For many carriers, the task of processing claims consistently and in accordance with new programs has become complex and daunting for both adjusters and the IT organizations that support them. The rules governing critical claim-handling processes are often cumbersome and managed manually by professional staff. In those instances where rules are automated, usually they are implicit within custom code scattered across multiple applications. Implicit business rules and policies prevent organizations from becoming agile, since even simple claim processing rule changes to such a system can take weeks or even months. The lack of a centralized, explicit, and auditable rule repository will eventually result in inconsistent claim handling and outcomes across the company. Faced with regulatory compliance issues, this can lead to fines and, worse, the loss of an unhappy customer to the competition.

Insurers are increasingly turning to Business Rules Management Systems (BRMS), or business-rules engines to address these challenges. Simply put, a BRMS is a software system that explicitly represents the business logic and policies in a format that can be understood by an actuary or business analyst. Business rules and policies are then managed externally and in parallel with other application software assets. Managing rules outside of the application, a BRMS increases agility and drives the automated decision-making capabilities of applications, such as claim processing, so that they can be changed more quickly and easily by the actuary instead of a programmer. As a result, a BRMS enables actuaries or business analysts to customize claim policies, programs, and other business decision criteria associated with the claim processing function almost in real time. Since the claim process is inherently rule-driven, doesn't it make sense to automate the rules so that highly skilled adjusters do not waste precious time repeating manual processes?

In claim processing, a BRMS provides a repository for the business policies and the business logic that governs the claim-handling processes. Its decision-support capabilities enable the automation of such tasks as approving claims, assigning claims, instantiating best practices, accommodating regulations that often vary by state, scoring claims on a number of criteria including fraud detection, and specifying payment and settlement methods. In fact, the business rules approach supports an earlier and more aggressive form of fraud detection, since predetermined triggers can be easily integrated into the system and more quickly and easily modified as methods used to attempt fraud are changed.