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Course Outline
AI in Credit Risk: Foundations and Opportunities
- Comparing traditional versus AI-driven credit risk models
- Challenges in credit assessment: bias, explainability, and fairness
- Real-world case studies of AI applications in lending
Data for Credit Scoring Models
- Data sources: transactional, behavioral, and alternative datasets
- Data cleaning and feature engineering for informed 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
- Leveraging gradient boosting (LightGBM, XGBoost) to enhance scoring accuracy
- Methods for model training, validation, and tuning
AI-Driven Lending Workflows
- Automating borrower segmentation and loan risk evaluation
- Enhancing underwriting and approval processes with AI
- Dynamic pricing and interest rate optimization using machine learning
Model Interpretability and Responsible AI
- Explaining predictions using SHAP and LIME
- Ensuring fairness in credit models: detecting and mitigating bias
- 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 model testing
Strategy and Governance for AI in Credit
- Building internal AI capabilities versus adopting external solutions
- Model lifecycle management and governance best practices
- Future trends: real-time credit scoring and open banking integration
Summary and Next Steps
Requirements
- A solid grasp of credit risk fundamentals
- Experience with data analysis or business intelligence tools
- Familiarity with Python or a willingness to learn basic syntax
Target Audience
- Lending managers
- Credit analysts
- Fintech innovators
14 Hours
Testimonials (1)
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