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Equian Launches NLP and Machine Learning Based Solution for P&C Subrogation

In View Summary

Equian, a US-based P&C payments enabler has announced the creation of a natural language processing engine, EquianAI, exclusively for the P&C subrogation market. The solution is based on predictive analytics, which can;

  • eliminate leakage associated with subrogation identification,
  • reduce false positives up to 36%,
  • enable productivity gains up to 20%,
  • and improve cycle time over 30%.

Equian manages over $500 billion in claims data annually and has designed EquianAI to assist clients in identifying and recovering inaccurate transactions in complex fields such as healthcare, workers’ compensation, and property & casualty markets.

EquianAI is currently serving the P&C subrogation market only

Sam Cooper, Senior VP, Client Solutions, Equian says, “Most carriers rely on the manual evaluation of free text with some predictive analytics on a limited amount of structured data to identify losses with subrogation opportunity. EquianAI provides automation that removes tedious human intervention by analyzing the free text, the structured data, and the unstructured data simultaneously.”

EquianAI performs in-depth analyses to prioritize claims by scoring recoverable files as High, Medium or Low based on recoverability likelihood. Rules-based technology then directs the scored cases to appropriate investigation and recovery experts.

Reported Benefits

Benefits to Customers:

  • Improves customer trust and experience
  • Increased customer engagement
  • Reduced costs and time to

Benefit to the Insurer:

  • Improves employee productivity
  • Reduced costs and response times
  • Reduced fraud
  • Improved customer retention

The Digital Insurer's View

Use of NLP and Machine Learning in subrogation activities will lower fraud, detect semantic relation patterns, and help in automatic categorization to enables sentiment analysis. All this ensures that subrogation actions will be better informed and help assist insurers eliminate instances of false or excessive payouts in the event of major claims.

References

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