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Course Outline

Foundations and Opportunities of AI in Credit Risk

  • Comparing traditional credit risk models with AI-powered alternatives
  • Addressing challenges in credit evaluation: bias, explainability, and fairness
  • Real-world case studies illustrating AI applications in lending

Data Requirements for Credit Scoring Models

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

Machine Learning Applications in Credit Scoring

  • Utilizing logistic regression, decision trees, and random forests
  • Enhancing scoring accuracy with gradient boosting (LightGBM, XGBoost)
  • 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
  • Optimizing dynamic pricing and interest rates using machine learning

Model Interpretability and Responsible AI

  • Explaining predictions using SHAP and LIME
  • Ensuring fairness in credit models: bias detection and mitigation
  • Adhering to regulatory frameworks (e.g., ECOA, 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
  • Emerging trends: real-time credit scoring and open banking integration

Summary and Next Steps

Requirements

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

Target Audience

  • Lending managers
  • Credit analysts
  • Fintech innovators
 14 Hours

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