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Course Outline
AI in Credit Risk: Core Principles and Potential
- Comparing traditional approaches with AI-driven credit risk models
- Addressing credit evaluation challenges: bias, explainability, and fairness
- Examining real-world case studies of AI application in lending
Data Sources for Credit Scoring Models
- Leveraging transactional, behavioral, and alternative data sources
- Data cleansing and feature engineering tailored for lending decisions
- Managing class imbalance and data scarcity in risk prediction scenarios
Machine Learning Applications in Credit Scoring
- Utilizing logistic regression, decision trees, and random forests
- Enhancing scoring accuracy with gradient boosting (LightGBM, XGBoost)
- Techniques for model training, validation, and parameter tuning
AI-Integrated Lending Processes
- Automating borrower segmentation and assessing loan risk
- Enhancing underwriting and approval workflows with AI
- Implementing dynamic pricing and interest rate optimization via ML
Model Interpretability and Ethical AI
- Explaining model predictions using SHAP and LIME frameworks
- Ensuring fairness in credit models: detecting and mitigating bias
- Adhering to regulatory standards (e.g., ECOA, GDPR)
Generative AI in Lending Contexts
- Employing LLMs for application review and document analysis
- Prompt engineering strategies for borrower communication and insight generation
- Generating synthetic data for robust model testing
Strategic Governance of AI in Credit
- Deciding between building internal AI capabilities and adopting external solutions
- Best practices for model lifecycle management and governance
- Emerging trends: real-time credit scoring and open banking integration
Recap and Subsequent Actions
Requirements
- A solid grasp of credit risk fundamentals
- Practical experience with data analysis or business intelligence tools
- Proficiency in Python or a strong willingness to learn basic syntax
Target Audience
- Lending managers
- Credit analysts
- Fintech innovators
14 Hours
Testimonials (1)
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