Binary Process Paths

Executive Summary

In the auto insurer sector, predicting a damaged vehicle’s destination at the first notice of loss (FNOL) is critical so that the claims cost can be mitigated, operations can be simplified, and the policy holder’s experience is optimized. The current case study features this process through which a binary machine learning model was created to manage such challenges as it led to meaningful cost savings and more precise rates.

Background Information about the Client

A customer of ours is an insurance organization that ranks among the leaders of the market and applies an approach that relies on constant improvement of claims management. Acknowledging the need for correct decision-making in this process, we initiated a project for better prediction of vehicle outcome after the First Notice of Loss (FNOL).

Client Challenge

Insurance companies have to choose whether to tow the car to a scrap yard or a repair facility after getting the policyholder’s first notice of loss. Prediction errors can result in large financial losses as well as inefficient operations.

Case Evaluation

The existing method the client uses to estimate the destination of vehicles in some cases operates with the accuracy rates which causes extra costs and extra time needed for claims processing.

Solution

Our model is based on binary machine learning built on logistic regression and binary class techniques that predict a vehicle’s end state after initial notification/report. The capabilities of our model became noticeable when it went through the data available for FNOL, incident descriptions, vehicle specifications, and whether the vehicle should be towed to a repair shop or to a salvage yard.

Results

Our solution’s accuracy improved significantly from as low as 15% to almost 65% while maintaining a similar percentage of false alarms. For that particular insurance carrier, this moderate but clear increase in accuracy has had a material impact as the saving on insurance coverage premiums, amounting to millions of dollars over the course of three years.

Conclusion

Our innovative approach to predictive modeling has revolutionized claims management processes in the auto insurance industry. By leveraging advanced analytics and machine learning techniques, we have not only enhanced prediction accuracy but also saved millions of dollars in operational costs. This success underscores the importance of innovation and data-driven decision-making in optimizing efficiency and delivering superior service to customers.