AI for Fraud Detection in BFSI: Navigating Financial Integrity

Aim

This course explains how AI is used to detect and prevent fraud in Banking, Financial Services, and Insurance (BFSI). Participants learn how fraud signals are captured, how detection models are designed, how alerts are tuned to reduce false positives, and how governance is applied for safe deployment.

Who This Course Is For

• Fraud, risk, compliance, and AML teams in banks, NBFCs, insurers, and FinTech
• Fraud operations and investigation teams handling alerts and cases
• Data/AI professionals building transaction monitoring and fraud analytics systems
• Audit and governance teams reviewing model outputs and controls

Prerequisites

• Basic understanding of BFSI workflows is helpful
• No coding required (optional demonstrations can be included)
• Familiarity with common fraud terms (transaction monitoring, AML) is a plus

What You’ll Learn

• Fraud types in BFSI: card fraud, account takeover, payments fraud, loan fraud, insurance fraud
• Fraud data signals: transactions, customer profile, device/session, geo, merchant behavior
• Detection approaches: rules, supervised ML, anomaly detection, and hybrid systems
• Feature design: velocity checks, geo/device mismatch, risky merchant patterns, behavior change
• Alert quality: precision/recall, thresholding, reducing false positives
• Case workflow: triage, prioritization, investigation notes, and feedback loops
• Monitoring: drift, pattern shifts, alert volume changes, retraining triggers
• Governance: privacy, access control, explainability, documentation, audit trails

Program Structure

Module 1: Fraud Landscape in BFSI

• How fraud occurs across channels (UPI/cards/net banking/loans/insurance)
• Detection goals: prevention, early warning, loss reduction
• Key fraud KPIs and operational constraints

Module 2: Fraud Data & Signals

• Transaction + customer + device/session data sources
• Labeling challenges and leakage control
• Data quality, imbalance, and privacy considerations

Module 3: Detection Strategies (Rules + AI)

• Rules engines and when they are effective
• Supervised models for known fraud patterns
• Hybrid strategy for real-world monitoring

Module 4: Anomaly Detection & Behavioral Profiling

• Finding unusual behavior without labels
• Customer baseline vs sudden change detection
• Risk scoring and prioritization logic

Module 5: Network & Collusion Signals (Conceptual)

• Entity linking: accounts, devices, merchants, beneficiaries
• Detecting mule networks and coordinated fraud rings
• Network features for stronger detection

Module 6: Alert Tuning & Investigation Workflow

• Precision vs recall trade-offs for operations
• Thresholds, segmentation, and prioritization
• Case management and feedback loops

Module 7: Monitoring & Drift Management

• Fraud pattern shifts and seasonality
• Drift detection and alert volume anomalies
• Controlled retraining triggers and reporting

Module 8: Governance & Controls

• Explainability and decision traceability
• Access control, logging, and audit trails
• Operational controls for high-risk decisions

Tools & Platforms Covered

• Concepts used in transaction monitoring systems and alert pipelines
• Supervised + anomaly detection workflows (conceptual + optional demos)
• Basic investigation dashboards and reporting metrics

Outcomes

• Define fraud use-cases and select suitable detection methods
• Design an alert pipeline with prioritization and feedback loops
• Plan monitoring for drift and emerging fraud patterns
• Apply governance controls for safe and auditable deployment

Certificate Criteria (Optional)

• Complete learning checkpoints
• Submit a short fraud detection strategy note (use-case + detection approach + KPIs)