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Course Outline
AI in Credit Risk: Foundations and Opportunities
- Traditional models vs AI-driven credit risk frameworks
- Navigating challenges in credit assessment: bias, explainability, and fairness
- Real-world case studies illustrating AI in lending
Data for Credit Scoring Models
- Data sources: transactional, behavioural, and alternative datasets
- Data cleansing and feature engineering for informed lending decisions
- Addressing class imbalance and data scarcity in risk forecasting
Machine Learning for Credit Scoring
- Logistic regression, decision trees, and random forests
- Gradient boosting techniques (LightGBM, XGBoost) for enhanced scoring precision
- Strategies for model training, validation, and parameter tuning
AI-Driven Lending Workflows
- Automating borrower segmentation and loan risk evaluation
- AI-assisted underwriting and approval procedures
- Dynamic pricing and interest rate optimisation via ML
Model Interpretability and Responsible AI
- Explaining predictions using SHAP and LIME
- Ensuring fairness in credit models: detecting and mitigating bias
- Adherence to regulatory standards (e.g. ECOA, GDPR)
Generative AI in Lending Scenarios
- Leveraging LLMs for application review and document analysis
- Prompt engineering for effective borrower communication and insight generation
- Synthetic data creation for model testing
Strategy and Governance for AI in Credit
- Developing in-house AI capabilities vs adopting external solutions
- Model lifecycle management and governance best practices
- Emerging trends: real-time credit scoring and open banking integration
Summary and Next Steps
Requirements
- A solid grasp of credit risk fundamentals
- Practical experience with data analysis or business intelligence platforms
- Basic knowledge of Python or a readiness to learn fundamental syntax
Intended Audience
- Lending managers
- Credit analysts
- Fintech innovators
14 Hours
Testimonials (1)
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