Insurance Data Science — Skills & Portfolio Guide
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Insurance Data Science — Skills & Portfolio Guide
Practical skills, application areas, and project ideas to build a strong insurance-focused data science portfolio.
1. Core Application Areas
Where data science is most impactful in insurance.
| Domain Area | Typical Use Case | Example |
|---|---|---|
| Risk Modeling | Predict probability of claims | Accident likelihood per driver |
| Underwriting Automation | Automate risk evaluation | AI health risk scoring |
| Pricing Optimization | Optimize premiums | Telematics-based dynamic pricing |
| Fraud Detection | Detect suspicious claims | Graph-based fraud ring detection |
| Claims Management | Estimate claim cost & triage | Repair cost estimation from photos |
| Customer Analytics | Churn & cross-sell | CLV and product recommendations |
| Process Automation (NLP/LLM) | Docs and summaries automation | Auto-summary of claim reports |
2. Key Data Science Skills
Technical skills to develop and highlight in your portfolio.
Data & Modeling
- Data Engineering: SQL, ETL, pipelines
- Statistical Modeling: GLMs, Bayesian methods
- Machine Learning: XGBoost, LightGBM
- Deep Learning: CNNs (images), Transformers (NLP)
- Time Series: Prophet, SARIMA, LSTM
Advanced & Governance
- NLP / LLMs: document parsing, triage, summarization
- Graph Analytics: network-based fraud detection
- Causal Inference: policy change effects, uplift
- Explainability & Fairness: SHAP, LIME, Fairness tools
- Visualization: Power BI, Plotly, Tableau
3. Important Domain Knowledge
Insurance concepts to learn and reference in projects.
- Policy lifecycle: quotation → underwriting → claim → renewal
- Loss & Combined Ratio: core profitability KPIs
- Reinsurance: risk transfer mechanisms
- Actuarial science: pricing and reserve fundamentals
- Regulatory compliance: GDPR, Solvency II, fairness
- Telematics / IoT: usage-based insurance data
4. Project Ideas for Your Portfolio
Concrete, portfolio-ready projects that demonstrate both technical skill and domain knowledge.
| Project | Description | Techniques |
|---|---|---|
| Claim Severity Prediction | Predict repair/claim cost | Regression, feature engineering |
| Fraud Detection | Detect suspicious claim patterns | Classification, anomaly detection, graphs |
| Customer Lifetime Value (CLV) | Estimate long-term profit per policyholder | Predictive modeling, survival analysis |
| Dynamic Pricing Simulation | Optimize premiums under scenarios | Reinforcement learning, Monte Carlo |
| NLP Claim Triage Assistant | Auto-categorize and summarize claims | Transformers, LLMs |
| Churn Prediction | Who won't renew? | Classification, explainability |
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