Machine Learning for Risk Management: Predict, Detect, and Prevent Financial Threats

artificial-intelligence-ai.

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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