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Course Outline
AI in Credit Risk: Foundations and Opportunities
- Comparing traditional versus AI-driven credit risk models
- Key challenges in credit evaluation: bias, explainability, and fairness
- Real-world case studies involving AI in lending
Data for Credit Scoring Models
- Data sources: transactional, behavioral, and alternative data
- Data cleaning and feature engineering for lending decisions
- Addressing 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 tuning
AI-Driven Lending Workflows
- Automating borrower segmentation and loan risk assessment
- Strengthening underwriting and approval processes with AI
- Optimizing dynamic pricing and interest rates using ML
Model Interpretability and Responsible AI
- Explaining predictions using SHAP and LIME
- Ensuring fairness in credit models through bias detection and mitigation
- Maintaining compliance with regulatory frameworks (e.g., ECOA, GDPR)
Generative AI in Lending Scenarios
- Utilizing 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
- Developing 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 understanding of credit risk principles
- Practical experience with data analysis or business intelligence tools
- Basic familiarity with Python or a readiness to learn fundamental syntax
Target Audience
- Lending managers
- Credit analysts
- Fintech innovators
14 Hours
Testimonials (1)
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