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Course Outline
AI in Credit Risk: Foundations and Opportunities
- Comparing traditional vs. AI-driven credit risk models.
- Addressing challenges in credit evaluation: bias, explainability, and fairness.
- Examining real-world case studies of AI in lending.
Data for Credit Scoring Models
- Data sources: transactional, behavioral, and alternative datasets.
- Cleaning data and engineering features for informed lending decisions.
- Managing class imbalance and data scarcity in risk prediction.
Machine Learning for Credit Scoring
- Logistic regression, decision trees, and random forests.
- Utilizing gradient boosting (LightGBM, XGBoost) to enhance scoring accuracy.
- Techniques for model training, validation, and tuning.
AI-Driven Lending Workflows
- Automating borrower segmentation and loan risk assessment.
- Enhancing underwriting and approval processes with AI.
- Applying ML for dynamic pricing and interest rate optimization.
Model Interpretability and Responsible AI
- Using SHAP and LIME to explain predictions.
- 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 communication and insight generation.
- Generating synthetic data for model testing.
Strategy and Governance for AI in Credit
- Deciding between building internal AI capabilities vs. 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 foundation in credit risk principles.
- Proficiency with data analysis or business intelligence tools.
- Familiarity with Python or a commitment to learning its basic syntax.
Target Audience
- Lending managers.
- Credit analysts.
- Fintech innovators.
14 Hours
Testimonials (1)
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