Case Study: a major home insurance company improves profitability and reduces losses with Fractal

Use data to identify ideal premiums and non-renewable policies

The major home insurance company engaged Fractal to address challenges in optimizing profitability and risk management. The insurer sought to strategically grow its policy book by predicting non-catastrophic losses, estimating ideal premiums for profitability, and identifying which policies should be non-renewed to create a more balanced and sustainable portfolio.

Fractal implemented an advanced risk and profitability modeling solution utilizing machine learning. This data-driven approach involved building peril-based models to predict losses and identify key loss drivers. The resulting model significantly outperformed prior versions, successfully reducing the loss ratio and capturing a substantial amount of premium. This enabled the client to better target profits, improve renewal retention, and drive sustained growth.