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Course Outline
AI in Credit Risk: Core Principles and Potential
- Contrasting traditional credit risk models with AI-driven approaches
- Addressing credit evaluation hurdles: bias, explainability, and fairness
- Real-world case studies on AI in lending
Data Sources for Credit Scoring Models
- Inputs: transactional, behavioral, and alternative datasets
- Data refinement and feature engineering for lending decisions
- Managing class imbalance and data scarcity in risk prediction
Machine Learning in Credit Scoring
- Logistic regression, decision trees, and random forests
- Gradient boosting (LightGBM, XGBoost) to boost scoring precision
- Techniques for model training, validation, and optimization
AI-Powered Lending Workflows
- Automating borrower segmentation and loan risk evaluation
- Enhancing underwriting and approval processes with AI
- Dynamic pricing and interest rate optimization via ML
Model Interpretability and Responsible AI
- Explaining predictions using SHAP and LIME
- Ensuring fairness in credit models: identifying and mitigating bias
- Adhering to regulatory frameworks (e.g., ECOA, GDPR)
Generative AI in Lending Contexts
- Leveraging LLMs for application reviews and document analysis
- Prompt engineering for borrower engagement 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
- Future trends: real-time credit scoring and open banking integration
Summary and Next Steps
Requirements
- Solid grasp of credit risk basics
- Proficiency in data analysis or business intelligence tools
- Knowledge of Python or an eagerness to learn fundamental syntax
Target Audience
- Lending managers
- Credit analysts
- Fintech innovators
14 Hours
Testimonials (1)
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