Business Impact

  • 48,00+

    Claims analyzed

  • 335+

    Potentially suspicious claims identified

  • 20+

    Fraudulent claims identified

Customer Key Facts

  • Location : North America
  • Industry : Insurance

Problem Context

The customer is a specialty insurance company that wanted to understand trends in their data and prepare a model to identify potential fraudulent claims in their workers compensation line of business.



  • Identify anomalies in the data
  • Tag potentially fraud claims based on a set score

Technologies Used

Microsoft SQL Server
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Developing a ML model and Rule-based Indicators to Classify Potential Fraudulent Claims


Quantiphi leveraged various types of the customer’s claims-related data, such as Claims note, Claims details, ISO match and Loss run, to train a machine learning model and build a rule/indicators based system to tag potentially fraudulent claims based on a set score. Additionally, we built an unsupervised machine learning model to find anomalies in the data. A Tableau Dashboard was also built to showcase which claims are detected as suspicious.


  • Reduced claims payout and expenses owing to identification of fraudulent claims
  • Gained the ability to gauge the fraud risk of a claim in real time
  • Improved operational efficiency
  • Analytics-driven reporting via a BI platform

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