Machine Learning For Insurance Claim Fraud Detection

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The casualty insurance company was facing a large flow of fraudulent claims. The client was compelled to spend a lot of time and money in order to evaluate claims in terms of credibility. The company was in need of an effective software solution that would detect fraudulent claims more precisely.


Azati introduced a system of machine learning and analysis, that detects fraud with high accuracy.

At a first step, the system analyzes large volume of previously processed claims. By structuring primarily disparate information and revealing connections between multiple factors, the system identifies patterns of fraud.

Then, based on this analysis, it assesses new claims. The claims regarded by the system as fraudulent, are therefore handed over to human specialists for further investigation, along with a descriptive explanation on why it is considered to be fraudulent.


The introduction of cognitive technologies into the work of the insurance company allowed the latter to optimize its business processes and detect fraudulent cases 3 times more accurately than before. 

Implementing a cognitive system proves to be an efficient way to detect fraudulent claims in virtue of:

  • fraudulent claims are detected with greater precision
  • the huge amount of data is processed in a relatively short period of time
  • the system reveals connections between a great number of diverse factors, which can be imperceptible for a person
  • by the permanent review of existing claims, the program is able to discover new schemes of fraud


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

Some detailed information not disclosed due to NDA restrictions