By using predictive analytics, insurers can run through a number of scenarios, identify early signs of potential issues, and automatically flag situations that need special handling. These flagged claims can then be assigned to an appropriately skilled claims adjuster, mitigating losses and speeding resolution.

Without the insights available with the use of predictive analytics, insurers are only able to view the traditional claims data and make adjuster assignments based on workload and past resource allocation experience.

The use of predictive analytics in the fight against fraud has gained significant traction in recent years. Fraud rings are operating with highly sophisticated tools and techniques. The battle requires a mature analytics engine that is able to consolidate a view of relationships that encompass multiple unrelated parties. Insurers are using predictive analytics during the claims intake process to identify potential fraud. Using a view of the characteristics of a claim, the nature of the claim, the relationships of the parties involved, plus a number of other connected attributes, the insurer has more control and can proactively engage a special investigation unit review in cases where it is warranted.

Business development and marketing are other areas where insurers are reaping benefits by using predictive analytics. The pressure to grow books of business has insurers turning to more sophisticated models to determine what is possible and what will be the best fit for a particular company's strategy and capabilities.

Analytics are being used to help insurers define their options for profitable business models and to expose untapped opportunities. Once opportunities are prioritized, marketing departments are using predictive modeling to select target markets and design messages that will resonate with the desired audience.

While billing has not been a major part of the predictive analytics conversation in the past, things are rapidly changing. Attributes such as number of NSF's, statutory notices of cancellation, reinstatements, or bill plan changes can all be warning signals for potentially unprofitable risks. In most cases, the billing area is more of a contributor to predictive models than a user of predictive modeling results.

Modern insurers are realizing the real power of predictive analytics. The investments are paying top and bottom line dividends—big ones. SMA research shows that 38 percent of insurers are increasing their spending on predictive analytics technologies and tools. Predictive analytics will not diminish in importance; these highly sophisticated models will play a major role in determining winners and losers. Insurers must continue to gain experience in using predictive analytics.

The stakes are high. These sophisticated models, coupled with the right combination of internal and external data, are providing intelligence that is increasing profitability, decreasing cost, and improving operational efficiency.

Karen Furtado, a partner at SMA, is an expert in core systems and emerging technologies with a deep understanding of how insurers can connect solutions to business and IT requirements. Furtado can be reached at [email protected].