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Course Outline
Foundations of AI in Financial Crime Prevention
- The landscape of fraud and AML in the digital finance landscape
- Comparing traditional methods with AI-driven solutions
- Real-world examples from Mastercard, JPMorgan, and global banking institutions
Machine Learning for Monitoring Transactions
- Applying supervised learning for risk assessment and classification
- Using unsupervised learning techniques for spotting anomalies
- Generating real-time alerts through stream processing
Graph Analytics and Identifying Network Risks
- Mapping connections between entities and transaction flows
- Uncovering sophisticated fraud schemes using graph AI
- Practical exercises with Neo4j or comparable platforms
NLP Applications in Anti-Money Laundering
- Text analysis in customer due diligence (CDD) processes
- Watchlist screening using named entity recognition (NER)
- Document review and suspicious activity reports (SARs) via prompt-based systems
Model Governance and Transparency
- Creating models that are explainable and subject to audit
- Identifying and reducing bias in fraud detection algorithms
- Applying XAI methods in compliance environments
Ethics, Regulatory Compliance, and Model Risk
- Adhering to AML and KYC standards (e.g. FATF, FinCEN, EBA)
- Ethical considerations in AI-driven surveillance and customer monitoring
- Meeting reporting standards and ensuring regulatory audit trails
Deployment Tactics and Emerging Trends
- Embedding AI models into current transaction infrastructure
- Establishing feedback loops and mechanisms for model updates
- The role of generative AI in fraud investigation and SAR automation
Recap and Future Directions
Requirements
- A solid grasp of fraud risks and AML protocols
- Practical experience in data analytics or compliance reporting
- Fundamental knowledge of Python or analytics platforms
Target Audience
- Specialists in fraud risk management
- Teams responsible for AML compliance
- Security administrators
14 Hours
Testimonials (1)
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