Over time, insurers amassed increasingly large amounts of data, and steadily improved their data management capabilities. Legacy systems were brought up to spec. Data silos were bridged. New regulatory reporting requirements were accommodated.
But these days, advances in technology are generating vast new sources of data, some of which appear to be very relevant to decision-making for carriers. Consider just the technologies that bring geolocalization to risk assessment: routes, the insured, floods, weather the list goes on.
It is safe to say that both the volume of data and use-case opportunities have only one direction to go: up. This recognition brings an imperative, —a responsibility, even — for carriers to carefully examine the new sources of data and their content with a sharp eye. With each new appearance on the scene, big data announces new technological challenges and brand new business opportunities. Extracting knowledge from big data opens up a tremendous new field for any insurer that is aiming to evolve, to differentiate. As an industrywide inflexion point, the advent of big data should not go unnoticed.
For example, usage-based insurance (UBI) is an immediate opportunity that springs to mind. Ever since its early introduction by Progressive Insurance, this family of products has been gaining momentum. As an example of extreme micro-segmentation, UBI products require the analysis of large amounts of data to determine pricing based on individual usage. There ought to be a significant customer base willing to take advantage of this pricing model in exchange for information about driving habits.
The effectiveness of loss prevention programs are rooted as much in the expertise and experience of the individuals working a particular industry segment as they are on the hard data needed to back up a statistical approach to prevention. Yet, big data sources offer great potential for determining the dynamics of future losses. New potential sources include social media streams, video streams of commercial site locations and actual “hard” data from control systems ranging from HVAC to large industrial process control devices.
High-speed communications infrastructures further facilitate the possibilities for aggregators to “listen” to what machines are saying, and stay on top of potential claims-generating failures. This is the realm of industrial big data, a tremendous opportunity for developing new commercial insurance products.
Big data can also provide additional lift to fraud detection models — currently limited by the amount of situational, contextual and actual risk attributes that can be associated with claim events. If harnessed and mashed correctly to claim-event descriptions, for example, big data sources such as social media feeds may enhance the situational and time contexts of a claim event, revealing patterns and pointing to potentially willful misrepresentations, — fraud, in short. Social media feeds may not always be factual, but anecdotal evidence indicates that they can nudge an investigation in the right direction. In fact, police forces around the world have been finding this type of data very useful to help their investigation process.
Customer service portals also can take advantage of big data sources. For example, a personal property insurance carrier could incorporate publicly available data on public services by geographical area to help clients select a neighborhood for their next home. This data, mashed up with criminality rates and a carrier’s own claims data offers a perspective for clients that will certainly create a positive impression of the carrier, driving retention or attracting new clients in a very natural manner.
The second part of this blog will examine the challenges presented with the data deluge — from the purely technical to process- and change-management challenges, to outright cultural and sociological impacts.
Philippe Torres is a founding partner of InEdge, a consultancy specializing in analytics for the insurance industry.
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