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Course Outline
Introduction to AI in Combating Financial Crime
- The landscape of fraud and AML in the digital finance era
- Comparing traditional methods with AI-driven approaches
- Insights from case studies involving Mastercard, JPMorgan, and global banking institutions
Machine Learning for Transaction Surveillance
- Supervised learning techniques for risk assessment and classification
- Unsupervised learning methods for spotting anomalies
- Generating real-time alerts and processing data streams
Graph Analytics and Network Risk Identification
- Mapping connections between entities and transactions
- Identifying intricate fraud schemes through graph AI
- Practical exercises using Neo4j or comparable tools
Natural Language Processing for AML Compliance
- Text mining applications in Customer Due Diligence (CDD)
- Scanning watchlists using Named Entity Recognition (NER)
- Prompt-based document review and handling Suspicious Activity Reports (SARs)
Model Governance and Interpretability
- Constructing models that are explainable and subject to audit
- Identifying and mitigating bias in fraud detection algorithms
- Applying XAI techniques within compliance frameworks
Ethics, Regulatory Frameworks, and Model Risk
- Adhering to AML and KYC standards (e.g., FATF, FinCEN, EBA)
- Ethical considerations in surveillance and customer monitoring
- Maintaining reporting standards and ensuring regulatory auditability
Deployment Tactics and Emerging Trends
- Incorporating AI models into current transaction systems
- Establishing feedback loops and model update mechanisms
- The role of generative AI in fraud investigation and SAR automation
Conclusion and Recommendations
Requirements
- Familiarity with fraud risks and AML protocols
- Background in data analysis or compliance reporting
- Foundational knowledge of Python or analytics platforms
Target Audience
- Fraud risk specialists
- AML compliance personnel
- 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
