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 evaluation, including bias, explainability, and fairness.
• Examining real-world case studies of AI applications in lending.

Data for Credit Scoring Models

• Data sources: transactional, behavioral, and alternative datasets.
• Data cleansing and feature engineering tailored for lending decisions.
• Managing class imbalance and data scarcity in risk prediction.

Machine Learning for Credit Scoring

• Algorithms such as logistic regression, decision trees, and random forests.
• Enhancing scoring accuracy with gradient boosting (LightGBM, XGBoost).
• Techniques for model training, validation, and hyperparameter tuning.

AI-Driven Lending Workflows

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

Model Interpretability and Responsible AI

• Explaining predictions using SHAP and LIME.
• Ensuring fairness in credit models through bias detection and mitigation.
• Aligning with regulatory frameworks such as ECOA and GDPR.

Generative AI in Lending Scenarios

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

Strategy and Governance for AI in Credit

• Deciding between building internal AI capabilities and adopting external solutions.
• Best practices for model lifecycle management and governance.
• Future trends: real-time credit scoring and open banking integration.

Summary and Next Steps