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Course Outline

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

  • Comparing traditional versus AI-powered credit risk models. <\/li>
  • Addressing challenges in credit evaluation: bias, explainability, and fairness. <\/li>
  • Real-world case studies demonstrating AI in lending. <\/li> <\/ul>

    Data Preparation for Credit Scoring Models <\/p>

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

      Machine Learning Applications for Credit Scoring <\/p>

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

        AI-Driven Lending Workflows <\/p>

        • Automating borrower segmentation and loan risk assessment. <\/li>
        • Enhancing underwriting and approval processes with AI. <\/li>
        • Optimizing dynamic pricing and interest rates using machine learning. <\/li> <\/ul>

          Model Interpretability and Responsible AI Practices <\/p>

          • Explaining predictions using SHAP and LIME. <\/li>
          • Ensuring fairness in credit models through bias detection and mitigation. <\/li>
          • Adhering to regulatory frameworks (e.g., ECOA, GDPR). <\/li> <\/ul>

            Generative AI in Lending Scenarios <\/p>

            • Utilizing Large Language Models (LLMs) for application review and document analysis. <\/li>
            • Applying prompt engineering for borrower communication and insights. <\/li>
            • Generating synthetic data for model testing. <\/li> <\/ul>

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

              • Developing internal AI capabilities versus adopting external solutions. <\/li>
              • Best practices for model lifecycle management and governance. <\/li>
              • Emerging trends: real-time credit scoring and open banking integration. <\/li> <\/ul>

                Summary and Next Steps <\/p>

Requirements

  • A foundational understanding of credit risk principles. <\/li>
  • Experience with data analysis or business intelligence tools. <\/li>
  • Familiarity with Python, or a readiness to learn basic syntax. <\/li> <\/ul>

    Target Audience<\/strong> <\/p>

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

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