AI for Credit Risk, Scoring & Lending Optimization Training Course

AI in Credit Risk: Foundations and Opportunities

• Comparing traditional versus AI-driven credit risk models
• Addressing challenges in credit assessment: bias, explainability, and fairness
• Case studies on real-world AI applications in lending

Data for Credit Scoring Models

• Data sources: transactional, behavioral, and alternative datasets
• Data preparation and feature engineering for lending decisions
• Managing class imbalance and limited data in risk prediction

Machine Learning for Credit Scoring

• Algorithms including logistic regression, decision trees, and random forests
• Applying gradient boosting (LightGBM, XGBoost) to enhance scoring precision
• Techniques for model training, validation, and optimization

AI-Driven Lending Workflows

• Automating borrower segmentation and loan risk evaluation
• Enhancing underwriting and approval procedures with AI
• Optimizing dynamic pricing and interest rates using machine learning

Model Interpretability and Responsible AI

• Interpreting predictions using SHAP and LIME
• Ensuring fairness in credit models: identifying and mitigating bias
• Adhering to regulatory standards such as ECOA and GDPR

Generative AI in Lending Scenarios

• Leveraging LLMs for application review and document analysis
• Prompt engineering for borrower communication and insight generation
• Generating synthetic data for model testing purposes

Strategy and Governance for AI in Credit

• Developing internal AI competencies versus adopting external solutions
• Best practices for model lifecycle management and governance
• Emerging trends: real-time credit scoring and open banking integration

Summary and Next Steps