Using SHAP values to explain actuarial models is a powerful way to bring transparency and trust to complex predictive models that are often viewed as black boxes. SHAP, which stands for SHapley Additive exPlanations, breaks down a model’s prediction into the contribution of each feature, making it easier to understand how individual variables affect outcomes. This is especially valuable in actuarial science, where decisions impact financial risk assessments, insurance pricing, and regulatory compliance.

Let’s walk through how you can use SHAP values step by step to explain your actuarial models, with practical advice and examples to guide you.

First, it helps to understand what SHAP values represent. Think of your model as a cooperative game where each feature is a player contributing to the final payout — in this case, the model’s prediction. The SHAP value for each feature quantifies how much that feature adds or subtracts from the average prediction when considered in all possible combinations with other features. This ensures a fair allocation of “credit” for the prediction, rooted in solid game theory principles[2][6].

To start using SHAP values in actuarial models, you need to have a trained machine learning model — this could be a gradient boosting model, random forest, or any other algorithm suited for your data. SHAP works as a post hoc explanation tool, meaning it analyzes the model after training, independent of the model type, although it is particularly efficient for tree-based models[2].

The basic workflow looks like this:

1. Train your actuarial model: For example, you might build a gradient boosted tree model to predict motor insurance claim frequencies based on features like driver age, vehicle type, and past claim history.

2. Calculate SHAP values: Use a SHAP library (such as the Python shap package) to compute SHAP values for each feature and instance in your dataset. This will produce a matrix of SHAP values with the same shape as your input data, where each value explains the contribution of that feature to the prediction for that particular policyholder or claim.

3. Interpret SHAP values locally: For a single insurance claim, you can see which features pushed the prediction higher or lower compared to the average. For instance, a young driver’s age might have a positive SHAP value, indicating it increased the predicted claim frequency, while a good driving record might have a negative SHAP value, reducing risk.

4. Summarize globally: Aggregate SHAP values across all instances to find which features most influence the model overall. Bar plots of mean absolute SHAP values help identify top predictors — for example, vehicle age or region might emerge as key drivers of risk in your portfolio[1].

5. Visualize interactions and distributions: Beeswarm plots or heatmaps provide a richer view by showing the distribution of SHAP values per feature, revealing nonlinear effects and interactions. For example, you might discover that the impact of a vehicle’s age on claim risk varies dramatically for different driver age groups[1].

One practical tip: always keep the baseline or expected value in mind. This baseline is the average model output (e.g., average claim frequency) across the dataset, and SHAP values explain deviations from this baseline. So, if the baseline predicted claim frequency is 0.1, and the sum of SHAP values for a specific instance is +0.05, the model predicts a claim frequency of 0.15 for that policy[2].

Incorporating SHAP explanations into actuarial workflows has several benefits:

• Model validation and debugging: By examining SHAP values, you can detect if your model relies on unreasonable features or if any data issues skew predictions. For instance, if SHAP indicates that a rare but irrelevant feature dominates predictions, you might revisit your data preprocessing.

• Regulatory transparency: Insurance regulators increasingly demand explainable models. SHAP provides a mathematically sound, intuitive way to demonstrate how your model makes decisions, which can ease compliance burdens.

• Client communication: When discussing pricing or underwriting decisions with clients or stakeholders, SHAP visualizations make it easier to justify outcomes and build trust.

• Feature engineering insights: Understanding which features matter most can guide you to collect better data or engineer new variables that improve model accuracy.

Let me share a simplified example from motor insurance claims modeling. Suppose you have a model predicting the likelihood of a claim, and you want to explain the prediction for a particular policyholder:

• The baseline claim probability is 5%.

• SHAP shows that the driver’s age contributes +2%, vehicle age +1.5%, and prior claims +1%, increasing risk.

• Meanwhile, a clean driving record contributes -1.5%, lowering risk.

Adding these contributions to the baseline gives a personalized risk estimate of 8% for this driver.

To implement this practically, you would:

• Use Python’s shap library to calculate SHAP values after training your model.

• Generate visualizations like bar charts to see global feature importance, beeswarm plots for feature distributions, and force plots for individual explanations.

• Interpret these plots alongside domain knowledge to draw actionable conclusions.

Remember that SHAP is computationally intensive for large datasets or models with many features, but several approximation methods and optimizations are available, especially for tree-based models[1][3].

Another practical note: while SHAP values provide rich insights, they are one tool among many. Combining SHAP with partial dependence plots, feature importance scores, and domain expertise will give you the best understanding of your actuarial models.

Statistically speaking, transparency matters because studies show that trust in AI-driven decisions improves when stakeholders understand how features influence outcomes. Given that insurance decisions can have significant financial impacts, using SHAP to explain models is not just a nice-to-have but a strategic necessity.

In summary, adopting SHAP values in actuarial modeling workflows equips you with a clear, fair, and rigorous way to explain your machine learning models. Start by training your model, compute SHAP values, explore them locally and globally, and use visualizations to communicate insights. This approach will deepen your understanding, improve trust, and ultimately help you make better data-driven insurance decisions.