Grant: Engage Grants Program

As a medical and life insurance business that has existed for over 65 years, Medavie Blue Cross has developed a large database of information which is continuously being updated with new monthly information for its billing and claims process. Rule sets built by domain experts are used to search the information for potentially fraudulent claims. A flexible system to allow for modification of the rule sets while still efficiently extracting information based on those rule sets and finding new rule sets by exploring the extremely large historical data are needed. Computational analysis techniques on efficient processing to maintain precision while reducing processing time and storage will be explored. By reducing search times without significant impact on storage or precision, this will allow for expanded explorations of new rules and greater numbers of claims in a timely manner. Numerical computational methods have been used for decades in the field of engineering to develop efficient, precise solutions to modeling complex mechanical and natural systems. The utility of this study for Medavie Blue Cross and medical and life insurance businesses would be to reduce load on time and storage resources to free up the domain experts to conduct more, different types and expanded scope searches for the identification of current and new fraudulent cases. The reduction in costs to the businesses by saving on the loss due to fraud is the main potential benefit. A significant factor in reducing the losses is catching the potential fraud as early as possible before the next month's claim is processed, typically on a monthly basis. This is due to the greater difficulties inherent in recovering funds that have already been paid on claims rather than simply denying payment to begin with. So any reduction in processing time can directly reduce costs to the business.