Schadegarant puts the brakes on dubious insurance claims with automation

Schadegarant, a cooperation between 15 labels of 8 car insurance companies in the Netherlands, has partnered with Xomnia to automate the process with which it detects suspicious insurance claims. By creating a cloud-based end-to-end anomaly detection system, Xomnia aims to help Schadegarant reduce its costs and improve its processing speed.

The Xomnia X-Force team has shown its broad and deep knowledge and skills, designing and implementing a data-driven application. With a lot at stake, Xomnia showed to be flexible and trustworthy delivering within time and budget.
Frank van Donk, director Schadegarant


Schadegarant is a cooperation between 15 labels of 8 car insurance companies in the Netherlands. It negotiates on the behalf of those companies with car repair shops to get better pricing. It’s important for Schadegarant to be able to spot anomalies or mistakes in the pricing, in order to make sure that a claim is genuine.

Finding anomalies in car damage claims and sending experts out to ambiguous cases is time-consuming and expensive for Schadegarant. Xomnia’s job was to help the company automate the process of finding the anomalies in these claims, instead of having to go through them manually.


Xomnia designed a set of algorithms and filters which can assess dossiers of car damages and highlight questionable ones. Historical dossiers are analyzed by advanced algorithms after training them on a subset of the historical data. The accuracy of the estimation can be assessed by comparing it with the actual repair costs of several historical dossiers that have been approved by the insurance companies’ experts.

On average, the estimation deviates from the actual costs, both in high- and low-cost ranges. Now, new/unseen dossiers can be passed to the algorithm and an estimation of the repair costs can be retrieved. If the estimation of a new dossier deviates significantly from the average deviation, the dossier could qualify for being checked by an expert.

The algorithms run in real-time in a highly customized infrastructure that is built using i.e. Google Cloud Platform and Kubernetes technologies. The main application is built with Python and Java. For the algorithms, the Python machine learning library Scikit-learn is used. In order to maintain the quality of the application, code is automatically built, tested and deployed via CICD Pipelines. The initial Gitflow git strategy was later migrated to Trunk Based Development to support quicker feature releases.


Schadegarant is now able to act in a more data-driven way, relying on algorithms’ assessments regarding deployment of expensive expertise. This works in real-time, which means that there’s no delay in anomaly detection, and they can save time and money that used to be spent searching for these anomalies manually. Moreover, repair shops can start repairing a car straight away without having to wait for approval from Schadegarant, making the whole process quicker.