The new age of analytics has led to the development of a varietyof solutions that leverage internal and external claims-relateddata to enable claims handling resources to take preventativeactions earlier in the life cycle of a claim. From first notice ofincident (FNOI), and resource assignment, to the identification ofpotentially fraudulent claim activity, models are changing theclaims handling world. However, one critical component of helpingorganizations achieve maximum efficiency using predictive models is the upfront investment claim thatorganizations make to ensure that the models are implementedeffectively.

Modeling Basics

Claims predictive modeling combines internal claimcharacteristics and external third-party data to calculate amathematical score that allows claims to be segmented at FNOI andthroughout the life of the claim. Several hundred variables takenfrom background information about the employee, the work injury,external public databases, medical data, and other sources arestatistically tested to identify the 50 to 100 candidate variableswith the greatest predictive power. The final model’s variables aredetermined by leveraging a number of model development techniques(for example, correlation analysis, principal components analysis,variable prioritization, exploratory data analysis, and so on),iterative training, and testing of candidate predictive models toevaluate the statistical significance/confidence, robustness, andbusiness reasonability of the candidate variables.