MODULE 1

Introduction to AI and Fraud Detection

• Basics of AI in BFSI.
• Types and impacts of financial fraud.
• Evolution of fraud detection technologies.

MODULE 2

Mastering Data Handling & Preprocessing for AI in BFSI

• Data collection and preprocessing techniques.
• Feature engineering and data quality’s impact on fraud detection.
• Practical exercises on real financial datasets.

MODULE 3

Machine Learning Models for Fraud Detection

• Application of supervised learning models like decision trees and logistic regression.
• Advanced models including random forests and gradient boosting machines.
• Building, training, and evaluating models for fraud detection.

MODULE 4

Deep Learning and NLP for Fraud Detection

• Using neural networks and NLP to detect complex fraud patterns.
• Practical application of deep learning in real-world fraud detection.
• Integration of NLP techniques into fraud detection projects.

MODULE 5

Anomaly Detection and Behavioral Biometrics

• Techniques for identifying anomalies and behavioral patterns.
• Application of behavioral biometrics in fraud prevention.
• Hands-on case studies and project work on anomaly detection.

MODULE 6

AI in Fraud Detection: Case Studies and Regulatory Compliance

• Examination of real-world applications and case studies.
• Navigating legal and ethical considerations.
• Regulatory compliance affecting AI fraud detection systems.

MODULE 7

Developing an AI Fraud Detection Strategy

• Strategic considerations for implementing AI in fraud prevention.
• Emerging technologies in AI fraud detection.
• Design and implementation of AI strategies in financial institutions.

MODULE 8

Course Wrap-Up and Capstone Project

• Consolidation of learned concepts.
• Preparation and presentation of capstone projects.
• Reflective learning and application of AI techniques in real-world scenarios.