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Duration 14 hours
Course Outline
AI in Credit Risk: Core Principles and Potential
- Comparing traditional and AI-driven credit risk models
- Addressing credit evaluation hurdles: bias, interpretability, and fairness
- Practical case studies of AI applications in lending
Data Foundations for Credit Scoring Models
- Data sources: transactional, behavioral, and alternative datasets
- Data cleansing and feature engineering for lending judgments
- Managing class imbalance and data limitations in risk prediction
Machine Learning Techniques for Credit Scoring
- Logistic regression, decision trees, and random forests
- Gradient boosting methods (LightGBM, XGBoost) for enhanced accuracy
- Model training, validation, and parameter tuning
AI-Powered Lending Processes
- Automating borrower segmentation and loan risk evaluation
- Enhancing underwriting and approval workflows with AI
- Dynamic pricing and interest rate optimization via machine learning
Model Transparency and Ethical AI
- Interpreting predictions using SHAP and LIME
- Ensuring fairness in credit models: identifying and reducing bias
- Aligning with regulatory standards (e.g., ECOA, GDPR)
Generative AI in Lending Contexts
- Leveraging LLMs for application assessment and document processing
- Prompt engineering for improved borrower interaction and insights
- Generating synthetic data for model validation
Strategic Oversight and AI Governance in Credit
- Developing in-house AI capabilities versus adopting external solutions
- Best practices for model lifecycle management and governance
- Emerging trends: real-time credit scoring and open banking integration
Recap and Future Directions
Requirements
- A solid grasp of credit risk principles
- Proficiency with data analytics or business intelligence platforms
- Basic knowledge of Python or an eagerness to learn fundamental syntax
Target Audience
- Lending managers
- Credit analysts
- Fintech innovators
Testimonials (1)
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