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Course Outline
AI in Credit Risk: Foundations and Prospects
- Comparing traditional credit risk models with AI-driven alternatives
- Addressing challenges in credit assessment: bias, explainability, and fairness
- Practical case studies on AI applications in lending
Data for Credit Scoring Models
- Data sources: transactional, behavioral, and alternative datasets
- Data refinement and feature engineering for lending decisions
- Managing class imbalance and limited data in risk prediction
Machine Learning for Credit Scoring
- Logistic regression, decision trees, and random forests
- Utilizing gradient boosting (LightGBM, XGBoost) to enhance scoring precision
- Techniques for model training, validation, and hyperparameter tuning
AI-Driven Lending Workflows
- Automating borrower segmentation and loan risk evaluation
- Enhancing underwriting and approval processes with AI
- Optimizing dynamic pricing and interest rates using ML
Model Interpretability and Responsible AI
- Clarifying predictions using SHAP and LIME frameworks
- Ensuring fairness in credit models: detecting and mitigating bias
- Adhering to regulatory frameworks (e.g., ECOA, GDPR)
Generative AI in Lending Scenarios
- Leveraging LLMs for application review and document analysis
- Prompt engineering for borrower engagement and insight generation
- Creating synthetic data for model testing purposes
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
- Foundational knowledge of credit risk principles
- Proficiency with data analysis or business intelligence platforms
- Basic familiarity with Python or the willingness to learn core syntax
Target Audience
- Lending managers
- Credit analysts
- Fintech innovators
14 Hours
Testimonials (1)
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