Objectives:

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

• Interpret the key statutes, regulations, and judicial precedents governing **fair lending** and anti-discrimination in financial services.
• Differentiate between the legal concepts of **disparate treatment** and **disparate impact** in lending decisions.
• Identify and assess the specific prohibited bases for discrimination in credit underwriting and pricing.
• Develop and implement robust **fair lending compliance programs**, monitoring systems, and internal controls.
• Analyze lending data using statistical and qualitative methods to detect potential patterns of discrimination.
• Evaluate the fair lending risks inherent in new technologies, such as **AI/Machine Learning** in credit scoring and loan application processing.
• Apply best practices for documenting lending decisions and handling consumer complaints related to alleged discrimination.
• Understand the regulatory enforcement process for fair lending violations and the potential for penalties and remediation.

• Credit Underwriters and Loan Officers
• Compliance Officers and Fair Lending Specialists
• Internal Auditors and Risk Managers
• Legal Counsel and Regulatory Affairs Professionals
• Data Scientists and Analysts involved in Credit Scoring Model Development
• Senior Management responsible for Credit Policy
• Community Development and Financial Inclusion Officers
• Regulatory Examiners from Supervisory Authorities

• Case Studies analyzing regulatory examinations and enforcement actions.
• Hands-on Group Activities involving data-driven disparity testing scenarios.
• Discussions on ethical considerations of using alternative data in credit.
• Individual Exercises in developing a Fair Lending Risk Assessment matrix.
• Role-playing a credit committee debate over a policy change's impact.
• Statistical model overview for non-data scientists.

Unit 1: Foundations of Fair Lending Law

• Historical context and the moral/economic imperative for fair lending.
• Overview of core anti-discrimination statutes (e.g., in US, UK, EU, or regional equivalents).
• Defining protected classes and prohibited bases for discrimination.
• Scope of the law: Application, underwriting, pricing, and servicing.

• Understanding **Disparate Treatment** (intentional discrimination).
• Analyzing **Disparate Impact** (neutral policy with discriminatory effect).
• The four-part test for disparate impact and the "business necessity" defense.
• Illustrative case law and judicial interpretation of key statutes.

Unit 2: Fair Lending Compliance Program Design

• Elements of a robust and defensible **Fair Lending Compliance Management System (CMS)**.
• Developing a clear, written, non-discriminatory credit policy.
• Training requirements for all relevant staff (underwriters, marketers, sales).
• Role of the Board and Senior Management oversight.

• Conducting a comprehensive **Fair Lending Risk Assessment**.
• Designing and executing annual Fair Lending compliance testing programs.
• Reviewing marketing and advertising practices for potential targeting issues.
• Monitoring employee compensation and incentive structures for risk.

Unit 3: Data Analysis for Fair Lending Audits

• Data requirements for fair lending analysis (e.g., application, loan terms, demographics).
• Data cleaning, integrity checks, and ensuring accurate reporting.
• Selecting the appropriate comparator groups for analysis.
• Using statistical tools (e.g., regression analysis) to measure disparities.

• Analyzing pricing disparities (interest rates, fees, points).
• Reviewing underwriting overrides and exception practices.
• Examining redlining risks and branch/ATM location strategy.
• Qualitative file review and root cause analysis of detected disparities.

Unit 4: Technology, AI, and Future Risks

• Understanding how **Machine Learning (ML)** models can perpetuate bias.
• Techniques for mitigating bias in data selection and model training.
• The concept of "model explainability" (XAI) in fair lending.
• Regulatory guidance on the use of non-traditional data sources.

Unit 5: Enforcement, Remediation, and Best Practices

• The process of a regulatory Fair Lending Examination or Audit.
• Best practices for responding to regulatory findings and deficiencies.
• Developing effective **remediation plans** for proven violations.
• Case studies of major Fair Lending settlements and consent orders.