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Course Outline
Introduction to AI in Financial Crime
- The landscape of fraud and AML in digital finance
- Comparing traditional methods with AI-driven solutions
- Insights from case studies involving Mastercard, JPMorgan, and global banking institutions
Machine Learning for Transaction Monitoring
- Applying supervised learning for risk scoring and classification
- Utilising unsupervised learning for identifying anomalies
- Generating real-time alerts via stream processing
Graph Analytics and Network Risk Detection
- Modelling connections between entities and transactions
- Uncovering complex fraud schemes through graph AI
- Practical experience with Neo4j or comparable tools
Natural Language Processing for AML
- Text mining applications in customer due diligence (CDD)
- Executing watchlist scans using named entity recognition (NER)
- Prompt-driven document review and drafting of suspicious activity reports (SARs)
Model Governance and Explainability
- Developing models that are both explainable and auditable
- Identifying and mitigating bias in fraud detection algorithms
- Implementing XAI techniques within compliance frameworks
Ethics, Regulation, and Model Risk
- Adhering to AML and KYC frameworks (e.g., FATF, FinCEN, EBA)
- Ethical considerations in AI surveillance and customer monitoring
- Meeting reporting standards and ensuring regulatory auditability
Deployment Strategies and Future Trends
- Integrating AI models into established transaction systems
- Establishing feedback loops and model update mechanisms
- Exploring the role of generative AI in fraud investigation and SAR automation
Summary and Next Steps
Requirements
- Working knowledge of fraud risks and AML procedures
- Experience with data analysis or compliance reporting
- Fundamental familiarity with Python or analytics platforms
Target Audience
- Fraud risk professionals
- AML compliance teams
- Security managers
14 Hours
Testimonials (1)
i already have some reports that i know, i will use some of the prompts that looked at today
