Predictive analytics have been used in a variety of industries for a number of years. Perhaps nowhere is the concept of predicting and rating risks more common than in the insurance industry. From zip codes and income to credit scores and driving records, a whole host of data points can be used to accurately gauge risks, identify trends, and set rates.
Certainly there are those who have taken exception to some methodologies, as not all people with bad credit are bad drivers, nor are all people living in lower socio-economic zip codes going to be in an accident or have their vehicles stolen. But this overly simplistic 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 to leverage predictive analytics to guide other aspects of their businesses, such as the claims process. The capture of data provides a wealth of opportunities, such as the identification of medical fraud, staged accidents, injury buildup, repair-cost variations, towing, rental, and storage. When properly instituted, the use of this information benefits both the company and the consumer immeasurably.
From the outset of a claim, data that is captured, reviewed, and monitored can isolate gaps in the end-to-end process that are impeding productivity, quality, and accuracy. This historical data can isolate claim handlers who are not asking the right questions, properly appraising claims, overlooking fraud indicators, or missing subrogation opportunities. Much like a football coach reviewing game-day film, this type of analysis of processes, workflows, and results can be utilized to improve future outcomes.
In Re-Adjusted: 20 Essential Rules for Taking Your Claims Organization from Ordinary to Extraordinary, I discuss in greater detail real-life experiences of utilizing analytics to dramatically improve subrogation results. When considering this aspect of claims, recognize that there are a variety of critical functions that impact outcomes.
For response-side subrogation, carriers often utilize liability adjusters to assess those claims. As was the case during my tenure running claims operations, these types of inbound demands often had a low priority, resulting in missed opportunities relating to liability, estimatics, and historic alternative parts utilization.
On the demand side, there are similar challenges of ensuring that the right files are referred, that proper procedures are in place to minimize closures with no recovery, and that all files with potential comparative negligence are identified during the claims process.
As is the case in many aspects of claims, data analytics can be a 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 by adjusters with a missed subrogation opportunity. Calculate what even a small percentage of this would do to your bottom line.
By developing key metrics, you can utilize data to identify historical trends that will predicate future behavior. With the right set of data, it is possible to not only gauge what is being done internally, but also amongst the competition, creating an instant competitive advantage.
Whatever your opportunity—be it subrogation, salvage, fraud identification, inspections, or a myriad of other claims-related processes, consider the benefits of leveraging data for bottom line improvement.