A growing number of insurance companies, self-insureds, and third party administrators (TPAs) have embraced predictive analytics as a powerful and innovative way to enhance their claims management and adjustment processes.

Early adopters that have implemented end-to-end claims predictive analytics have observed better claims outcomes and bottom-line loss cost savings of up to 10 percent of an organization's annual claims spend driven by a number of factors:  

  • Improved assignment of adjusters, medical professionals, and specialty resources at first notice of loss (FNOL) and throughout the claim life cycle.
  • Increased focus on high severity claims, where case management and early adjudication can make a significant difference.
  • Improved quality of special investigative unit (SIU) referrals as well as a focus on deterrence versus evidence collection.
  • Enhanced focus on return-to-work (RTW) programs and safety measures.
  • Curtailing claims duration via patent pending injury grouping methodologies that segment seemingly like-injury claims.[1]

Just as underwriting predictive models helped reshape the way insurance companies segment their risks, claims models are helping reshape the claims adjustment process. In this article, we'll share real life stories, along with some important business implementation and change management matters that insurers should consider before deploying claims predictive models.

Claims Adjuster Stories From the Field

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