Predictive Modeling Using Corticon BRMS

DotThe Client

The client is the leading provider of technology-enabled claims and productivity management solutions in North America. It also facilitates financial and personal health and helps customers and consumers navigate complexity by designing and implementing customized programs based on proven practices and advanced technologies.

DotThe Challenges

The client aimed to deliver quality service in areas such as workers’ compensation, liability, property, disability and absence management. The client desired for a solution partner that would not just help them in managing claims but will also simplify the process and reduce the complexities, to achieve operational excellence and making it easy and effective for everyone involved.

 

Their business operations involved a process where a specific dynamic query was used that ran on a monthly basis, in order to extract data related to claims. These extracted file’s information was then used to create Statistical Analysis System (SAS) reports based on a percentile score that helped to identify and distinguish various claims and were analyzed by the supervisors to take appropriate proactive action.

 

The process involved heterogeneous systems and manual intervention, hence they lacked scalability. The categorization of claims followed a 30-day cycle, critical claims were discovered on the 30th day and the high-risk claims were not identified on time. This resulted in a delay of the whole cycle of proactive action. In addition, there was a need to improve the cycle time of the claim process and minimize the manual procedures.

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DotThe Objective

The client being a leading global provider of claim management services, risks and benefits solutions was required to deliver cost-effective risk consulting, managed care and other services. They sought for Predictive Analytics Capability that would help the claim examiners anticipate the likelihood of future events, lowers claim handling costs and help them with more accurate decisioning.

DotThe Solution

JK Tech re-engineered the business process by converting the existing system into an automated predictive modeling system using Corticon Business Rules Management System (BRMS) and OpenEdge. The focus was on reducing the turnaround time per claim and effective claim management.

 

The redesigned system would check for all open claims in the system and calculate a predictive score as per the rules defined for the predictive analytics model and identify or predict whether a claim belongs to a certain category or not. The new process now ran daily and in case there is any change to any critical claim field, then that claim was recorded. The System was now having the capability to alert the authorized user regarding the potential critical category of the claim.

 

Predictive Modeling removed the heterogeneous systems and the manual interventions were automated with the help of Corticon Studio and Corticon Server using SOAP services. In addition, the current manual process of identifying the high-risk claims was also automated, which helped the business to take proactive decisions and actions which in turn added more value to the business. Predictive Modeling used Corticon as Business Rule Engine to implement the business rule for predicting the high-risk claims and future attorney involvement claims.

 

MAJOR HIGHLIGHT

“The project was awarded in CIO 100 event in Colorado Spring, USA in August, 2017”

DotThe Benefits

  • The authorized user/Analytics team has a new capability to identify a potential critical category of a claim early on to help take proactive decisions/actions, bringing business value.
  • Reduced operational costs as now the task of identifying the potential high-risk claims is are automated.
  • Increased organizational agility – compressed the change cycle from months to days.
  • Improved claim management process with superior customer experience, operational excellence and managed risk.
  • With the help of predictive analytics modeling, the team could generate faster and accurate results that are actionable for managing resources in claims management.


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