Subject Overview

An advanced postgraduate subject integrating insurance domain knowledge, statistical learning, actuarial methods, machine learning, managerial decision-making and responsible AI. It develops the ability to design, validate, deploy and govern predictive solutions across underwriting, pricing, claims, fraud, customer management and enterprise risk.

Learning Goal

Students will be able to frame insurance problems analytically; prepare insurance data; build, interpret and validate predictive models; translate results into profitable and fair decisions; manage model deployment and monitoring; and lead ethical, governed and strategically aligned analytics initiatives.

Syllabus

1. Foundations of Predictive Analytics in Insurance

Learning Objective: Explain insurance economics, risk pooling, adverse selection, moral hazard and the roles of descriptive, diagnostic, predictive and prescriptive analytics; identify major use cases, stakeholders, terminology and the predictive modelling lifecycle.

Category: foundation

2. Insurance Data, Measurement and Architecture

Learning Objective: Understand policy, exposure, premium, claims, customer, intermediary, telematics, geospatial and external data; assess data provenance, quality, lineage, missingness, leakage, bias and temporal structure; relate data warehouses, lakes, APIs and cloud platforms to insurance analytics.

Category: foundation

3. Exploratory Analysis and Insurance Performance Metrics

Learning Objective: Apply segmentation, visualization and statistical analysis using frequency, severity, pure premium, loss ratio, combined ratio, retention, conversion, lifetime value and claims-development measures; distinguish correlation, association and causation.

Category: core

4. Statistical Foundations and Generalized Linear Models

Learning Objective: Use probability distributions, estimation, hypothesis testing and regression; develop interpretable GLMs for claim frequency, severity and pure premium using Poisson, negative binomial, Gamma, Tweedie and logistic models, including offsets, interactions, regularization and diagnostics.

Category: core

5. Feature Engineering and Data Preparation

Learning Objective: Construct credible predictors from policy, claims, behavioural, text, time-series and geospatial data; perform encoding, scaling, transformation, imputation, outlier treatment, class-imbalance handling and reproducible train-validation-test design.

Category: core

6. Machine Learning for Insurance Prediction

Learning Objective: Build and compare decision trees, random forests, gradient boosting, support-vector methods, neural networks and ensemble models; manage nonlinearities, interactions, hyperparameter tuning and the trade-off between predictive power, stability and interpretability.

Category: core

7. Model Evaluation, Validation and Uncertainty

Learning Objective: Evaluate classification, regression, ranking and calibration using suitable metrics; apply cross-validation, back-testing, lift and gains analysis, ROC/PR curves and stress testing; quantify uncertainty, assess robustness and detect overfitting, drift and data leakage.

Category: core

8. Pricing, Underwriting and Risk Selection

Learning Objective: Apply predictive models to risk classification, technical pricing, underwriting triage and portfolio optimization; integrate actuarial credibility, expenses, capital, competition, elasticity, regulation and fairness into rate and acceptance decisions.

Category: application

9. Claims Analytics, Reserving and Fraud Detection

Learning Objective: Use predictive methods for claims triage, severity estimation, settlement, litigation, recovery, reserving and catastrophe response; design anomaly, network and supervised fraud models while accounting for rare events, investigation capacity and false-positive costs.

Category: application

10. Customer, Distribution and Marketing Analytics

Learning Objective: Model acquisition, conversion, churn, renewal, cross-sell, next-best action and customer lifetime value; evaluate agents, brokers and digital channels; use experimentation, uplift modelling and causal inference to improve customer and distribution decisions.

Category: application

11. AI, Unstructured Data and Emerging Insurance Technologies

Learning Objective: Assess NLP, computer vision, speech analytics, telematics, IoT, satellite data and generative AI for underwriting, claims and service; evaluate human-in-the-loop automation, foundation-model risks, synthetic data and responsible adoption of insurtech capabilities.

Category: contemporary

12. Deployment, MLOps and Digital Transformation

Learning Objective: Translate models into operational decisions through scoring pipelines, APIs, decision engines and cloud platforms; manage versioning, testing, monitoring, retraining, cybersecurity and integration with core insurance systems; formulate build-buy-partner and transformation roadmaps.

Category: strategic

13. Ethics, Regulation and Model Governance

Learning Objective: Address privacy, consent, transparency, discrimination, proxy variables, explainability and accountability; establish documentation, validation, approval, audit, access controls and model-risk management consistent with insurance regulation and responsible-AI principles.

Category: strategic

14. Analytics Strategy, Leadership and Future Directions

Learning Objective: Develop an enterprise analytics strategy linking use cases to profitability, customer outcomes, solvency and resilience; prioritize portfolios, evaluate ROI and organizational readiness, lead multidisciplinary teams, communicate evidence to executives, and assess climate analytics, embedded insurance, real-time risk and autonomous decision systems.

Category: advanced