Data science is giving Azur a competitive advantage by delivering a better customer experience.
With Lara Korz – Chief Data Officer, Azur Insurance
Thanks for taking the time to speak to us, Lara. Please tell us a little bit about Azur.
Azur is technology-based platform which enables insurers to work more effectively with brokers and their end-user clients. Our mission is to help insurers digitally transform themselves. We believe that this will disrupt the market and ultimately change the way that insurance is sold; making it smarter and more responsive.
So improving the end-user experience sounds like it’s a key differentiator for you.
Yes, absolutely. Today’s customer demands a better user experience than legacy insurance technology platforms are able to deliver. The deep understanding we have of the insurance process from underwriting to risk assessment helps power our ability to deliver a best-in-class, modern, cloud-based platform to deliver that user experience. For example we’re about to be launch SMART Home, a new mid-market home insurance product and through our partnership with Outra we’ve managed to reduce our risk question set to just five questions! The application process is therefore simplified and speeded up significantly for our clients. After answering the questions online they are provided with an immediate quote and can go on to make a digital purchase all in the space of under two minutes. However, this is not to the detriment of the underwriting performance with 67 rating variables being captured for a robust actuarial model.
Clearly you are applying data science to good effect in terms of improving the insurance experience, but how about for improved business outcomes?
Data drives every single area of the business from managing our brokers’ metrics, and understanding key performance indicators of the portfolio for premiums and claims, to granular segmentation and business mix of individual variables such as driver age, property type, property age. These insights are all essential. They drive changes to the rating, business process and efficiencies and create differentiation against our competitors. For example we have built models that are proving to be predictive on scoring the likelihood of a new customer quote to convert and also on scoring the propensity of that customer to claim. There are a number of use cases currently being developed using predetermined buckets to triage the quotes through a light touch underwriting path. This will allow us to convert more, better performing business, tipping the portfolio to improve the loss ratio.
As data science becomes more mainstream, what are the challenges facing businesses that might be thinking of investing?
For us it’s the explosion of available data. This may sound counter-intuitive as insurance is an industry driven by data. However with the growing availability of open source data and non-insurance data providers realising their data value, the onslaught of data becomes a minefield to navigate through. No one data set is fit for purpose and to assess the true value takes dedicated time, resource and a full cost benefit exercise. Consequently it’s really important to choose partners carefully. Understanding who can help you derive business outcomes not just data insights is the key.
And finally what is your favourite outcome derived from data?
The majority of policyholders perceive fire or theft to be the biggest risk to their home and property when in fact, water damage is the most frequent and can be extremely disruptive. Fifty-four percent of households have experienced water damage, the average disruption time is six months and the average cost of a claim is £24k. Armed with this knowledge we have recently launched a new pilot initiative offering a water leak detector to end-user clients. It is already proving successful as ultimately risk management and prevention of losses is a benefit to both the consumer and the carrier.
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