Data scientists often default to Ordinary Least Squares regression for predictive modeling, but insurance claim data presents unique challenges that require more thoughtful approach selection. The French Motor Third-Party Liability Claims dataset contains numerous zero values and extreme outliers that violate OLS assumptions, making standard linear regression inadequate for accurate predictions.

When claim amounts cluster at zero with a long tail of large values, OLS produces impossible negative predictions and poorly distributed residuals. Adding interaction terms between features like Exposure and Bonus Malus captures some complexity but fails to address the fundamental distribution problem. The model's histogram and Q-Q plots reveal non-normal residuals, particularly for higher claim amounts.

Tweedie regression handles zero-inflated, skewed data by modeling both the probability of zero outcomes and the magnitude of positive outcomes simultaneously. This approach aligns with the actual data generation process in insurance, where most policyholders file no claims while a few generate large payouts. The method provides interpretable coefficients while accommodating the compound nature of insurance loss data.

For practitioners working with similar datasets, the choice isn't about model complexity but matching the statistical framework to the data's true distribution. Tweedie regression offers a principled solution when OLS assumptions clearly fail.