AI for Risk Management in BFSI: Navigating the Future of Finance

Aim

This course explains how AI strengthens risk management in Banking, Financial Services, and Insurance (BFSI). Participants learn how AI supports risk identification, scoring, monitoring, and early warning—across credit, market, liquidity, operational, and compliance risk—while maintaining governance, explainability, and audit readiness.

Who This Course Is For

• Risk, credit, underwriting, compliance, and governance teams in BFSI
• Banking/FinTech professionals working on risk systems and controls
• Analysts and managers in credit operations, portfolio monitoring, and collections
• Data/AI teams supporting risk analytics and decision systems

Prerequisites

• Basic familiarity with BFSI and risk terms is helpful
• No coding required (optional demonstrations can be included)
• Comfort with KPIs and dashboards is a plus

What You’ll Learn

• Risk landscape in BFSI: credit, market, liquidity, operational, fraud, and compliance
• Risk data: customer profile, bureau, transactions, behavior, macro signals
• Credit risk modeling: scoring, early warning signals, delinquency prediction
• Portfolio monitoring: segmentation, roll-rate logic, and risk trend dashboards
• Stress testing and scenario analysis (baseline vs adverse scenarios)
• Model validation: leakage control, bias checks, and explainability
• Monitoring: drift detection, threshold tuning, and retraining triggers
• Governance: documentation, approvals, audit trails, and Responsible AI

Program Structure

Module 1: BFSI Risk Fundamentals and AI Use-Cases

• Where AI improves risk decisions and controls
• Key risk KPIs: PD, delinquency, loss rate, NPA trends, early warning
• Common deployment challenges and how to manage them

Module 2: Risk Data and Feature Design

• Data sources: bureau, transactions, customer behavior, macro indicators
• Data quality, missingness, imbalance, and leakage prevention
• Feature design: lag signals, rolling metrics, stability checks

Module 3: Credit Risk Scoring and Underwriting

• Scoring approaches and model selection overview
• Early delinquency prediction and limit management signals
• Reason codes and explainability for decision support

Module 4: Portfolio Risk Monitoring and Early Warning

• Customer and portfolio segmentation
• Early warning dashboards and trigger-based alerts
• Collections support: prioritization and risk-based actions

Module 5: Market, Liquidity, and Operational Risk (Conceptual)

• Signals for volatility, exposure monitoring, and risk concentrations
• Operational risk analytics: process issues, anomalies, loss events
• Controls and exceptions management

Module 6: Stress Testing and Scenario Analysis

• Scenario design and sensitivity analysis basics
• Adverse macro conditions and portfolio impact reasoning
• Linking scenarios to risk actions and limits

Module 7: Model Monitoring and Drift Management

• Performance tracking by segment and time
• Drift signals, threshold tuning, and alerting
• Controlled retraining and approvals

Module 8: Governance and Audit Readiness

• Validation, documentation, and change control
• Privacy, access control, and secure handling of risk data
• Responsible AI practices for regulated environments

Tools & Platforms Covered

• Risk analytics workflows and monitoring dashboards (conceptual)
• Credit scoring and portfolio monitoring patterns (optional demos)
• Governance templates: validation notes, monitoring reports, audit checklist

Outcomes

• Create a BFSI risk use-case map and prioritize AI initiatives
• Define model KPIs, thresholds, and monitoring routines
• Plan stress testing and scenario-driven decision support
• Apply governance controls for compliant, auditable AI deployment

Certificate Criteria (Optional)

• Complete learning checkpoints
• Submit a short risk strategy note (use-case + KPIs + monitoring + governance)