Most P&C insurers across the country are at various stages of upgrading their core claims management systems. A fortunate few have already completed the journey of an information technology (IT) transformation.
From automated workflows and straight-through processing, to strategic allocation of claims resources, these 21st century claims management systems are helping companies gain a competitive edge through the use of modern technology, enhanced key performance indicators, and key performance predictors. However, for many insurance companies, the claims IT transformation journey is just beginning. Numerous challenges can exist, such as gaining C-suite support for the significant investment, unraveling complicated legacy systems, competing IT resource priorities, the complexity of researching/selecting a processing system vendor, and determining how to connect with the cloud computing evolution.
Insurers can start the journey by thinking about the business problems the organization would like to solve today, in three years, and possibly five to 10 years down the road. Although an organization may not be ready to implement a claim predictive modeling solution immediately, it is important to consider the impact that advanced analytics could have on the claims organization and the claims handling process in the future. As analytical capabilities grow and continue to be adopted, market forces and competition will likely make predictive modeling table stakes on the claims side.
It is important to fully vet and understand the analytic horse power of the organization today, and where the organization would like to be in the future. The same current state and future state gap analysis should be performed on data collection; the metrics monitored and claim handling processes. A clear vision helps lay a foundation for analytics early on in the technology integration process. This is especially important as the most effective time for planning and design is at the start of the transformational process. Done effectively, analytics can have a game changing impact on various aspects of the claims lifecycle: claims assignment, special investigative unit (SIU) referral, medical case management, litigation, subrogation, escalation and, ultimately, claim settlement.
These questions should be considered during pre-planning and integration design for a new claims management solution, which will typically be able to invoke business rules based on captured data and in part, further facilitate claims management capabilities.
It is imperative to consider how data will be used while designing the future state operating model and claims business processes. The good news is claims organizations already have much of the data they need to begin building effective claim predictive models. Deloitte’s model building experience to date suggests that up to three quarters of good predictive variables come from internal insurance company data. The remaining predictive variables come from a number of external data sources.
By capturing a few more demonstrated data fields and integrating this information into models sooner rather than later, a model can provide greater predictive power by leveraging 50 to 100 variables taken from background information about the employee/claimant, the injury, external public databases, medical data and other external sources. In the end, predictive models can essentially act as decision support “eye glasses” for the claims adjuster’s mind.
As recently as three to four years ago, the notion of advanced claims analytics was largely conceptual in nature. Most claims organizations were considering internal analytics and performing core legacy technology replacement as separate and distinct business initiatives. Unfortunately, a number of companies weren’t effectively approaching the opportunity as an integrated program or portfolio of projects. When contemplating analytics enablement, some were primarily using core claims or basic claims data as the primary input variables for establishing basic routing guidance and business rules. At the time, this helped these companies build basic analytic capabilities, but likely left them short of where they should be to take full advantage of predictive modeling.