AI for Credit Risk, Scoring & Lending Optimization Training Course

AI in Credit Risk: Foundations and Opportunities

• Comparative analysis of traditional versus AI-driven credit risk models
• Addressing credit evaluation challenges: bias, explainability, and fairness
• Real-world case studies on AI application in lending

Data for Credit Scoring Models

• Data sources: transactional, behavioral, and alternative datasets
• Data cleaning and feature engineering tailored for lending decisions
• Strategies for handling class imbalance and data scarcity in risk prediction

Machine Learning for Credit Scoring

• Foundational algorithms: Logistic regression, decision trees, and random forests
• Leveraging Gradient boosting (LightGBM, XGBoost) for enhanced scoring accuracy
• Techniques for model training, validation, and parameter tuning

AI-Driven Lending Workflows

• Automating borrower segmentation and assessing loan risk
• Enhancing underwriting and approval processes with AI
• Implementing dynamic pricing and interest rate optimization via ML

Model Interpretability and Responsible AI

• Explaining predictions using SHAP and LIME frameworks
• Ensuring fairness in credit models through bias detection and mitigation
• Adhering to regulatory frameworks (e.g. ECOA, GDPR)

Generative AI in Lending Scenarios

• Utilizing LLMs for application review and document analysis
• Prompt engineering for effective borrower communication and insight generation
• Generating synthetic data for rigorous model testing

Strategy and Governance for AI in Credit

• Evaluating the build-vs-buy decision for internal AI capabilities
• Best practices in model lifecycle management and governance
• Future trends: real-time credit scoring and open banking integration

Summary and Next Steps