TECHNICAL FIELD

The present disclosure relates to prediction by machine learning models, and more particularly to methods, computer program products, and systems for service cost prediction using machine learning.

BACKGROUND

Conventionally, entities offering a service for a fee determine some fixed levels of fees for all subscribers based on an overall cost to provide the service to all subscribers and obtain a desired profit margin. Depending on the characteristics of the service, costs to provide the agreed service to each individual subscriber vary greatly from subscriber to subscriber. The amount of data and the type of information that can be used to make plans on the service cost in the future increases and diversifies significantly over time while the service is being subscribed.

SUMMARY

The shortcomings of the prior art are overcome, and additional advantages are provided, through the provision, in one aspect, of a method. The method includes, for instance: forming, by one or more processors, a claims risk model for predicting a per member per month cost based on variables trained in early prediction models and variables of claim record data from data sources; generating, by the one or more processors, a first dataset for a capped submodel and a second dataset for an outlier submodel by modifying a training dataset for the claims risk model based on a preconfigured cap value; training, by the one or more processors, the claims risk model with the first dataset for the capped submodel, resulting in the capped submodel; training, by the one or more processors, the claims risk model with the second dataset for the outlier submodel, resulting in the outlier submodel; producing, by the one or more processors, a first cost predicted by the capped submodel and a second cost predicted by the outlier submodel; interpolating, by the one or more processors, the first cost predicted by the capped submodel and the second cost predicted by the outlier submodel from the producing by use of a splice model including a plurality of splice variables that fit datapoints in the training dataset at a predetermined number of splice points between the first cost and the second cost; and predicting, by the one or more processors, the per member per month cost for a member by running an individual dataset for the member on the capped submodel and the outlier submodel, and then running the splice model that redistributes the outcomes of the capped submodel and the outlier submodel.

Additional features are realized through the techniques set forth herein. Other embodiments and aspects, including but not limited to computer program products and systems, are described in detail herein and are considered a part of the claimed invention.

