Abstract: Systems and methods for segmenting claims data for actuarial modeling are disclosed. For example, traditional actuarial models determine the money paid out in a given month and estimate future months based at least in part on the money paid out, but these traditional models fail when dealing with volatile data for new markets and/or expanding member base. The presently-disclosed systems and methods segments data into incremental metrics and uses the segmented data to provide more granular information about the claims. Increasing granularity in data not only increases accuracy for predictions but the new actuarial model developed using segmented data may also be used as market analysis tools to predict favorable new markets to enter.

Abstract: Systems and methods for segmenting claims data for actuarial modeling are disclosed. For example, traditional actuarial models determine the money paid out in a given month and estimate future months based at least in part on the money paid out, but these traditional models fail when dealing with volatile data for new markets and/or expanding member base. The presently-disclosed systems and methods segments data into incremental metrics and uses the segmented data to provide more granular information about the claims. Increasing granularity in data not only increases accuracy for predictions but the new actuarial model developed using segmented data may also be used as market analysis tools to predict favorable new markets to enter.