Predicting fraud leveraging data science for a large US insurer

Problem Statement

An American supplemental insurance major need to deal with fraudulent cases in ever-increasing claims transactions. The insurer was largely dealing the fraudulent activities with a rule-based approach of manual processing and investigation (driven by expert judgement of agents, investigators and auditors). They wanted to move to a more scientific approach leading to strategizing the next generation Fraud Analytics System.

Solution Overview

• Machine learning-based predictive model devised.
• Implementing feature engineering techniques
  • Segmentation to detect patterns​
  • Supervised learning on specific clusters to measure its strengths and further strengthen the model​
• Tool stack used: Python, Splunk
  
  

Outcomes

• 500 basis points increase in the accuracy of Fraud detection.
• Moving from rule-based fraud identification to automated way using Machine Learning