AI for Fraud Detection and Risk Management

INTRODUCTION

Fraud and risk are persistent threats to public and private institutions. Artificial Intelligence (AI) presents transformative opportunities to detect, prevent, and respond to these risks in real time. This course provides a strategic and technical foundation in using AI and machine learning to strengthen fraud detection, monitor risk patterns, and support data-driven decision-making.

HOW YOU BENEFIT

• Understand core concepts in fraud risk and detection
• Explore the capabilities of AI, ML, and predictive analytics
• Design AI-based systems for real-time fraud monitoring
• Reduce false positives and improve investigation outcomes
• Enhance enterprise risk management through intelligent automation

WHO SHOULD ATTEND

• Internal auditors and compliance officers
• Risk managers and fraud investigators
• IT and cybersecurity teams
• Financial controllers and operations managers
• Data analysts and AI implementation leads

DURATION

10 Days

COURSE MODULES

Module 1: Introduction to Fraud and Risk in the Digital Age

• Types of fraud (financial, identity, cyber, procurement)
• Trends in fraud schemes and risk vulnerabilities

Module 2: Fundamentals of Artificial Intelligence and Machine Learning

• Key AI/ML concepts and terminology
• Supervised vs. unsupervised learning

Module 3: Data Sources for Fraud Detection

• Data quality, integration, and cleaning
• Real-time vs. historical data sources

Module 4: Designing AI Models for Fraud Detection

• Algorithms for anomaly detection and classification
• Training, testing, and model evaluation

Module 5: Pattern Recognition and Behavioral Analysis

• Detecting outliers and fraud signatures
• User profiling and transaction scoring

Module 6: Risk Scoring and Prioritization

• Building risk matrices
• Integrating AI output with decision frameworks

Module 7: Implementing AI Systems

• AI platforms and tools (Python, R, cloud AI tools)
• Automation of alerts and case management systems

Module 8: Governance, Ethics, and Regulatory Compliance

• Bias, transparency, and accountability in AI
• Legal frameworks and compliance obligations

Module 9: Business Continuity and Incident Response

• AI for early warning and risk mitigation
• Designing resilient systems for response and recovery

Module 10: Future Trends and Strategic Integration

• AI in enterprise risk management (ERM)
• Building an AI roadmap for sustainable fraud prevention

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