 By Scott Thomson, Vice President, Decision Analytics, UK & Europe, EXLService
All new technology experiences a gap between the time it’s released and when it becomes widely used. When GPS first became popular, most people still left home with a road map or handwritten directions. Now, a few years later most of us trust our smart phones more than we trust ourselves. This leap could have happened faster. In the business world, this lack of trust in new tools can hold companies back. Early adopters who keep their minds open to all potential applications can propel themselves ahead of the competition. Most organisations try to balance being on the “leading edge” versus the “bleeding edge”.
Where does the insurance industry fall when it comes to analytics?
We’re now in a position in the insurance industry where platforms and analytics can fully exploit the big data that we’ve always had access to. However, the practical applications of analytics to help insurers, whether in the life or property and casualty spaces, is patchy; one man’s ‘predictive analytics’ is another man’s ‘business intelligence’. By this I mean there is still confusion around what analytics means and can deliver. Some organisations view analytics as simple business reporting.
For more aggressive adopters, the issue is not what to use analytics for, or the potential impacts, it’s where to use it and, more importantly, how to leverage it in their operations to drive core business strategies like improving customer acquisition, satisfaction and retention.
This is the real battleground: real-time decision making driven by analytics - and no insurers have fully achieved this yet. Telcos, retailers, and banks have achieved real-time to a degree bylooking into churn analytics and providing the next best offer, independent of channel. Insurance needs to do the same. Having accurate, real-time analytical models driving optimal decisions at each customer touch point is an absolute must in today’s market.
More and more insurers are questioning how to get the right decision, to the right person, at the right time, in a format they can use. Forget retrospective dashboards and KPI packs, the future requires predictive analytics to proactively alter business processes in a seamless and dynamic way. Consider the impact of a 3 per cent point change in recovery rates, or preventing customers from lapsing as a result of minor complaints. Just knowing the best outcome for these two decisions alone could save you in excess of £20million a year.
Everything exists to allow this; enriched data sources, insights, and the technology. The main obstacle,in getting information to the frontline,isgetting the experts needed to build and interpret what a company’s data is telling it.
The best claim handler can’t forecast every scenario each claim can take. Knowing about non-disclosure, preventing litigation or even suggesting an early offer could save a staggering £30million. It’s not about underwriting or claims management systems, it’s about making factual decisions at the right time when in direct contact with the customer.
Telematics opens up the world of insurance as a consumable, moving it from a commodity. It now offers far more than just monitoring your driving behaviour and is part of the way we live and can used to develop a new set of offers to the consumer.This is an important development – to insure only what you use, when you use it, and how you use it, otherwise known as Usage-based Insurance (UBI). The promise is of lower premiums, however, this will need to be monitored, and comparing premiums will be more difficult when they are changing on a daily basis.
In terms of analytics and telematics, again, this is a game changer. The big data theme around insurers understanding driving patterns, claims data, fraud, etc. is massive. The data gathered can even assist with the analytics for non-telematics customers, providing vital, additional data to improve premium setting and accuracy.
Consider this, experts estimate that insurers will need to collect 10,000 customer-years of data to be able to statistically correlate driving behaviour to their accidents. So does it mean good-bye to the risk-based rating factors and saying hello to a new set of personal risk-based questions?
As analytical software, applications and modelling improves - and becomes better trusted and understood -and the insurers master the art of data mining, the next step is uncovering how analytics is best applied. Would people be willing to buy a five-year motor policy? When making a claim, would it be a good time for people to buy additional products? Regarding risk, could the data from ‘club cards’ be combined with telematics data to look for correlations between shopping habits and driving style? Imagination is the only barrier.
Returning to the everyday GPS system -which previously struggled to get us to a conference in Milton Keynes from Reading, sometimes with an unexpected scenic trip across the Severn Bridge on the way -GPS as it exists today can now be found in golf carts, where integrated GPS rangefinders are tailored to specific golf courses, providing interactive course maps and live readings of distance measurements to the green. Insurers need to apply this same lateral thinking to analytics software and applications; those that do so now are likely to be leading the pack for years to come.
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