Trust, Technology & Transparency: AI in Financial Law

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About Course

This one-hour CLE course delivers a targeted, practical overview of how Artificial Intelligence (AI) is transforming the banking, finance, and insurance industries—areas where regulation, compliance, and risk management are paramount. Participants will learn how AI is applied in fraud detection, transaction monitoring, anti-money laundering (AML) processes, risk modeling, and claims management, as well as in meeting financial and insurance regulatory obligations. 

The session emphasizes real-world applications and the legal frameworks that govern them, offering insights into the use of industry-specific AI tools versus general-purpose systems. The course will also address critical challenges, including data privacy, algorithmic bias, auditability, and compliance with global and domestic regulations such as the EU AI Act, FTC guidance, and U.S. state-level laws. 

By the end, participants will have a clear understanding of both the capabilities and the legal risks of AI in this high-stakes sector—enabling them to advise clients, implement AI responsibly, and maintain compliance with evolving regulations.

Learning Objectives 

Participants will be able to: 

  • Identify core AI technologies shaping the banking, finance, and insurance sectors. 
  • Evaluate use cases such as fraud detection, AML compliance, and risk assessment. 
  • Distinguish between general-purpose AI and financial-grade, regulatory-compliant AI tools. 
  • Recognize legal risks and regulatory requirements for AI adoption. 
  • Apply best practices for AI governance and internal compliance policies. 
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What Will You Learn?

  • Implement AI in fraud detection and claims analysis workflows.
  • Use AI to enhance compliance reporting and monitoring.
  • Mitigate legal risks by understanding bias, privacy, and explainability requirements.

Course Content

Welcome and Introduction
Presenter introduction and course overview Housekeeping and materials reference

Foundations of AI in Financial Services
Key AI types in finance (ANI, LLMs, AI agents) Overview of adoption trends in banking and insurance

AI in Fraud Detection, Risk Management & AML
Transaction monitoring and anomaly detection Automated KYC and AML processes Predictive analytics for credit and market risk

Regulatory Compliance & Legal Risks
Meeting regulatory reporting requirements with AI Data privacy, confidentiality, and privilege concerns Algorithmic bias and explainability obligations

Implementation Strategies & Best Practices
Selecting financial-grade AI tools Vendor due diligence and contract considerations Internal AI governance policies and audit processes

Case Study & Q&A
Real-world AI adoption example in finance or insurance Audience questions and discussion

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