Building a Bias-Free AI Credit Model

Learn practical strategies for developing, testing, and maintaining AI credit models that comply with fair lending requirements and avoid disparate impact.

Webinar Overview

This focused 30-minute presentation addresses one of the most critical challenges in AI adoption: ensuring your AI and machine learning credit models comply with fair lending laws and avoid discriminatory bias.

Drawing from RegVizion's extensive experience conducting AI bias audits and fair lending assessments, this webinar provides practical, actionable strategies for building and maintaining bias-free AI models.

What You'll Learn

Fair Lending Fundamentals for AI

ECOA and Fair Housing Act application to AI models
Disparate treatment vs. disparate impact
"No fancy technology exemption" principle
Recent regulatory guidance and enforcement actions

Sources of Bias in AI Models

Training Data Bias
- Historical discrimination embedded in data
- Selection bias in data collection
- Class imbalance issues
- Missing data patterns
Algorithm Bias
- Feature selection and engineering
- Model architecture choices
- Optimization objectives
- Hyperparameter decisions
Deployment Bias
- Threshold selection and calibration
- Population drift
- Feedback loops
- Application process differences

Bias Detection Methodologies

Disparate impact ratio (80% rule)
Standardized mean difference (SMD)
Equal opportunity and equalized odds
Calibration across groups
Using BISG for protected class estimation

Bias Mitigation Techniques

Pre-Processing Approaches:

Data reweighting and resampling
Feature transformation and removal
Synthetic data generation
Historical bias correction

In-Processing Approaches:

Fairness constraints in optimization
Adversarial debiasing
Regularization techniques
Multi-objective learning

Post-Processing Approaches:

Threshold optimization by group
Calibration adjustment
Score transformation
Decision rule modification

Less Discriminatory Alternatives (LDA)

Regulatory requirement to seek LDAs
Methodology for identifying alternatives
Testing and comparing alternatives
Documentation requirements

Real-World Case Studies

Case Study 1: Credit Card Approval Model

Challenge: 15% disparate impact against minority applicants
Root Cause: Training data from historically biased decisions
Solution: Data reweighting + fairness constraints
Result: Eliminated disparate impact while maintaining accuracy

Case Study 2: Small Business Lending Model

Challenge: Proxy discrimination through zip code features
Root Cause: Geographic features correlated with race
Solution: Feature engineering + alternative data sources
Result: Improved fairness without sacrificing performance

Case Study 3: Mortgage Underwriting Model

Challenge: Inability to explain AI decisions for adverse actions
Root Cause: Complex ensemble model architecture
Solution: SHAP-based explainability + surrogate model
Result: Regulatory-compliant adverse action reasons

Practical Tools and Techniques

Testing Tools Demonstrated:

Aequitas (bias audit toolkit)
Fairlearn (mitigation algorithms)
AI Fairness 360 (IBM toolkit)
SHAP for explainability
Custom testing frameworks

Metrics Deep-Dive:

When to use each fairness metric
Trade-offs between fairness definitions
Statistical significance testing
Monitoring metrics over time

Who Should Attend

Essential For:

Data Scientists developing credit models
Model Risk Managers overseeing AI
Fair Lending Officers
Compliance Officers
Model Validators

Also Valuable For:

Chief Risk Officers
Chief Information Officers
Legal Counsel
Board Risk Committee Members

Key Takeaways

Participants will learn to:

Identify potential sources of bias before model development Implement bias testing at each stage of model lifecycle Apply appropriate bias mitigation techniques Conduct less discriminatory alternative analysis Document fairness testing for regulatory examinations Balance fairness with model performance

Webinar Structure

Part 1: Fair Lending Requirements (8 minutes)

Regulatory framework for AI
Recent CFPB and FDIC guidance
Enforcement trends
Documentation expectations

Part 2: Bias Detection Methods (10 minutes)

Testing methodologies
Practical tool demonstrations
Metric selection and interpretation
Statistical significance

Part 3: Mitigation Strategies (10 minutes)

Pre-, in-, and post-processing techniques
LDA identification and testing
Real-world implementation examples
Ongoing monitoring approaches

Part 4: Q&A Highlights (2 minutes)

Common questions and answers
Best practices summary
Resources for further learning

Materials Included

Participants receive:

Presentation slides (PDF)
Fair lending testing checklist
Python code samples for bias testing
Fairness metrics reference guide
LDA analysis template
Link to open-source toolkits

Why This Matters

Regulatory Reality:

Massachusetts AG's $2.5M AI lending settlement (2024)
CFPB actively examining AI models for bias
Fed warning of fair lending risks in AI
State AGs increasing enforcement activity

Business Impact:

Fair lending violations costly (millions in fines + restitution)
Reputational damage from discrimination allegations
Customer trust eroded by biased decisions
Competitive disadvantage if AI adoption delayed by compliance concerns

Presenter Expertise

Led by RegVizion's AI Governance Practice, bringing expertise in:

Fair lending compliance and testing
AI/ML model validation and audit
Disparate impact analysis
Bias mitigation implementation
Regulatory examination support

Request Access to This Webinar

Duration: 30 minutes Format: Recorded presentation with tool demonstrations Delivery: Secure streaming link via email within 24 hours Cost: Complimentary for financial institutions

To request access, complete the form below or contact us directly.

Related Resources

Next Steps

After completing this webinar, consider:

SR 11-7 Model Validation Introduction (45-minute webinar)
Scheduling an AI bias audit for your models
Consulting on LDA analysis for identified disparate impact

Concerned about bias in your AI credit models? RegVizion provides comprehensive AI bias audits, fair lending testing, and remediation support. Contact us for a confidential assessment.