New Technology Is Transforming Insurance Claims Management and Traditional Auditing

Using technology to manage large volumes of data

Broadly speaking, the overall purpose of claims administration is to service the company’s obligations to its policyholders and third parties in a way that optimizes financial outcomes. The various processes that help execute these broad objectives (such as timely resourcing, skillful responses, and anticipatory internal and external interactions) must be balanced against other priorities (such as controlling administrative costs, customer expectations, and establishing and maintaining appropriate reserves).

The growing volumes of available claims-related data that are now available can help claims departments strike a balance and make better, more informed judgments about which claims merit alternative or additional actions. At the same time, however, the added volumes of data can complicate efforts, especially if claims departments already are struggling to find time to review currently available information. 

Consequently, insurance companies should establish a systematic method to quickly evaluate large volumes of data in order to determine how to allocate the department’s resources to achieve the greatest possible benefit. This effort is further complicated by the fact that much of the most useful, insight-rich data exists in unstructured sources such as interview notes, adjuster case commentaries, medical reports, and similar text-based formats. Mining unstructured data for indicative claims information can greatly improve the effectiveness of predictive models deployed. A growing number of claims departments are finding that advanced analytics tools such as AI and machine learning can help them more readily recognize and differentiate the critical indicators within their unstructured data sources. This shift is helping them more effectively identify and segment those claims that are likely to become severe or that exhibit complexities over the course of the claims-handling process. The potential benefits run across important management metrics such as volume, duration, average cost, and third-party provider expenses.

New roles for technology in claims auditing

In addition to improving the general effectiveness of the claims-handling process, advanced analytics also can lead to both improved effectiveness and lower costs for conventional claims and compliance auditing. 

Traditional auditing typically begins by selecting a sample of the total population to be audited. Often this sampling methodology is fundamentally random, with very little bias toward choosing audit samples based on likely risks. AI, on the other hand, can empower audit teams to review and test populations in their entirety, looking for anomalies or exceptions that could be indicators of higher risk. These improvements can be compounded with added text analysis and keyword search, which can introduce even more power and insight into the audit process.

Other recent technological advantages can add efficiency to the process. For example, rather than manually reviewing randomly selected claims for evidence of proper controls and practices, automated claims auditing technological can enable much of the work to be done virtually and on a continuous basis. This automation translates into faster and more frequent assessments, which will have a positive impact on overall audit effectiveness.

Preparing for the future

Even with all the potential advantages of recent technology advances, it must be noted that technology alone is not a cure-all. Introducing new technology into claims management and auditing processes can be a costly and ineffective undertaking if the process is not well-planned as part of a larger performance improvement effort.

In most organizations, the effort is best carried out in a series of phases, beginning with a thorough initial assessment of the overall claims management and claims auditing processes, as well as the individual elements of each. This initial assessment involves thoroughly understanding the current state of claims practices and policies, organizational structure, roles and responsibilities, workloads and workflows, critical performance metrics, and overall outcomes measurements. The purpose is to help management and the technology team determine where technology can be best applied and which elements of the process are most capable of and amenable to improvement.

Once there is an appropriate foundation, redesigning processes and methodologies, reassessing resources, and developing prototypes and models will provide the operational requirements needed to develop a prioritized, sequenced delivery plan or road map for implementation.

While such a carefully phased effort might appear daunting at first, experience indicates that with appropriate buy-in and commitment, the effort can be carried out at a reasonable pace and can begin to produce operating results quickly.

When existing systems and processes are adequately prepared, and when new technology projects are carefully planned, today’s advances in data analytics, AI, and machine learning have the potential to transform insurers’ claims processing and auditing practices and enable greater efficiency, enhanced effectiveness, and improved results.