Abstract

Investing in customer data and analyzing customer behavior have helped companies across different industries improve their bottom-line significantly. However, this practice of data-driven decision making, while somewhat prevalent in the actuarial vertical of the insurance business is rare in the rest of the data rich insurance industry. In the early Nineties, Capital One revolutionized the credit card industry by building its entire business around informed intelligence- collecting customer-driven information, mining the data, and using predictive modeling techniques-to target distinct segments of the market with uniquely designed, relevant products. Other similar success stories include Wal-Mart, Best Buy and Harrah's-companies that have created repeated successes by investing time, resources, and establishing data-mining to better understand their customers' needs, and fulfilling them. In contrast, there has been little effort in the insurance industry to leverage the potential of customer information or of the data that it possesses.

We will outline significant areas of opportunity across the customer life-cycle for data mining and predictive modeling in the insurance industry (both Property and Casualty, and Health), and also detail a framework that will help business executives to make informed decisions.

The Power of Analytics

ANALYTICS AND INFORMATION-DRIVEN DECISIONING HAS allowed companies including Capital One, Wal-Mart, Best Buy, and Harrah's to re-write the rules of their industries and consistently generate superior bottomline results. An examination of these companies suggests some common themes:

End-to-End Value Creation: These companies have assessed the use of analytics across their entire business model and have focused on areas where maximum value can be captured. For example, some have used micro-segmentation of their customer prospect base and have tailored products to specifi c segments; while others have used analytics to weed out unprofi table customers; and some others have leveraged analytics to optimize their distribution and sales channel strategy.

Target with the Right Product at the Right Price Using the Right Channel: They have revised signifi cant parts of their business models and have been able to move beyond conventional industry practices in targeting, setting prices, product design or customer and channel management.

Executable Solutions: They have ensured that the back-end "rocket science" analyses fi nally emerge as simple and actionable business decision rules that the front-line staff can execute upon.

Test and Learn Culture: They have created a fact-based, data-driven company culture by incorporating analytics in almost all decision making.

ALL OF THESE COMPANIES COMPETE IN INDUSTRIES WHERE there is an abundance of customer-level transactional data. Considering insurance companies collect granular data on customers, policies and claims, there is a tremendous upside to investing in analytics: strong bottom-line improvements. This paper will outline a framework of effectively leveraging analytics. It will also address some of the concerns that insurers frequently raise:

• Lack of extensive, horizontal data can render analytics nearly impossible for some companies such as those underwriting specialized commercial insurance risks (e.g., large amusement parks)The highly regulated nature of setting prices
  may provide little or no pricing fl exibility
• Lack of clarity and information about the customer may limit availability of data (for example, in the health insurance segment, the end customer can be the member, the company or the broker)
• Actuaries have already built time-tested pricing models by applying sophisticated statistical tools. Incremental benefi t from additional analysis is not clear