AI in Risk Assessment Full Syllabus

Module 1: Introduction to Risk Assessment

• What is risk? Types of risks (financial, operational, cyber, health, credit)
• Why use AI in risk analysis?
• Key industries using AI for risk: finance, insurance, healthcare, logistics, IT
• Risk vs Uncertainty: Definitions & differences

Module 2: Foundations of AI & Machine Learning

• Supervised vs unsupervised learning
• Regression vs classification in risk modeling
• Neural networks for non-linear risk relationships
• AI lifecycle: Data → Model → Deployment → Monitoring

Module 3: Data for Risk Analysis

• Internal vs external risk data
• Risk indicators: historical loss, fraud records, credit scores, etc.
• Data collection techniques: APIs, web scraping, sensor data, etc.
• Handling missing data and outliers in risk metrics

Module 4: Data Preprocessing for Risk Modeling

• Feature engineering for risk prediction (ratios, flags, trends)
• Encoding categorical risk variables (e.g., occupation, location)
• Normalization/scaling for risk scores
• Creating lag-based features for time-sensitive risks

Module 5: Risk Scoring Techniques

• Traditional vs AI-based risk scoring
• Creating risk scorecards (credit, fraud, loan defaults)
• Weight of Evidence (WoE) and Information Value (IV)
• Model interpretability using SHAP and LIME

Module 6: AI Models for Risk Prediction

• Logistic regression for binary risk classification
• Decision Trees & Random Forest for interpretability
• Gradient Boosting (XGBoost, LightGBM) for high-accuracy risk models
• Deep learning models for complex risk data

Module 7: Risk in Cybersecurity & Anomaly Detection

• AI for threat detection and risk scoring in networks
• Using unsupervised learning for outlier (anomaly) detection
• Autoencoders for rare-event detection
• Real-time event stream risk analysis (e.g., log files)

Module 8: Credit Risk & Loan Default Modeling

• Credit scoring models with AI
• Predicting probability of default (PD)
• Loss Given Default (LGD), Exposure at Default (EAD)
• Anti-fraud AI techniques in credit assessments

Module 9: AI in Health Risk Assessment

• Predicting health insurance claims risk
• Early disease prediction from EHR data
• Patient re-admission risk prediction
• Personalized medicine risk modeling

Module 10: Risk Aggregation & Simulation

• Aggregating individual risks into portfolio risk
• Monte Carlo simulations for risk distribution
• Stress testing AI models for worst-case events
• Sensitivity analysis of risk factors

Module 11: Ethics, Bias & Regulation

• AI model bias in risk predictions (race, gender, income)
• Regulatory compliance (GDPR, AI Act, Fair Lending Laws)
• Explainable AI in regulated industries
• Risk of using AI for human-impacting decisions

Module 12: Risk Dashboard & Real-Time Monitoring

• Building a dynamic risk monitoring dashboard
• Integration with alert systems (email, SMS, Slack)
• Risk heatmaps and visual reports
• Real-time risk trend analysis with Streamlit or Power BI