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Course Outline
AI in Credit Risk: Foundations and Opportunities
- Contrasting traditional versus AI-powered credit risk models
- Addressing challenges in credit evaluation: bias, explainability, and fairness
- Examining real-world case studies in AI-driven lending
Data for Credit Scoring Models
- Data sources: transactional, behavioural, and alternative data sets
- Data cleaning and feature engineering for informed lending decisions
- Managing class imbalance and data scarcity in risk prediction
Machine Learning for Credit Scoring
- Core algorithms: logistic regression, decision trees, and random forests
- Utilising gradient boosting (LightGBM, XGBoost) to enhance scoring accuracy
- Techniques for model training, validation, and hyperparameter tuning
AI-Driven Lending Workflows
- Automating borrower segmentation and loan risk assessment
- Streamlining underwriting and approval processes with AI
- Implementing dynamic pricing and interest rate optimisation using ML
Model Interpretability and Responsible AI
- Explaining predictions using SHAP and LIME
- Ensuring fairness in credit models: detecting and mitigating bias
- Aligning with regulatory frameworks (e.g., ECOA, GDPR)
Generative AI in Lending Scenarios
- Leveraging LLMs for application review and document analysis
- Applying prompt engineering for enhanced borrower communication and insights
- Generating synthetic data for robust model testing
Strategy and Governance for AI in Credit
- Weighing the benefits of building internal AI capabilities versus adopting external solutions
- Best practices for model lifecycle management and governance
- Emerging trends: real-time credit scoring and open banking integration
Summary and Next Steps
Requirements
- A foundational grasp of credit risk principles
- Practical experience with data analysis or business intelligence tools
- Basic familiarity with Python, or a readiness to learn fundamental syntax
Target Audience
- Lending managers
- Credit analysts
- Fintech innovators
14 Hours
Testimonials (1)
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