AI for Fraud Detection in BFSI

This course aims to equip participants with advanced knowledge and skills in utilizing artificial intelligence to detect and prevent fraud within the Banking, Financial Services, and Insurance (BFSI) sector, focusing on practical applications and cutting-edge technologies.

• Understand the fundamentals and advanced concepts of AI as they apply to fraud detection.
• Analyze various types of financial fraud and the role of AI in combating these activities.
• Develop hands-on skills in data handling, machine learning models, and AI strategies specific to fraud detection.
• Explore the ethical and regulatory aspects of using AI in financial environments.

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.