Competitive pricing, stringent regulatory reporting and retention remain continuous challenges for insurers and, for these reasons and more, predictive analytics continues to be squarely in their radar screen as a critical differentiating capability. However, to date, few insurers have been able to unlock the full potential of this capability.
It is no longer a question of finding the right price for the risk. Risk assessment and pricing has evolved into a scientific subject area which at the same time continues to perplex insurers in this world of increasing uncertainty. Insurers need to look deeper and broader in their use of analytics for risk assessment as well as identifying, growing and retaining potential customers.
Insurers need to move from broad segmentation of risk such as age, weight or health risks (individuals) and industry, size or location (corporations), to a more granular level "test cells," using many such factors simultaneously. While predictive scoring achieves this, it traditionally supplants and fails to leverage the actuarial experience gathered over decades. Insurers need to use a hybrid approach that combines scores and traditional actuarial practices. This is where predictive analytics comes into play.
Use of analytics needs to expand beyond risk management alone. One of the most underleveraged areas we found is in product development, where analytics is looked at in the context of underwriting. Other areas that are typically underleveraged are loyalty, cross sell/up-sell and enterprise performance management.
Property-casualty or life-health insurance may have different industry dynamics, but the need for better intelligence continues to be the same.
For life insurers, it may be 20%-30% cheaper to grow the portfolio through an acquisition than through a traditional agency channel. This has always been driven by the fact that life & health insurance is "sold," not "bought." Given the high expense of acquiring a new customer, two factors become critical for an insurer: better understanding of the key life events of a customer that warrant a sale, and proactively managing the retention of these customers.
Retention is no longer a given in life & health insurance. A good example is a product like term life, where a customer has the choice to shop for the best price through the push of a button. This has made the market fiercely competitive, causing carriers to think of innovative ways of designing the right product for the customer. An example can be taken from the credit card market where the players who have emerged as the leaders have perfected the use of predictive analytics to balance the customer's price sensitivity with overall product profitability.
Furthermore, being able to manage retention requires establishing a process that allows an insurer to act before it is too late. In order to do so, we have found that it is important to look at three key areas.
