The new age of analytics has led to the development of a variety of solutions that leverage internal and external claims-related data to enable claims handling resources to take preventative actions earlier in the life cycle of a claim. From first notice of incident (FNOI), and resource assignment, to the identification of potentially fraudulent claim activity, models are changing the claims handling world. However, one critical component of helping organizations achieve maximum efficiency using predictive models is the upfront investment claim that organizations make to ensure that the models are implemented effectively.

Modeling Basics

Claims predictive modeling combines internal claim characteristics and external third-party data to calculate a mathematical score that allows claims to be segmented at FNOI and throughout the life of the claim. Several hundred variables taken from background information about the employee, the work injury, external public databases, medical data, and other sources are statistically tested to identify the 50 to 100 candidate variables with the greatest predictive power. The final model’s variables are determined 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 to evaluate the statistical significance/confidence, robustness, and business reasonability of the candidate variables.