Machine learning models have become increasingly popular in actuarial science, helping actuaries make better predictions for insurance claims, pricing, and risk assessment. But one common challenge many actuaries face—especially those without a deep technical background—is understanding how these complex models arrive at their predictions. This is where SHAP, or SHapley Additive exPlanations, comes into play. SHAP offers a clear and mathematically sound way to interpret machine learning models by breaking down their predictions into understandable pieces. If you’ve ever struggled to explain a model’s output to colleagues or stakeholders, this step-by-step guide is for you.

To start, SHAP values come from a concept in game theory known as Shapley values. Imagine a group of players working together in a game, and you want to fairly divide the total winnings based on each player’s contribution. In machine learning, the “players” are the features (variables) in your model, and the “winnings” are the model’s prediction for a specific case. SHAP calculates the average contribution of each feature to the prediction by considering all possible combinations of features, ensuring a fair allocation of influence[1][6].

The beauty of SHAP is that it’s model-agnostic—you can use it with any machine learning model, whether it’s a random forest, gradient boosting, or neural network. For actuaries, this means you can apply SHAP to your favorite predictive models and get clear explanations without rewriting your entire workflow[2][4].

Here’s how you can interpret and explain actuarial machine learning models using SHAP, even if you don’t have a technical background.

First, understand the base value or expected value of the model. This is the average prediction the model makes when no feature information is known. For example, if you have a model predicting car insurance claims, the base value might be the average claim frequency in your dataset. SHAP values then show how each feature pushes the prediction away from this base value for each individual policyholder[2].

Next, compute SHAP values for your dataset. Many software packages, like Python’s shap library, automate this calculation. The output is a table with the same shape as your input data but filled with SHAP values instead of raw feature values. Each SHAP value quantifies the contribution of its corresponding feature to the prediction for that row (policyholder)[1][5].

Once you have SHAP values, you can create visualizations to better understand the model’s behavior. The two most common plots are:

• Summary plots: These show the distribution of SHAP values for each feature across all observations. You’ll see which features have the largest impact overall and how their values (high or low) affect the prediction. For example, if “age” has high positive SHAP values, it means older drivers might increase predicted claim frequency.

• Force plots: These explain individual predictions by showing how each feature’s SHAP value pushes the prediction from the base value. Imagine a tug-of-war where some features pull the prediction higher, others pull it lower, and the final position is the model’s output for that case[2][5].

Let’s make this practical. Suppose you’re analyzing a machine learning model predicting the number of claims per policyholder. You want to explain why the model predicts a high claim frequency for a particular customer. By generating a force plot for that customer, you might see that a high vehicle age and previous claims history strongly push the prediction upward, while a low annual mileage pulls it down a bit. This transparent explanation helps actuaries and underwriters understand risk drivers in familiar terms.

Interpreting SHAP values also helps identify potential issues with your model. For example, if a feature consistently has SHAP values near zero, it may not be contributing meaningfully and could be dropped to simplify the model. Or if SHAP reveals unexpected feature interactions or non-intuitive effects, it may prompt a review of data quality or model assumptions[4][5].

For actuaries new to SHAP, here are some actionable tips to get started:

• Start with simple models: Linear models or tree-based models like random forests are easier to interpret with SHAP and can help build intuition before tackling deep learning.

• Use a background dataset: SHAP requires a baseline to define what “missing” means when computing feature contributions. Use a representative sample of your data to provide this context, ensuring SHAP values are meaningful.

• Focus on business insights: Translate SHAP explanations into actionable business terms. For example, instead of just saying “feature X has a SHAP value of 0.2,” explain “a higher value of feature X increases the predicted claim frequency by 20% compared to average.”

• Combine SHAP with traditional actuarial tools: Use SHAP alongside generalized linear models (GLMs), partial dependence plots, and other diagnostics to cross-validate findings and build confidence.

• Communicate clearly: When sharing SHAP results with non-technical stakeholders, use visuals and analogies. Describe the base value as the “average prediction” and SHAP values as the “push or pull” each factor has on that average.

Remember, SHAP values are additive, meaning the model’s prediction for any policyholder equals the base value plus the sum of all SHAP values for that individual’s features. This property ensures your explanations are consistent and trustworthy[2].

While SHAP can be computationally intensive—especially when models have many features—it’s been optimized for common actuarial models like tree-based algorithms, making it practical for real-world use[1]. Also, many actuarial software environments now support SHAP integration, so you can explore these explanations without heavy coding.

In summary, SHAP is a powerful, mathematically grounded method that lets actuaries peek inside their machine learning models. By breaking down predictions into understandable feature contributions, SHAP demystifies complex algorithms and strengthens trust in model-driven decisions. Whether you’re explaining a single policy’s risk or assessing feature importance across your portfolio, SHAP gives you the tools to communicate insights clearly and confidently.

Give SHAP a try in your next machine learning project. With just a little practice, you’ll find it an invaluable friend in bridging the gap between technical models and actuarial expertise. And as you grow more comfortable, you might even discover new patterns and relationships in your data that were previously hidden in the black box.