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Course Outline
AI in Credit Risk: Foundations and Opportunities
- Comparing traditional and AI-driven credit risk models
- Addressing challenges in credit evaluation: bias, explainability, and fairness
- Real-world case studies demonstrating AI in lending
Data for Credit Scoring Models
- Data sources: transactional, behavioral, and alternative data streams
- Data cleaning and feature engineering tailored for lending decisions
- Managing class imbalance and data scarcity in risk prediction
Machine Learning for Credit Scoring
- Techniques including logistic regression, decision trees, and random forests
- Gradient boosting (LightGBM, XGBoost) to enhance scoring accuracy
- Strategies for model training, validation, and tuning
AI-Driven Lending Workflows
- Automating borrower segmentation and loan risk assessment
- Enhancing underwriting and approval processes with AI
- Dynamic pricing and interest rate optimization using ML
Model Interpretability and Responsible AI
- Explaining predictions using SHAP and LIME
- Ensuring fairness in credit models: detecting and mitigating bias
- Aligning with regulatory frameworks such as ECOA and GDPR
Generative AI in Lending Scenarios
- Leveraging LLMs for application review and document analysis
- Prompt engineering for improved borrower communication and insights
- Generating synthetic data for robust 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
Requirements
- A solid foundation in credit risk fundamentals
- Practical experience with data analysis or business intelligence tools
- Familiarity with Python or a readiness to learn basic syntax
Target Audience
- Lending managers
- Credit analysts
- Fintech innovators
14 Hours
Testimonials (1)
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