How telematics and predictive analysis can help insurers fight fraudulent claims
The claims process has always been considered the defining moment in the relationship between an insurer and a client. It is the moment in which, more than any other, a client experiences and truly appreciates – or not – his or her customer experience. And yet, especially for insurers, it’s not only a crucial point, but also often the hardest and most complex activity in the industry.
Insurers and claims inspectors often face too much work, frequent file transfers and may even lack the very information and/or experience that is necessary to evaluate the indicators in the claim. Completing background checks and verifying accident dynamics often slow the claims process down and fraud, unfortunately, is not only a constant threat, but also on the rise. This is where predictive analytics can step into oil and expedite the assessment mechanism.
Insurance telematics is based on the collection of data and with the current unprecedented availability of enormous amounts of data – big data – insurers need to optimize internal procedures and develop models or pattern recognition algorithms – not only on the road, but also in the office – that will allow them to accelerate the claims process, whilst limiting the possibility of inadvertently clearing a fraudulent claim.
Moreover, the benefits of predictive analytics are not only limited to claims processing and fraud identification. It also allows organisations to improve the overall experience of their customers by improving all internal process and allowing all parties involved in a transaction to save both money and time.
The hard part, of course, is determining the modus operandi, deciding on the models and developing the algorithms. Insurers must first conduct an old school analysis of their business needs, challenges and issues. Subsequently, reliable high-quality data must be identified and correctly integrated with all the meta-data labels necessary to fuel the pattern recognition algorithms. Finally, it’s time to develop the algorithms. And then, it’s once again back to old school methods, to correctly integrate the predictive analytics process and procedures into the greater organisational system of the insurer.
In practice, once claims have been analysed, filtered and categorized by a computer-based system that analyses a wide range of risk parameters, the easiest cases can be assigned to relatively inexperienced inspectors, while unusual, suspect or particularly complicated claims are reserved for senior or more experienced inspectors. The objective, of course, is not to replace humans with machines, but to minimise the risk of bottlenecks in the claims process, providing nearly automatic clearance for simple, straightforward cases and immediate expert supervision on the most complicated or dubious claims.
However, even the most experienced insurers often face claims that just seem to defy clearing. Indeed, finding the right person for the right job is just one part of the game. The immediate identification of claims that present dubious or high-risk elements is fundamental. This is another key junction at which predictive analytics and big data come to the rescue. Instead of relying on the experience of a single individual, claims can be compared against the hundreds of thousands of other claims – or claims with comparable data – that have been processed by the company or industry.
In addition to all of this, another important point is that the use of big data and predictive analytics gradually improves an office’s collective knowledge and fortifies the experience of newbies and seasoned insurance claims inspectors alike, in a virtuous circular process.
While it’s clear that the human relationship and touch between insurers and their clients remains irreplaceable, especially in major accidents involving serious damage to property and lives or death, the promise of predictive telematics is to deliver greater precision and greater speed in the claims process. Telematics provides the professional insurer with the experience of decades of claims in a keystroke.
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