AI in Risk Management and Fraud Detection
54mIntermediate2025-07-11
Authors
Glenn Hopper
Course details
In this course, instructor Glenn Hopper examines the role of AI in improving risk management and fraud detection, providing practical solutions for financial challenges. Explore ways to use machine learning techniques for anomaly detection. Learn about using generative AI applications to create automated fraud reports and alerts. Discover hands-on strategies to streamline workflows and level up your decision-making. This course emphasizes practical, hands-on implementation, equipping finance professionals with strategies to streamline workflows, detect anomalies, and enhance decision-making.
Learning objectives
Demonstrate anomaly detection using machine learning algorithms with no-code machine learning tools.
Analyze fraud patterns and extract actionable insights with AI.
Break down the creation of detailed fraud reports and real-time alerts using generative AI.
Identify optimal stages for integrating AI into financial risk management workflows to maximize efficiency.
Learning objectives
Demonstrate anomaly detection using machine learning algorithms with no-code machine learning tools.
Analyze fraud patterns and extract actionable insights with AI.
Break down the creation of detailed fraud reports and real-time alerts using generative AI.
Identify optimal stages for integrating AI into financial risk management workflows to maximize efficiency.
Skills covered
Security TestingAI for Business FoundationsArtificial Intelligence for BusinessCybersecurityOne-Off
Concepts
0. Introduction
- 01 - AI-powered solutions for risk management
- 02 - Using AI in risk management and fraud detection
1. Foundations of AI in Risk Management
- 03 - Understanding risk management and AI
- 04 - Key techniques for anomaly detection
- 05 - Generative AI for fraud detection and reporting
- 06 - Identifying AI opportunities in risk workflows
2. Building Anomaly Detection Models
- 07 - Data understanding and preparation in ChatGPT
- 08 - Handling imbalanced datasets
- 09 - Feature engineering and preprocessing in ChatGPT
- 10 - Building and evaluating baseline models
- 11 - Advanced modeling and hyperparameter tuning
- 12 - Model interpretation, validation, and monitoring
3. Applying Generative AI to Fraud Reporting
- 13 - Automating fraud alerts with generative AI
- 14 - Designing comprehensive fraud reports
- 15 - Integrating AI tools into finance operations
- 16 - Best practices and emerging trends
Conclusion
- 17 - Continue your AI risk management journey
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