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.
Course Outline
AI in Credit Risk: Foundations and Strategic Opportunities
- Comparing traditional versus AI-driven credit risk models.
- Addressing challenges in credit evaluation: bias, explainability, and fairness.
- Examining real-world case studies of AI in lending.
Data Strategies for Credit Scoring Models
- Leveraging transactional, behavioural, and alternative data sources.
- Performing data cleaning and feature engineering for lending decisions.
- Managing class imbalance and data scarcity in risk prediction.
Machine Learning Applications in Credit Scoring
- Utilising logistic regression, decision trees, and random forests.
- Enhancing scoring accuracy with gradient boosting (LightGBM, XGBoost).
- Mastering model training, validation, and tuning techniques.
AI-Enhanced Lending Workflows
- Automating borrower segmentation and loan risk assessment.
- Improving underwriting and approval processes with AI.
- Optimising 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.
- Achieving compliance with regulatory frameworks (e.g., ECOA, GDPR).
Generative AI in Lending Scenarios
- Deploying LLMs for application review and document analysis.
- Applying prompt engineering for borrower communication and insights.
- Generating synthetic data for model testing.
Strategy and Governance for AI in Credit
- Deciding between building internal AI capabilities versus adopting external solutions.
- Best practices for model lifecycle management and governance.
- Exploring future trends: real-time credit scoring and open banking integration.
Key Takeaways and Next Steps
Requirements
- A solid grasp of credit risk fundamentals.
- Practical experience with data analysis or business intelligence tools.
- Familiarity with Python or a strong willingness to learn basic syntax.
Target Audience
- Lending managers.
- Credit analysts.
- Fintech innovators.
14 Hours
Testimonials (1)
i already have some reports that i know, i will use some of the prompts that looked at today
