While predictive modeling has been used for years in underwriting, with ever-increasing sophistication and accuracy, it has now moved into other areas such as marketing and claims.

This growth stems from a combination of the wealth of data companies possess on their customers and the processing power now available across the board; statistical routines can be data intensive and only recently have 'mainstream' hardware and software been able to handle the workload.

To evaluate how to utilize predictive modeling most effectively, efficiently, and with the maximum amount of flexibility, insurance carriers need to consider the following key guidelines:

• Getting started—Utilize a narrow, focused approach to identify a target area, and generate the best results before broadening the approach further while also making sure the design is scalable to allow for quick expansion
• Availability of data—Review the availability of good, usable (not necessarily perfect) data, both internal and external to the organization, and including both historical and ongoing information
• Incorporate of models—Ensure that models are designed and incorporated into business processes and don't simply comprise of analysis of historical data after the fact
• Model management—Create a discipline around not only the initial model development but also around the on-going model maintenance

Leveraging the Models