AI for Credit Risk, Scoring & Lending Optimization Training Course

AI in Credit Risk: Core Concepts and Opportunities

• Comparing traditional credit risk models with AI-driven approaches
• Addressing challenges in credit evaluation, including bias, explainability, and fairness
• Examining real-world case studies of AI application in lending

Data Strategies for Credit Scoring Models

• Leveraging sources such as transactional, behavioral, and alternative data
• Performing data cleaning and feature engineering to support lending decisions
• Mitigating class imbalance and addressing data scarcity in risk prediction

Applying Machine Learning to Credit Scoring

• Utilizing logistic regression, decision trees, and random forests
• Enhancing scoring accuracy with gradient boosting frameworks like LightGBM and XGBoost
• Implementing best practices for model training, validation, and tuning

Optimizing Lending Workflows with AI

• Automating borrower segmentation and assessing loan risk
• Enhancing underwriting and approval processes through AI integration
• Driving dynamic pricing and interest rate optimization using machine learning

Model Interpretability and Responsible AI

• Explaining model predictions using SHAP and LIME techniques
• Ensuring fairness in credit models through bias detection and mitigation
• Maintaining compliance with regulatory frameworks such as ECOA and GDPR

Generative AI in Lending Contexts

• Employing LLMs for application review and document analysis
• Applying prompt engineering to improve borrower communication and gain insights
• Generating synthetic data for rigorous model testing

Strategy and Governance for AI in Credit

• Deciding between building internal AI capabilities and adopting external solutions
• Managing the model lifecycle and adhering to governance best practices
• Exploring future trends, including real-time credit scoring and open banking integration

Summary and Recommended Next Steps