AI for Fraud Detection & Anti‑Money Laundering Training Course
AI is transforming how financial institutions detect fraud and combat money laundering through intelligent, real-time analysis of vast transaction datasets.
This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to apply machine learning and AI tools to automate and enhance financial crime detection, compliance monitoring, and operational governance.
By the end of this training, participants will be able to:
- Understand AI use cases in fraud detection and AML monitoring.
- Design and implement models for anomaly detection and transaction scoring.
- Leverage graph-based AI for network risk detection.
- Ensure ethical, explainable, and regulatory-compliant model deployment.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction to AI in Financial Crime
- Overview of fraud and AML in the digital finance era
- Traditional vs AI-based approaches
- Case studies from Mastercard, JPMorgan, and global banks
Machine Learning for Transaction Monitoring
- Supervised learning for risk scoring and classification
- Unsupervised learning for anomaly detection
- Real-time alert generation and stream processing
Graph Analytics and Network Risk Detection
- Modeling relationships between entities and transactions
- Detecting complex fraud schemes using graph AI
- Hands-on with Neo4j or similar tools
Natural Language Processing for AML
- Text mining in customer due diligence (CDD)
- Watchlist scanning using named entity recognition (NER)
- Prompt-based document review and suspicious activity reports (SARs)
Model Governance and Explainability
- Building explainable and auditable models
- Bias detection and mitigation in fraud detection algorithms
- Use of XAI techniques in compliance settings
Ethics, Regulation, and Model Risk
- Compliance with AML and KYC frameworks (e.g. FATF, FinCEN, EBA)
- AI ethics in surveillance and customer monitoring
- Reporting standards and regulatory auditability
Deployment Strategies and Future Trends
- Integrating AI models into existing transaction systems
- Feedback loops and model updating mechanisms
- Future of generative AI in fraud investigation and SAR automation
Summary and Next Steps
Requirements
- An understanding of fraud risk and AML procedures
- Experience with data analysis or compliance reporting
- Basic familiarity with Python or analytics platforms
Audience
- Fraud risk professionals
- AML compliance teams
- Security managers
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