Objectives:

Upon completion of this course, participants will be able to:

• Analyze the application of **AI/Machine Learning** techniques (e.g., anomaly detection, predictive modeling) for financial crime and **fraud prevention**.
• Identify the inherent **consumer rights risks** in AI-driven fraud detection (e.g., false positives, algorithmic bias, privacy invasion).
• Design a data governance framework to ensure the **lawful and ethical use of consumer data** for fraud prevention purposes.
• Develop and implement strategies for minimizing **false positives** (incorrectly flagging legitimate transactions) and ensuring timely redress.
• Understand the regulatory requirements for consumer notification, explanation, and the **right to contest** automated fraud decisions.
• Evaluate the role of **Explainable AI (XAI)** in providing transparent justification for freezing or blocking consumer accounts.
• Assess the compliance challenges related to cross-border data sharing for global fraud detection purposes.
• Implement a robust internal review process for model validation, fairness testing, and error remediation in AI-driven systems.

• Financial Crime, Fraud, and AML Analysts and Managers
• Data Scientists and AI/ML Engineers working on Security/Risk Models
• Compliance Officers and Regulatory Affairs Professionals
• Internal Auditors and Operational Risk Managers
• Legal Counsel specializing in AI, Data Privacy, and Consumer Law
• Regulators focused on Market Conduct and Financial Integrity
• FinTech and Payments System Operators

• Case Studies analyzing instances of AI fraud models generating discriminatory false positives.
• Group Activities on designing a consumer notification and appeal process for a transaction block.
• Discussions on the ethics of using private data for fraud detection without explicit, fresh consent.
• Individual Exercises on developing a risk mitigation plan for reducing false positives in a payments model.
• Workshop on drafting internal policy requirements for XAI in a fraud detection system.
• Expert Q&A on the latest AI and machine learning techniques in financial security.

Unit 1: AI in Financial Crime Prevention

• Overview of traditional vs. AI-driven fraud detection systems (rule-based vs. machine learning).
• Machine learning techniques: Supervised, unsupervised, and deep learning for anomaly detection.
• Application to various fraud types: Payments fraud, identity theft, and synthetic identity fraud.
• The benefits of AI: Speed, scalability, and detection of novel fraud patterns.

• Defining the legal basis for using consumer transaction data for fraud detection (legitimate interest vs. consent).
• Data minimization and ensuring only necessary data is used for model training.
• Compliance with **Data Privacy** regulations (e.g., GDPR) for cross-border fraud data sharing.
• The use of alternative or non-traditional data sources for enhanced detection.

Unit 2: Consumer Rights and Algorithmic Errors

• The problem of **False Positives** (incorrectly flagged transactions) and their consumer impact.
• The consumer's **right to explanation** and notification for account freezes or transaction blocks.
• Designing a fast, accessible, and independent **internal appeal/redress mechanism** for false positives.
• Ensuring the principle of **due process** in automated fraud decision-making.

• How bias can inadvertently lead to discriminatory fraud flags for certain protected groups.
• Implementing fairness testing for fraud models to ensure equal error rates across demographics.
• The regulatory expectation for transparency in model selection and validation.
• The potential for AI systems to perpetuate or amplify financial exclusion.

Unit 3: Transparency, Recourse, and Explainability

• Applying **Explainable AI (XAI)** techniques to justify automated fraud decisions.
• Translating complex model rationales into clear, concise, and compliant consumer communication.
• Regulatory requirements for internal model documentation, auditability, and validation.
• The balance between transparency and protecting the secrecy of fraud detection logic.

Unit 4: Regulatory Oversight and Future Trends

• Developing a robust **Model Risk Management (MRM)** framework for AI fraud systems.
• The role of the compliance function in auditing the fairness and accuracy of fraud models.
• Regulatory guidance on the use of **Generative AI** and deepfakes by fraudsters and the policy response.
• Policy approaches for managing the trade-off between speed/security and consumer autonomy.