As India’s financial services sector embraces rapid digitization, the risk of fraud has escalated. To stay ahead of increasingly sophisticated fraud tactics, banks and NBFCs must adopt advanced fraud surveillance strategies.

In this series, we explore practical, regulation-aligned strategies and technologies that are transforming fraud detection and monitoring capabilities.

01  Shift from Rule-Based Systems to AI-Driven Fraud Detection

📌 Trend:

Traditional, rule-based fraud monitoring systems are increasingly being replaced or augmented by AI and machine learning algorithms.

📋 Why It Matters:

Rule-based systems often generate high false positives and cannot adapt to new fraud typologies. AI-driven solutions can learn from data patterns, spot anomalies, and flag emerging fraud risks in real-time.

🇮🇳 Regulatory Relevance:

The RBI has emphasized the use of advanced analytics in fraud risk management (e.g., via circulars on cybersecurity frameworks and early warning systems).

✅ Recommended Practices:

• Deploy supervised ML models for transaction monitoring and customer behavior analysis.
• Implement adaptive risk scoring mechanisms.

Establish regular model validation and audit trails for regulatory scrutiny.

02  Real-Time Surveillance for Transactional Integrity

📌 Trend:

Fraud prevention is moving from batch-based detection to real-time, continuous monitoring across digital channels.

📋 Why It Matters:

With UPI, IMPS, and mobile banking facilitating instant payments, delays in fraud detection lead to irreversible losses and reputational harm.

🇮🇳 Regulatory Relevance:

RBI’s circular on “Framework for Digital Payment Security Controls” encourages real-time fraud control mechanisms for high-speed digital payments.

✅ Recommended Practices:

• Integrate fraud detection engines with real-time transaction pipelines.
• Use real-time behavioral analytics to detect deviations from normal activity.
• Automate red-flag alerts with escalation workflows for fraud operations teams.

03  Centralized Fraud Risk Management Systems (FRMS)

📌 Trend:

Institutions are centralizing fraud detection across products and channels to avoid silos and ensure holistic risk visibility.

📋 Why It Matters:

Fraud often spans multiple customer touchpoints—credit, deposits, mobile apps, etc. A siloed approach misses cross-channel threats and weakens response times.

🇮🇳 Regulatory Relevance:

RBI’s guidelines on fraud classification and reporting suggest coordinated monitoring and timely reporting through a centralized FRMS.

✅ Recommended Practices:

• Build an enterprise-wide FRMS that aggregates alerts across departments.
• Use unified dashboards for cross-functional risk visibility.

Ensure integration with regulatory reporting tools (e.g., FRMS-FIU submissions).

04  Behavioral Biometrics and Continuous Authentication

📌 Trend:

Authentication is moving beyond one-time passwords and static credentials to behavioral biometrics—such as typing speed, device orientation, and navigation patterns.

📋 Why It Matters:

Fraudsters can bypass traditional login methods, but behavioral biometrics are harder to replicate and enable continuous authentication even after login.

🇮🇳 Regulatory Relevance:

While not mandated, RBI’s emphasis on multi-factor authentication and risk-based access controls supports the use of such adaptive methods, especially under cybersecurity and digital lending guidelines.

✅ Recommended Practices:

• Integrate behavioral biometric tools into mobile/web applications.
• Use anomaly detection to flag suspicious user behavior in-session.
• Combine with contextual data (location, device, IP) for dynamic risk scoring.

05  Integration of AML and Fraud Monitoring Systems

📌 Trend:

Financial institutions are increasingly merging Anti-Money Laundering (AML) and fraud detection systems to identify overlapping risks and reduce operational inefficiencies.

📋 Why It Matters:

Many fraud patterns also indicate AML concerns (e.g., mule accounts, smurfing). Separate systems often lead to fragmented analysis and delayed action.

🇮🇳 Regulatory Relevance:

FIU-IND and RBI both require timely Suspicious Transaction Reports (STRs) and Early Warning Signals (EWS). A unified platform helps meet these timelines and improves investigative outcomes.

✅ Recommended Practices:

• Establish a shared intelligence framework between fraud and AML teams.
• Use common risk indicators (e.g., sudden account activity, layering) across modules.

Align workflows for reporting STRs and fraud alerts via centralized case management.

06  Geolocation and Device Intelligence for Fraud Detection

📌 Trend:

Geolocation tracking, device fingerprinting, and IP intelligence are being used to flag inconsistencies in customer access and transaction behavior.

📋 Why It Matters:

A transaction initiated from an unfamiliar device or location can be an early signal of fraud—especially in account takeover (ATO) scenarios.

🇮🇳 Regulatory Relevance:

Though indirect, RBI’s digital payment security and mobile banking circulars urge banks to detect anomalous access behavior and ensure secure customer environments.

✅ Recommended Practices:

• Integrate geolocation analytics with login and payment flows.
• Maintain device profiles and flag unknown or risky device fingerprints.
• Trigger step-up authentication or temporary holds for high-risk behavior.

07  Early Warning Systems (EWS) for Credit Fraud and NPAs

📌 Trend:

Institutions are strengthening Early Warning Systems (EWS) to detect signs of credit-related fraud and proactively manage potential Non-Performing Assets (NPAs).

