AI-Driven Fraud Detection & Financial Crime Prevention

Description

Background / Overview

Financial fraud and financial crime continue to evolve in complexity and scale, driven by digitalization, online payments, and sophisticated criminal networks. Traditional fraud detection methods—often rule-based—struggle to keep pace with modern threats such as synthetic identities, advanced phishing, trade-based money laundering, and cyber-enabled fraud.

Artificial Intelligence (AI) and Machine Learning (ML) are now at the forefront of combating financial crime, enabling organizations to detect anomalies, predict suspicious activities, and automate compliance processes in real time. By leveraging AI, financial institutions, fintechs, regulators, and compliance officers can significantly improve detection rates, reduce false positives, and strengthen their financial crime risk management frameworks.

This program equips participants with the knowledge and tools to apply AI techniques in fraud detection and financial crime prevention, combining theory, case studies, and practical exercises.

Agenda / Content

Day 1 – Foundations of AI in Financial Crime Prevention

• Introduction to Fraud & Financial Crime Typologies

  • Fraud, Money Laundering, Terrorist Financing, Cyber-Enabled Crime

• Limitations of Traditional Fraud Detection (rule-based systems, false positives)

• Fundamentals of AI & ML in Fraud Detection

  • Supervised vs. unsupervised learning

  • Natural Language Processing (NLP) & anomaly detection

• Case Studies: AI applications in banking, insurance, payments

Day 2 – AI Applications in Fraud Detection & AML

• Building AI Models for Fraud Detection

  • Data sources: transactional, behavioral, biometric, KYC/CDD data

  • Feature engineering & model training

• AI in Anti-Money Laundering (AML) & Counter-Terrorism Financing (CTF)

  • Suspicious activity monitoring & automated reporting

  • AI for sanctions screening & transaction monitoring

• Hands-On Session: Using an AI tool for anomaly detection in transactions

• Case Study: AI-driven fraud prevention in digital banking & fintech

Day 3 – Implementation, Ethics, and Governance

• Deploying AI Systems in Financial Institutions

  • Integration with core banking systems, payment platforms, and compliance tools

  • Balancing automation with human oversight

• Risks & Challenges

  • Data privacy & regulatory compliance (GDPR, FATF, Basel guidelines)

  • AI explainability, bias, and model governance

• Future Trends in AI & Financial Crime

  • Generative AI in fraud prevention

  • Autonomous monitoring systems & predictive analytics

• Group Exercise: Designing an AI-driven fraud detection framework for an institution

• Final Review & Q&A

Objectives

By the end of this program, participants will:

1. Understand how AI and ML are applied to fraud detection and financial crime prevention.

2. Recognize fraud and financial crime patterns beyond traditional detection systems.

3. Gain insights into AI-powered tools for AML, KYC, and transaction monitoring.

4. Learn to evaluate, deploy, and govern AI solutions within compliance frameworks.

5. Explore case studies and hands-on exercises to bridge theory and practice.

Outcomes

Participants will leave with:

• Practical knowledge of AI techniques for fraud detection and AML compliance.

• Improved ability to design, implement, or evaluate AI-powered fraud prevention systems.

• Awareness of regulatory, ethical, and operational risks in AI adoption.

• A draft framework/action plan for integrating AI into their institution’s financial crime strategy.

• Enhanced readiness to lead AI-driven fraud detection initiatives.