Claims organizations continue to fight the good fight, constantly balancing loss adjustment expense with controlling claims costs while also attempting to stay focused on the needs of the customer. Complicating matters are a shrinking talent base, challenging regulatory and legal environments, and claims technology that often is inadequate to get the job done. Yet despite this difficult environment, many claims operations have effectively instituted a brand of best practices and worked hard to squeeze out the "claim leakage." Ironically, claims organizations frequently are doing the right things but just not early enough in a claim's life to produce maximum results. To tackle this problem, industry leaders are turning to data analytics as an innovative approach to claims management.

Over the past decade, many insurance companies have utilized predictive models to improve dramatically their risk selection and underwriting results. Based on the potential business impact of prospective claim segmentation, insurers, third-party administrators, and self-insured employers now are turning their sights–and predictive models–on claims.

Claims management has long been more of an art than a science. The art is the expertise and experience claims adjusters develop over time, enabling them to investigate, administer, and settle claims effectively. Adjuster experience builds sharp instincts that influence the actions and intensity applied to a given claim, and yet there often are important insights that sit beyond even the most seasoned adjuster's line of sight. An extraordinary opportunity now exists to take the value associated with adjuster experience and competency and significantly enhance it by applying the science of predictive modeling. The result is a claims organization positioned to achieve breakthrough performance.