The ML model achieves a prediction accuracy of 94 percent. It is trained on a wide range of damage types — from minor parking dents to wildlife damage. The average deviation from the subsequent expert appraisal is between 170 and 200 euros.

The model is based on over 800 data points per claim and 15 years of historical appraisal data. The more claims the model processes, the more precise the prediction becomes — a continuous learning effect that further improves accuracy over time.