SYSTEMS AND MODELS FOR DATA ANALYTICS

摘要

Systems and methods are provided that allow for generating and applying an improved predictive data model that aggregates two or more models performed sequentially, for the purposes of improving the prediction of overall profitability of individuals or households in a population. The models may be generated by the processing of customer profitability data and third-party population data together. One of the two aggregated models may be an inherently probabilistic, binary model tasked with determining whether an individual is a high-loss individual and using that result to improve the predictive capability of the system.

主权项

1. A computing system comprising:
one or more data stores storing:
a first dataset including first data items associated with respective individuals of a first plurality of individuals; anda second dataset including second data items associated with respective individuals of at least some of the first plurality of individuals; a computer processor; and a computer readable storage medium storing program instructions configured for execution by the computer processor in order to cause the computer processor to:
perform a fuzzy match between the first dataset and the second dataset to identify a plurality of overlapping individuals associated with both the first dataset and the second dataset;generate a training data set including data items from the first and second data sets associated with at least some of the plurality of overlapping individuals;train, based on at least a subset of the training dataset, a first predictive model configured to determine a predicted profitability of an individual;train, based on at least the subset of the training dataset, a second predictive model configured to determine a predicted likelihood of disaster of an individual;access a third dataset including third data items associated with a second plurality of individuals;apply the first predictive model to the third dataset to determine predicted profitabilities of respective individuals of the second plurality of individuals;apply the second predictive model to the third dataset to determine predicted likelihoods of disaster of respective individuals of the second plurality of individuals; andfilter, based on the predicted likelihoods of disaster of respective individuals of the second plurality of individuals, the third dataset to determine a subset of the second plurality of individuals that are unlikely to experience a disaster.