December 2017 – Being Predictive & Analytical

RussB

In our experience, many insurers are engaged in either consuming or producing predictive analytics in an attempt to produce better returns. These insurers have a strong point of view about build-versus-buy and have addressed the questions introduced in this article.

The only question that remains is whether it is working. The answer is, they aren’t saying.

1. Does your firm have access to a statistically significant, reasonably accurate set of data about the risk, the process or the prices that you want to predict? Can that data be refreshed frequently enough to be relevant? Simply having the coded data from the claims experience of your current policyholders may not yield much.

2. Do you have business leaders who have hypotheses about where the business could improve its performance? Simply looking at correlations won’t derive a conclusion. Predictive analytics initiatives are much more valuable and conclusive when they start with a hypothesis and then iterate new hypotheses. In short, those leaders have to realize that every answer begets a new question for quite some time. The data reveals nothing.

3. Do you have the brand, culture, and capability to build and retain a staff of data scientists? Or should you focus instead on buying scores and other insight that provides a great deal of the value at a predictable, risk-free cost? The classic build-versus-buy decision is just as present in predictive analytics as it is in other aspects of information technology.

4. Are you willing to invest in creating a virtuous cycle where future investments are paid for by the gains from prior initiatives? It’s true that storage, processing and tools have never been cheaper, but it’s equally true that the benefits take time to materialize and there are significant upfront people-costs to create a predictive analytics capability in house.

5. Lastly, are you willing to move from the lab to the field and actually put into practice the products, pricing, and risk selection that the predictive analytics team indicates are profitable? Or is this just another IT science project that checks a box on being innovative?

This article was reproduced with the kind permission of ITA Pro Magazine.

Russ Bostik,  Managing Partner at MVP Advisory Group, can be contacted at russ.bostick@mvpadvisorygroup.com