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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>
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Real-world case studies demonstrating AI in lending.
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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>
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Managing class imbalance and data scarcity in risk prediction.
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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>
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Techniques for model training, validation, and tuning.
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AI-Driven Lending Workflows <\/p>
- Automating borrower segmentation and loan risk assessment. <\/li>
- Enhancing underwriting and approval processes with AI. <\/li>
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Optimizing dynamic pricing and interest rates using machine learning.
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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>
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Adhering to regulatory frameworks (e.g., ECOA, GDPR).
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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>
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Generating synthetic data for model testing.
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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>
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Emerging trends: real-time credit scoring and open banking integration.
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Summary and Next Steps <\/p>
Requirements
- A foundational understanding of credit risk principles. <\/li>
- Experience with data analysis or business intelligence tools. <\/li>
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Familiarity with Python, or a readiness to learn basic syntax.
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Target Audience<\/strong> <\/p>
- Lending managers. <\/li>
- Credit analysts. <\/li>
- Fintech innovators. <\/li> <\/ul>
14 Hours
Testimonials (1)
Trainer was very knowledgeable and easy to speak to
