Given a set of desired business outcomes and the predictive models as inputs, prescriptive analytics will recommend the decisions to be made in order to achieve the optimal results.  With each generation, analytics technology has not just improved in power and value, but has quickly become a necessity for insurers to remain competitive and agile.  However, many insurers have yet to discover this paradigm shift in decision-making through the use of big data and the latest generation of analytical tools.

To illustrate the value of big data combined with prescriptive analytics this article will take a closer look at pricing decisions.  One of the most important decisions an insurer makes is around pricing. The main factor in the pricing decision is related to the policy holders' risk.  After all, if an insurer doesn't get the risk right, little else matters.

Yet, optimal pricing presents a predicament as there is more to the pricing decision then getting the risk component correctly.  What are competitors doing relative to pricing?  How do I make sure I don't leave money on the table by setting premium too low?  If I set premium too high, do I risk losing market share?  How do I determine pricing for maximum return while staying in good favor with the regulators?

Companies have multiple business objectives that are driving change. For instance, profitability of the book of business might be the utmost goal; yet increasing market share in particular segments while reducing operational costs and risk levels may also be key objectives. To achieve ideal business performance, one needs to consider all of the options when trying to determine an optimal price.  And, determining the best price is simply not possible without the right technology.

Using 'what-if' predictive analysis tools provides a good start if we were talking about changing a single or a few factors within a rate order. However when it comes to rating orders with tens of tables and hundreds of parameters, predictive analytics stops short of optimal decision making and is too labor intensive.

It is not humanly feasible to review all the scenarios with so many 'moving parts' in the form of variables and parameters. Much like an Internet search engine, prescriptive analytics automates this process in an efficient and timely manner converging to the optimal solution, thus enabling the decision maker to hone in the suggested rate changes that will achieve the desired business goals while adhering to all the regulatory and business constraints. .

At first glance, prescriptive analytics has a lot of skeptics.  There is often disbelief of the capability of the technology to offer the best decision for achieving the desired business outcome, while others believe the technology may be too complex to effectively deploy within the organization. But, consider the concept of credit scoring. Not that long ago, it seemed awkward for financial institutions to evaluate a customer's risk level based on a number crunched using some complex calculation based on some wide range of data. It turned out that the early adopters of credit scoring as a risk metric enjoyed the early competitive advantage.   While there is always an element of risk in being on the forefront of new technology adoption, insurers who embrace prescriptive analytics sooner rather than later will also gain the early advantage, while those who wait on the sidelines will eventually adopt prescriptive analytics to remain viable—much like the adoption cycle of credit scoring.

Evidence of the increasing adoption rate for analytics technologies can be found in recently published research from both Earnix and Strategy Meets Action (SMA).  An  Earnix survey of the top insurers in North America showed that close to half of the top 25 auto insurers are either currently using or about to employ a price optimization strategy—feasibly possible only through the use of prescriptive analytics.

Data and Analytics in Insurance: P&C Plans and Priorities for 2013 and Beyond, a report published in May, 2013 and authored by Mark Breading, a partner with SMA, indicated that almost half of all P&C insurers are now investing in predictive models, with 47 percent of personal lines and 49 percent of commercial lines carriers investing, And, the SMA research also uncovered that almost one in four insurers have new analytics projects underway for pricing models.

Insurers who incorporate prescriptive analytics using price elasticity models as well as utilize fraud and underwriting models are gaining a significant advantage over their competitors. These insurers can better balance new business versus renewal business and design optimal growth strategies.

Insurance is a business of "survival of the fittest" and is soon to be "survival of the analytical."  The investment in people, process and technology of predictive and prescriptive analytics will evolve from a nice to have today to a necessity in the near future.