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Polaris - Fraud Mitigation


 Polaris is an enherent business analytics solution that reduces cost by improving the speed and accuracy of identifying insurance fraud. The solution utilizes IBM advanced analytics technologies to analyze both text and structured data in claims. Polaris™ finds fraudulent claims more accurately, consistently and earlier in the claim process to reduce cost, improve claim administration and retain business knowledge.

The Solution

Polaris uses text analysis to get beyond the limitations of predictive analytics by combining insights from traditional forms of structured data with the insight from text. Effective fraud detection requires the rapid analysis of structured data and text contained in multiple sources from claim systems to the wide variety of notes and documents associated with an insurance claim. Polaris™ can access both text and structured data from more than fifty different data sources and create a  360 view of all relevant claim data for analysis.

Parameter Development and Predictive Modeling

Using enherent starter dictionaries indicators are quickly created and refined accelerating Polaris™ deployment. The Scoring Module Workbench allows individual indicators to be combined and weighted based on their ability to identify fraud. The weighted fraud indicators are then applied and a total fraud score is calculated for each claim.

Analysis and Workflow

The output of the Polaris™ scoring models is reviewed and acted upon using the Analyst Workbench. Examiners and fraud analysts can quickly review highly suspicious claims based on the fraud funnel, easily navigate through claims with user adjustable filters and take action via pre-defined workflows

Reporting and Performance Management

Standard reports and dashboards provide drill-down capabilities into claim data and the models to support performance analysis and deliver better insights for decision making.

Solution Benefits

Benefits achieved by the use of this solution include:

  • Improved loss ratios - loss prevention and compromise opportunities are identified early in the process
  • Improved expense ratios - claim decisions are made earlier in the process
  • Retained claims knowledge and mitigated risk of knowledge drain
  • Process improvements – streamline claim administration and increase customer satisfaction
  • Consistent application of fraud expertise across all claims by automating the application of fraud indicators
  • Regulatory compliance - analysis of text identifies suspicious claims not reported to state agencies
 
 
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