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Course Outline

AI in Credit Risk: Foundations and Opportunities <\/p>

  • Traditional vs AI-powered credit risk models <\/li>
  • Challenges in credit evaluation: bias, explainability, and fairness <\/li>
  • Real-world case studies in AI for lending <\/li> <\/ul>

    Data for Credit Scoring Models <\/p>

    • Sources: transactional, behavioral, and alternative data <\/li>
    • Data cleaning and feature engineering for lending decisions <\/li>
    • Handling class imbalance and data scarcity in risk prediction <\/li> <\/ul>

      Machine Learning for Credit Scoring <\/p>

      • Logistic regression, decision trees, and random forests <\/li>
      • Gradient boosting (LightGBM, XGBoost) for scoring accuracy <\/li>
      • Model training, validation, and tuning techniques <\/li> <\/ul>

        AI-Driven Lending Workflows <\/p>

        • Automating borrower segmentation and loan risk assessment <\/li>
        • AI-enhanced underwriting and approval processes <\/li>
        • Dynamic pricing and interest rate optimization using ML <\/li> <\/ul>

          Model Interpretability and Responsible AI <\/p>

          • Explaining predictions with SHAP and LIME <\/li>
          • Fairness in credit models: bias detection and mitigation <\/li>
          • Compliance with regulatory frameworks (e.g. ECOA, GDPR) <\/li> <\/ul>

            Generative AI in Lending Scenarios <\/p>

            • Using LLMs for application review and document analysis <\/li>
            • Prompt engineering for borrower communication and insights <\/li>
            • Synthetic data generation for model testing <\/li> <\/ul>

              Strategy and Governance for AI in Credit <\/p>

              • Building internal AI capabilities vs external solutions <\/li>
              • Model lifecycle management and governance best practices <\/li>
              • Future trends: real-time credit scoring, open banking integration <\/li> <\/ul>

                Summary and Next Steps <\/p>

Requirements

  • An understanding of credit risk fundamentals <\/li>
  • Experience with data analysis or business intelligence tools <\/li>
  • Familiarity with Python or willingness to learn basic syntax <\/li> <\/ul>

    Audience<\/strong> <\/p>

    • Lending managers <\/li>
    • Credit analysts <\/li>
    • Fintech innovators <\/li> <\/ul>
 14 Hours

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