AI in Fraud Detection and Risk Management

Introduction to AI in Fraud Detection and Risk Management
• Overview of AI and its applications in fraud detection and risk management.
• Key fraud types (financial fraud, identity theft, etc.) and AI’s role in combating them.
• Benefits of using AI tools in risk management.

Fraud Detection Techniques Using AI
• Introduction to machine learning in fraud detection.
• Supervised learning for fraud identification: basic algorithms like decision trees and logistic regression.
• Anomaly detection using AI to spot unusual behavior or outliers in data.

Machine Learning Models for Fraud Detection
• Key algorithms for fraud detection: Random Forests, Support Vector Machines (SVM).
• Data preprocessing for training AI models.
• Evaluation of model performance: accuracy, precision, recall, and F1-score.

AI in Risk Assessment
• Using AI to assess and predict risks: financial, operational, and credit risks.
• Real-time risk mitigation with AI tools.
• Risk prediction models and their application in various industries.

1ST BATCH: Tuesday, March 24, 2026 — Friday, March 27, 2026.

2ND BATCH: Tuesday, July 14, 2026 — Friday, July 17, 2026.

3RD BATCH: Tuesday, November 10, 2026 — Friday, November 13, 2026.

The training methodology integrates lectures, interactive discussions, collaborative group exercises, and
illustrative examples. Participants will acquire a blend of theoretical insights and hands-on practical
experience, emphasizing the application of learned techniques. This approach ensures that attendees return
to their professional environments equipped with both the competence and self-assurance to effectively
implement the acquired skills in their responsibilities.