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Cursusaanbod
AI in Credit Risk: Foundations and Opportunities
- Traditional vs AI-powered credit risk models
- Challenges in credit evaluation: bias, explainability, and fairness
- Real-world case studies in AI for lending
Data for Credit Scoring Models
- Sources: transactional, behavioral, and alternative data
- Data cleaning and feature engineering for lending decisions
- Handling class imbalance and data scarcity in risk prediction
Machine Learning for Credit Scoring
- Logistic regression, decision trees, and random forests
- Gradient boosting (LightGBM, XGBoost) for scoring accuracy
- Model training, validation, and tuning techniques
AI-Driven Lending Workflows
- Automating borrower segmentation and loan risk assessment
- AI-enhanced underwriting and approval processes
- Dynamic pricing and interest rate optimization using ML
Model Interpretability and Responsible AI
- Explaining predictions with SHAP and LIME
- Fairness in credit models: bias detection and mitigation
- Compliance with regulatory frameworks (e.g. ECOA, GDPR)
Generative AI in Lending Scenarios
- Using LLMs for application review and document analysis
- Prompt engineering for borrower communication and insights
- Synthetic data generation for model testing
Strategy and Governance for AI in Credit
- Building internal AI capabilities vs external solutions
- Model lifecycle management and governance best practices
- Future trends: real-time credit scoring, open banking integration
Summary and Next Steps
Vereisten
- An understanding of credit risk fundamentals
- Experience with data analysis or business intelligence tools
- Familiarity with Python or willingness to learn basic syntax
Audience
- Lending managers
- Credit analysts
- Fintech innovators
14 Uren
