AI for Fraud Detection in BFSI

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

• 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.

• 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.

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

• 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.

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