System and method for determining expected loss using a machine learning framework

A non-transitory storage medium comprising instructions executable by a processor, the instructions comprising steps for the processor to:

• receive a set of input claims having a dataset defining each claim;
• extract a set of features associated with each input claim derived from the dataset;
• apply, for each input claim, a prediction model to predict a loss cost based on extracting the set of features and to infer a claim type of the input claim as related to a segment type, wherein applying the prediction model comprises:
• applying a first prediction model for predicting a claim frequency for each input claim from the set of features;
• applying a second prediction model for predicting a claim severity for each input claim from the set of features;
• and
• applying a product of each predicted one of the claim frequency and the claim severity via a third loss cost model for identifying the loss cost for each input claim from the set of features based on the segment type.

A computer implemented method for predicting an expected loss for a set of claim transactions received for processing at a server, the computer implemented method comprising:

• (a) predicting, at a first prediction model, a claim frequency of the set of claim transactions over a given time period, the first prediction model trained using historical frequency data for an average number of claims and segment types, each segment type having corresponding subtypes further defining a type of claim;
• (b) predicting, at a second prediction model, claim severity of the set of claim transactions during the given time period, the second prediction model trained using historical severity data including average loss severity values based on the segment types and the corresponding subtypes;
• (c) identifying the expected loss for the set of claim transactions over the given time period by applying a product of outputs of the first prediction model and the second prediction model;
• and,
• (d) applying the first and the second prediction model, once trained, for predicting an expected loss for subsequent claim transactions associated with any one of the subtypes.

A computer program product comprising a non-transitory storage device storing instructions that when executed by at least one processor of a computing device predict an expected loss for a set of claim transactions received for processing at a server, and configure the computing device to:

• (a) predict, at a first prediction model, a claim frequency of the set of claim transactions over a given time period, the first prediction model trained using historical frequency data and segment types, each segment type having corresponding subtypes further defining a type of claim;
• (b) predict, at a second prediction model, claim severity of the set of claim transactions during the given time period, the second prediction model trained using historical severity data including average loss severity values based on the segment types and the corresponding subtypes;
• (c) identify the expected loss for the set of claim transactions over the given time period by applying a product of outputs of the first prediction model and the second prediction model;
• and,
• (d) wherein the first and the second prediction model, once trained for each of the types of segments and thereby trained for different peril types are applying the first and the second prediction model, once trained, to predict a expected loss for subsequent claim transactions associated with any one of the subtypes.