Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
AI in Credit Risk: Foundations and Opportunities
- Comparing traditional versus AI-driven credit risk models
- Navigating challenges in credit evaluation: bias, explainability, and fairness
- Exploring real-world case studies of AI in lending
Data for Credit Scoring Models
- Data sources: transactional, behavioral, and alternative datasets
- Data cleaning and feature engineering for informed lending decisions
- Managing class imbalance and data scarcity in risk prediction
Machine Learning for Credit Scoring
- Techniques including logistic regression, decision trees, and random forests
- Utilizing gradient boosting (LightGBM, XGBoost) for enhanced scoring accuracy
- Best practices for model training, validation, and tuning
AI-Driven Lending Workflows
- Automating borrower segmentation and loan risk evaluation
- Enhancing underwriting and approval processes with AI
- Optimizing dynamic pricing and interest rates using ML
Model Interpretability and Responsible AI
- Explaining predictions using SHAP and LIME
- Ensuring fairness in credit models: detecting and mitigating bias
- Adhering to regulatory frameworks (e.g., ECOA, GDPR)
Generative AI in Lending Scenarios
- Leveraging LLMs for application review and document analysis
- Prompt engineering for borrower communication and insight generation
- Generating synthetic data for robust model testing
Strategy and Governance for AI in Credit
- Weighing internal AI capabilities against external solutions
- Best practices in model lifecycle management and governance
- 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 tools
- Knowledge of Python or a readiness to learn basic syntax
Target Audience
- Lending managers
- Credit analysts
- Fintech innovators
Testimonials (1)
i already have some reports that i know, i will use some of the prompts that looked at today
