Predictive analytics have been used in a variety of industriesfor a number of years. Perhaps nowhere is the concept ofpredicting and rating risks more common than in the insuranceindustry. From zip codes and income to credit scores and drivingrecords, a whole host of data points can be used to accuratelygauge risks, identify trends, and set rates. 

Certainly there are those who have taken exception to somemethodologies, as not all people with bad credit are bad drivers,nor are all people living in lower socio-economic zip codes goingto be in an accident or have their vehicles stolen. But this overlysimplistic view misses the point: risk is rated upon probabilities,borne out by statistically validated historical facts.

The bigger question should be the ability of insurers toleverage predictive analytics to guide other aspects of theirbusinesses, such as the claims process. The capture ofdata provides a wealth of opportunities, such as theidentification of medical fraud, staged accidents, injury buildup,repair-cost variations, towing, rental, and storage. When properlyinstituted, the use of this information benefits both the companyand the consumer immeasurably. 

From the outset of a claim, data that is captured, reviewed, andmonitored can isolate gaps in the end-to-end process that areimpeding productivity, quality, and accuracy. This historical datacan isolate claim handlers who are not asking the right questions,properly appraising claims, overlooking fraud indicators, ormissing subrogation opportunities. Much like a football coachreviewing game-day film, this type of analysisof processes, workflows, and results canbe utilized to improve future outcomes. 

In Re-Adjusted: 20Essential Rules for Taking Your Claims Organization fromOrdinary to Extraordinary, I discuss in greater detailreal-life experiences of utilizing analyticsto dramatically improve subrogation results. Whenconsidering this aspect of claims, recognize that there are avariety of critical functions that impact outcomes.

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For response-side subrogation, carriers often utilize liabilityadjusters to assess those claims. As was the case during my tenurerunning claims operations, these types of inbound demands often hada low priority, resulting in missed opportunities relating toliability, estimatics, and historic alternative partsutilization. 

On the demand side, there are similar challenges of ensuringthat the right files are referred, that proper procedures are inplace to minimize closures with no recovery, and that all fileswith potential comparative negligence are identified during theclaims process.

As is the case in many aspects of claims, data analytics can bea powerful tool to identify missed opportunities on both demand-and response-side subrogation. When factoring in perceived costs,consider that, industry wide 15 percent of all claims are closed byadjusters with a missed subrogation opportunity. Calculate whateven a small percentage of this would do to your bottomline. 

By developing key metrics, you can utilize data to identifyhistorical trends that will predicate future behavior. With theright set of data, it is possible to not only gauge what is beingdone internally, but also amongst the competition, creating aninstant competitive advantage.

Whatever your opportunity—be it subrogation, salvage, fraudidentification, inspections, or a myriad of other claims-relatedprocesses, consider the benefits of leveraging data for bottom lineimprovement.