📋 Why It Matters:

Many frauds related to loans and advances are discovered only after the account turns into an NPA. Timely EWS helps flag irregularities like fund diversion, overstated financials, or unusual repayment behavior.

🇮🇳 Regulatory Relevance:

RBI’s Master Directions on Fraud Classification and subsequent circulars mandate EWS frameworks and fraud monitoring committees for loans above certain thresholds.

✅ Recommended Practices:

• Monitor borrower behavior patterns (e.g., delayed EMI, sudden drop in turnover).
• Cross-reference financials with GST, bank statement, and bureau data.

Automate flagging of known red-flag indicators (RFIs) per RBI guidelines.

08  Fraud Surveillance in Digital Lending Ecosystems

📌 Trend:

With the rise of Digital Lending Platforms (DLPs) and Loan Service Providers (LSPs), regulators are focusing on fraud risk throughout the digital lending value chain.

📋 Why It Matters:

Digital lending involves multiple stakeholders—banks, NBFCs, tech partners—creating vulnerabilities around customer KYC, loan misrepresentation, and recovery practices.

🇮🇳 Regulatory Relevance:

RBI’s 2022 and 2023 Digital Lending Guidelines set out clear directives on partner accountability, data handling, KYC, and grievance redressal, with a focus on preventing fraud and misuse.

✅ Recommended Practices:

• Conduct due diligence on all LSPs and fintech partners.
• Monitor loan disbursement patterns for anomalies (e.g., burst disbursements, high early defaults).
• Ensure all customer-facing digital journeys include consent, audit trails, and risk triggers.

09  Enhanced Reporting and Case Management Frameworks

📌 Trend:

Institutions are building integrated case management systems to streamline fraud investigations, maintain auditability, and ensure timely regulatory reporting.

📋 Why It Matters:

Disjointed or manual investigation processes often lead to delays in fraud escalation and incomplete documentation—risking compliance failures.

🇮🇳 Regulatory Relevance:

RBI mandates timelines for fraud reporting (FMR submission within 3–21 days, depending on case type) and encourages prompt filing of police complaints and STRs under PMLA.

✅ Recommended Practices:

• Centralize alert triaging, investigation, and resolution under one platform.
• Automate generation of Fraud Monitoring Returns (FMR) and Suspicious Transaction Reports (STR).

Maintain a complete audit trail of actions taken for internal and external audit purposes.

10  Customer Education as a First Line of Defense

📌 Trend:

Banks and NBFCs are increasingly investing in customer awareness programs as part of their fraud risk management strategies.

📋 Why It Matters:

Many frauds—especially phishing, vishing, and OTP scams—occur due to customer unawareness or negligence. Educated customers are significantly less likely to fall victim to such attacks.

🇮🇳 Regulatory Relevance:

RBI’s guidelines on grievance redressal and consumer protection (especially in digital banking and payments) highlight the need for proactive fraud awareness communication.

✅ Recommended Practices:

• Run regular multilingual campaigns via SMS, email, and app notifications.
• Simulate common fraud scenarios (e.g., fake calls, QR code traps) to educate users.
• Track engagement metrics to assess the effectiveness of awareness initiatives.

11  Cybersecurity Integration in Fraud Risk Management

📌 Trend:

Fraud surveillance is being increasingly integrated with cybersecurity operations, recognizing the overlap between fraud, breaches, and insider threats.

📋 Why It Matters:

Sophisticated frauds often stem from system vulnerabilities, compromised credentials, or insider access—blurring the lines between cyberattacks and financial fraud.

🇮🇳 Regulatory Relevance:

RBI’s Cybersecurity Framework (especially for UCBs, NBFCs, and PSBs) mandates integrated security controls, incident response mechanisms, and periodic audits.

✅ Recommended Practices:

• Establish cross-functional coordination between fraud, IT security, and compliance teams.
• Use a unified Security Information and Event Management (SIEM) platform for monitoring.
• Run red team exercises to test fraud detection and cyber incident readiness.

12  Use of Alternative Data and Social Intelligence

📌 Trend:

Institutions are tapping into alternative data sources—including telecom data, social media, and digital footprints—to enrich fraud risk scoring.

📋 Why It Matters:

Traditional data points like credit bureau scores or financial statements may not be available or sufficient, especially in first-time borrower segments or new-to-credit profiles. Alternative data can provide early fraud indicators.

🇮🇳 Regulatory Relevance:

While RBI hasn’t formally regulated the use of alternative data, digital lending norms require transparency, consent-based data use, and proper risk disclosures—especially for non-traditional underwriting.

✅ Recommended Practices:

• Combine telco metadata, device reputation, and app usage behavior for deeper fraud profiling.
• Ensure customer consent is obtained for any non-financial data used in scoring.
• Use explainable AI models to justify decisions based on alternative data inputs.

Building a Fraud-Resilient Financial Future

As India’s financial services ecosystem rapidly evolves, so must our approach to fraud surveillance and management.

A strategic blend of technology, regulatory insight, and system-wide integration is essential to stay ahead of emerging threats.

@ AgilisOne

www.AgilisOne.com