Fraudulent Claims Detection

Insurance
case study

Business Impacts

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.

Challenges

 

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

Technologies Used

Microsoft SQL Server

Microsoft SQL Server

Python

Python

TensorFlow

TensorFlow

Tableau

Tableau

Keras

Keras

Auto Encoder

Auto Encoder

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

Solution

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.

Result

  • 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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