Get in Touch

Course Outline

AI in Credit Risk: Foundations and Opportunities

  • Comparing traditional credit risk models with AI-powered ones.
  • Challenges in credit evaluation: addressing bias, explainability, and fairness.
  • Real-world case studies demonstrating AI applications in lending.

Data Requirements for Credit Scoring Models

  • Data sources: transactional, behavioral, and alternative data.
  • Data cleaning and feature engineering techniques for lending decisions.
  • Managing class imbalance and data scarcity in risk prediction.

Machine Learning Techniques for Credit Scoring

  • Logistic regression, decision trees, and random forests.
  • Gradient boosting methods (LightGBM, XGBoost) for enhancing scoring accuracy.
  • Techniques for model training, validation, and tuning.

AI-Driven Lending Workflows

  • Automating borrower segmentation and loan risk assessment.
  • Enhancing underwriting and approval processes with AI.
  • Dynamic pricing and interest rate optimization using machine learning.

Model Interpretability and Responsible AI Practices

  • Explaining predictions using SHAP and LIME frameworks.
  • Ensuring fairness in credit models: detecting and mitigating bias.
  • Adhering to regulatory frameworks such as ECOA and GDPR.

Generative AI in Lending Scenarios

  • Utilizing Large Language Models (LLMs) for application review and document analysis.
  • Prompt engineering for borrower communication and insights.
  • Generating synthetic data for model testing.

Strategy and Governance for AI in Credit

  • Developing internal AI capabilities versus adopting external solutions.
  • Best practices for model lifecycle management and governance.
  • Future trends: real-time credit scoring and open banking integration.

Summary and Next Steps

Requirements

  • A foundational understanding of credit risk principles.
  • Practical experience with data analysis or business intelligence tools.
  • Familiarity with Python, or a willingness to learn basic programming syntax.

Target Audience

  • Lending managers.
  • Credit analysts.
  • Fintech innovators.
 14 Hours

Number of participants


Price per participant

Testimonials (1)

Upcoming Courses

Related Categories