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Course Outline
AI in Credit Risk: Foundations and Opportunities
- Comparing traditional credit risk models with AI-powered solutions
- Addressing challenges in credit evaluation: bias, explainability, and fairness
- Real-world case studies demonstrating AI applications in lending
Data for Credit Scoring Models
- Data sources: transactional, behavioral, and alternative data
- Data cleaning and feature engineering for lending decisions
- Managing class imbalance and data scarcity in risk prediction
Machine Learning for Credit Scoring
- Utilizing logistic regression, decision trees, and random forests
- Enhancing scoring accuracy with gradient boosting (LightGBM, XGBoost)
- Techniques for model training, validation, and tuning
AI-Driven Lending Workflows
- Automating borrower segmentation and loan risk assessment
- AI-enhanced underwriting and approval processes
- Dynamic pricing and interest rate optimization using ML
Model Interpretability and Responsible AI
- Explaining predictions with SHAP and LIME
- Ensuring fairness in credit models: bias detection and mitigation
- Compliance with regulatory frameworks (e.g. ECOA, GDPR)
Generative AI in Lending Scenarios
- Leveraging LLMs for application review and document analysis
- Prompt engineering for borrower communication and insights
- Generating synthetic data for model testing
Strategy and Governance for AI in Credit
- Building internal AI capabilities 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
Requirements
- A solid grasp of credit risk fundamentals
- Proficiency in data analysis or business intelligence tools
- Basic knowledge of Python or a readiness to learn fundamental syntax
Target Audience
- Lending managers
- Credit analysts
- Fintech innovators
14 Hours
Testimonials (1)
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