, with customers often questioning why certain documents were requested or loan terms were revised.

The Path Ahead

Looking forward, financial risk management is likely to move from silos, reactive systems to proactive, integrated intelligence. The next frontier is a holistic, AI-powered framework that integrates compliance, credit risk, and fraud detection. Rather than monitoring risks in siloes, a unified view will allow financial institutions to anticipate problems before they arise.

As regulators tighten oversight and consumers demand transparency, banks that embed explainability, fairness, and inclusivity at the model level will lead the transformation.

For institutions that enable AI-first risk frameworks globally, this shift represents a unique opportunity to unify credit decisioning, compliance automation, and fraud prevention within a single intelligent ecosystem, making risk management resilient and trusted.

The author is Kaushal Verma, Head – Global Banking Centre of Excellence, Newgen Software.

Disclaimer: The views expressed are solely of the author and ETCISO does not necessarily subscribe to it. ETCISO shall not be responsible for any damage caused to any person/organization directly or indirectly.

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