Here’s a comprehensive markdown list that covers both data science fundamentals and insurance-specific applications, each with brief explanations to help you quickly review or build a study plan.
🧠 Data Science for Insurance – Technical Interview Prep
📊 Core Data Science & Machine Learning Topics
1.
Descriptive Statistics
Measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation, IQR) are foundational for exploring data distributions and summarizing claim amounts, premiums, etc.
2.
Probability & Distributions
Understanding normal, binomial, Poisson, and exponential distributions is crucial in modeling risk and rare events like claims. Conditional probability and joint distributions are key in policyholder modeling.
3.
Bayes’ Theorem
Especially useful in fraud detection or underwriting where you want to update risk probabilities based on new information.
4.
Hypothesis Testing & Confidence Intervals
Used to validate model assumptions or compare groups, e.g., claim rates between regions or customer segments.
5.
Linear & Logistic Regression
Linear regression helps model continuous outputs like claim cost, while logistic regression is widely used for classification tasks like churn prediction or fraud detection.
6.
Tree-Based Methods (Decision Trees, Random Forest, XGBoost)
Powerful non-linear models that perform well on tabular insurance data, often used in risk modeling, segmentation, and pricing.
7.
Model Evaluation Metrics
AUC, ROC, precision, recall, F1 score, MAE, RMSE – these help assess classification and regression model performance. Especially important when dealing with imbalanced datasets (e.g., fraud detection).
8.
Feature Engineering
Crafting useful predictors from raw policy, claim, and customer data. Temporal features, ratios, frequency counts, and one-hot encoding are often used.
9.
Cross-Validation & Hyperparameter Tuning
Essential for generalizing model performance. Techniques like K-fold CV, grid/random search, and Bayesian optimization are common in insurance ML workflows.
10.
Clustering & Unsupervised Learning
K-Means, DBSCAN, hierarchical clustering – useful for customer segmentation, risk categorization, and detecting outliers.
11.
Principal Component Analysis (PCA)
Dimensionality reduction technique to handle multicollinearity or visualize high-dimensional insurance datasets.
12.
NLP (Optional but Valuable)
Applications include analyzing customer complaints, claims descriptions, or chatbot conversations. Key techniques: TF-IDF, topic modeling, BERT embeddings.
📈 Time Series & Forecasting
13.
Time Series Decomposition
Breaking down data into trend, seasonality, and residuals – especially useful for modeling policy renewals, claim frequency, or revenue.
14.
Forecasting Models (ARIMA, SARIMA, Exponential Smoothing)
Used for predicting future claims, cash flows, or customer growth. Seasonality is common in insurance (e.g., natural disaster-related claims).
15.
Autocorrelation & Stationarity
Key for diagnosing time series models and understanding patterns like claim spikes or lags.
🧾 Insurance-Specific Topics
16.
Generalized Linear Models (GLMs)
The gold standard in insurance pricing. GLMs model frequency and severity separately and handle non-normal distributions (Poisson, Gamma, etc.).
17.
Risk Pooling & Law of Large Numbers
Core actuarial concept – spreading risk across many policyholders ensures predictable aggregate outcomes. Crucial for designing stable insurance products.
18.
Frequency-Severity Modeling
Claims are modeled as two separate processes: how often (frequency) and how much (severity). These are often combined to estimate expected loss.
19.
Credibility Theory
A way to combine group-level data and individual experience. Useful in adjusting premiums or modeling rare but important risks.
20.
Underwriting Models
Automating underwriting decisions using predictive modeling. Models assess risk based on demographics, credit scores, driving behavior, etc.
21.
Fraud Detection
Classification or anomaly detection techniques applied to claim data to identify suspicious activity. Often uses ensemble models or unsupervised learning.
22.
Lapse/Churn Modeling
Predicting when a customer is likely to cancel a policy. Involves survival analysis, logistic regression, or time-to-event models.
23.
Customer Lifetime Value (CLV)
Estimating the future profit a customer brings. Important for retention strategies and marketing spend optimization.
24.
Reserving Models
Predicting future claim liabilities using models like chain ladder, Bornhuetter–Ferguson. Important for financial planning and regulatory compliance.
25.
Reinsurance Modeling
Modeling catastrophic risk, evaluating attachment points, and simulating portfolio-wide losses to support reinsurance decisions.
🧰 Technical Tools & Skills
26.
Python / R
Core languages used for data wrangling, modeling, and reporting. Libraries like pandas, scikit-learn, statsmodels, xgboost, data.table, caret are widely used.
27.
SQL
Must-know for querying policy, claim, and transaction data. Practice joins, subqueries, window functions, and aggregations.
28.
Big Data Tools (Optional but Beneficial)
Tools like Spark, Hive, or distributed SQL engines are common in large insurers handling high-volume data.
29.
Data Visualization
Proficiency in tools like matplotlib, seaborn, Plotly, or BI tools like Tableau is valuable for explaining models and results to business teams.
30.
Model Deployment / APIs
Some roles may expect knowledge of how models are deployed via APIs or integrated into web services using Flask/FastAPI, Docker, etc.
🧪 Practice & Soft Skills
31.
End-to-End Case Studies
Be ready to walk through a complete project: framing the problem, data wrangling, modeling, validation, and communicating the impact.
32.
Storytelling & Business Impact
Practice translating technical results into actionable insights. Especially important in insurance, where decisions affect financial risk.
33.
Domain Knowledge Communication
Be able to discuss how your model fits into actuarial workflows, claims management, or customer retention efforts.
Let me know if you’d like a printable version, flashcards, or some mock interview questions to go with this!