AI for Fraud Detection in BFSI: Navigating Financial Integrity Course

• Use unsupervised learning for anomaly detection

• Build real-time transaction monitoring pipelines

• Detect insurance fraud using vision and text analysis

• Apply graph-based AI to uncover fraud rings

• Deploy secure, scalable models in banking environments

• Implement Explainable AI for regulatory reporting

• Design end-to-end fraud classification solutions

Module 1 — AI & Financial Integrity Foundations

• History of financial fraud: from traditional methods to digital-age scams
• The mathematics of risk: probability and statistics for fraud
• AI fundamentals for BFSI professionals
• Overview of the Indian regulatory landscape (RBI, DPDP Act)

Module 2 — Data Engineering & Feature Pipelines

• Handling imbalanced datasets where fraud is a needle in a haystack
• Feature engineering: identifying red flag variables in transaction logs
• Data preprocessing for high-velocity financial streams
• Synthetic data generation using SMOTE for model training

Module 3 — Model Architecture & Algorithm Design

• Unsupervised learning for anomaly and outlier detection
• Supervised learning for fraud classification using XGBoost and Random Forest
• Deep learning for sequential pattern recognition using RNNs and LSTMs
• Graph neural networks for fraud ring identification

Module 4 — Training, Hyperparameter Optimization & Evaluation

• Tuning models for high recall to ensure no fraud is missed
• Optimizing thresholds to balance security and customer experience
• Evaluating models using Precision-Recall curves and AUC-ROC
• Cross-validation strategies for imbalanced financial datasets

Module 5 — Deployment & MLOps

• Building real-time inference engines for transaction approval
• Monitoring for concept drift as fraudsters evolve their tactics
• Secure model deployment within banking firewalls
• Logging, auditing, and rollback strategies in production

Module 6 — Ethics, Bias & Responsible AI

• Preventing algorithmic bias in credit and fraud scoring
• Explainable AI (XAI): providing reasons for flagged transactions to regulators
• Data privacy and the DPDP Act (India) compliance
• Ethical design principles for high-stakes automated financial decisions

Module 7 — Industry Integration & Case Studies

• Case study: detecting UPI-based phishing scams in India
• AML (Anti-Money Laundering) patterns in global banking
• Insurance fraud detection using computer vision and text analysis
• Real-world project: building a credit card fraud classifier

Module 8 — Capstone: End-to-End AI Fraud Detection Solution

• Define a fraud detection problem in a BFSI context
• Select and justify the appropriate AI and graph-based methodology
• Build an integrated model that detects anomalies and classifies fraud type
• Deliver an explainability report for compliance and regulatory review