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Course Outline
AI in Credit Risk: Core Concepts and Potential
- Comparing traditional models with AI-driven credit risk frameworks
- Addressing challenges in credit evaluation: bias, explainability, and fairness
- Examining real-world case studies of AI in lending
Data for Credit Scoring Models
- Identifying sources: transactional, behavioral, and alternative data
- Data cleansing and feature engineering for informed lending decisions
- Managing class imbalance and data scarcity in risk prediction
Machine Learning for Credit Scoring
- Exploring logistic regression, decision trees, and random forests
- Utilizing gradient boosting (LightGBM, XGBoost) to enhance scoring precision
- Techniques for model training, validation, and optimization
AI-Driven Lending Workflows
- Streamlining borrower segmentation and loan risk evaluation
- Enhancing underwriting and approval processes with AI
- Implementing dynamic pricing and interest rate optimization via ML
Model Interpretability and Responsible AI
- Clarifying 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 Scenarios
- Employing LLMs for application review and document processing
- Applying prompt engineering for borrower communication and insights
- Generating synthetic data for model testing
Strategy and Governance for AI in Credit
- Evaluating internal AI capabilities against external solutions
- Best practices for model lifecycle management and governance
- Looking ahead: real-time credit scoring and open banking integration
Conclusion and Future Actions
Requirements
- A solid grasp of credit risk fundamentals
- Prior experience with data analysis or business intelligence tools
- Knowledge of Python or a readiness to learn basic syntax
Target Audience
- Lending managers
- Credit analysts
- Fintech innovators
14 Hours
Testimonials (1)
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