Machine Learning For Insurance Claim Fraud Detection

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There is a large flow of claims to the casualty insurance company, which are fraudulent. The insurance company spend a lot of money and time checking and working with such applications. The insurance company needed improved fraud detection and new innovative ways to mitigate risk.


We prepared a solution that can analyze the data of the processed claims and, based on this analysis, assess the new claims. Our company developed a system of machine learning and analysis, which is based on internal and external data.

The cognitive system processes huge amounts of data in a relatively short time, structuring the primary disparate information, revealing the connections between the imperceptible factors for the person, so the system can predict fraud by itself.

The cognitive system сan predict with high probability attempts to deceive in virtue of:

  • permanent review of existing claims
  • аbility to learn and take into account the corrections that a person makes
  • сlear system of descriptions why a given claim looks fraudulent


The introduction of cognitive technologies into the work of the insurance industry allowed to optimize business processes and to predict fraudulent cases with high accuracy that reduced the cost.


  • Business Analysis
  • Java
  • Oracle
  • AngularJS
  • Machine Learning

Some detailed information not disclosed due to NDA restrictions