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Machine Learning for Risk Management: Predict, Detect, and Prevent Financial Threats
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Course Modules:
Module 1: Introduction to Risk in Financial Systems
Types of risk: credit, market, operational, compliance, fraud
Traditional vs. machine learning-based risk models
Why AI is essential for modern risk frameworks
Module 2: Data for Risk Modeling
Sources: credit histories, transactions, claims, KYC data
Dealing with imbalanced datasets and rare events
Feature engineering for financial behavior
Module 3: Supervised Learning for Risk Prediction
Logistic regression and decision trees
Random Forests, Gradient Boosting, and XGBoost
Credit scoring, default prediction, and loss estimation
Module 4: Anomaly Detection for Fraud and Abuse
Unsupervised methods: Isolation Forest, DBSCAN, Autoencoders
Pattern recognition in transaction and user data
Real-time fraud detection pipelines
Module 5: Model Validation, Fairness & Regulatory Concerns
Performance metrics: AUC, precision, recall, F1 score
Fair lending laws, bias audits, and explainability (LIME, SHAP)
Regulatory frameworks: Basel III, GDPR, and responsible AI
Module 6: Capstone Project – Build a Risk Management Model
Choose a use case: credit scoring, fraud detection, or loan default prediction
Develop, train, and test your model on real/synthetic data
Submit performance analysis, fairness review, and project report
Tools & Technologies Used:
Python
Scikit-learn, XGBoost, LightGBM
Imbalanced-learn, SHAP, LIME
Pandas, NumPy, Matplotlib, Seaborn
Target Audience:
Finance professionals and analysts
Data scientists building risk-related AI tools
Banking and fintech engineers
Students preparing for roles in financial AI
Global Learning Benefits:
Build robust AI models to mitigate financial risk
Detect fraud and abuse with intelligent automation
Ensure fairness and transparency in predictive models
Align with regulatory expectations in risk-sensitive industries
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