Method for constructing segmentation-based predictive models from data that is particularly well-suited for insurance risk or profitability modeling purposes

A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform a method for constructing segmentation-based models that satisfy constraints on the statistical properties of the segments, the method comprising:

• (1) presenting a collection of training data records comprising examples of input values that are available to the model together with the corresponding output value(s) that the model is intended to predict;
• and
• (2) generating, on the basis of the training data, a plurality of segment models, that together comprise an overall model, wherein each segment model is associated with a specific segment of the training data, said generating comprising performing optimization comprising:
• a) generating alternate training data segments and associated segment models using statistical constraints to guide the construction of the data segments in a closed-loop fashion so as to ensure that the resulting data segments satisfy the statistical constraints;
• and
• b) selecting a final plurality of segment models and associated segments from among the alternates generated.

A program storage device readable by a machine, tangibly embodying program instructions executable by the machine to perform a method for constructing segmentation-based models that satisfy constraints on the statistical properties of the segments, the method comprising:

• (1) presenting a collection of training data records comprising examples of input values that are available to the model together with the corresponding desired output value(s) that the model is intended to predict;
• and
• (2) generating on the basis of the training data, a plurality of segment models, that together comprise an overall model, wherein each segment model is associated with a specific segment of the training data, said generating comprising:
• a) generating alternate pluralities of data segments and associated segment models;
• and
• b) adjusting the alternate pluralities so that the resulting data segments satisfy the statistical constraints.

A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform a method for constructing segmentation-based models of insurance risks, the method comprising:

• (1) presenting a collection of training data comprising examples of historical policy and claims data;
• and
• (2) generating on the basis of the training data a plurality of segment models, that together comprise an overall model, wherein each segment model comprises a statistical model of insurance risk that is associated with a specific segment of the training data, said generating comprising:
• a) generating alternative pluralities of segment models in one of a top-down fashion and a bottom-up fashion;
• b) comparing said alternative pluralities of segment models using statistical likelihood scores based on statistical models of insurance risk;
• and
• c) selecting a final plurality of segment models and associated segments from among the alternates generated so as to optimize aggregate statistical likelihood scores for the plurality.

An apparatus comprising:

• (1) a receiver to receive a collection of training data records comprising examples of input values that are available to a model together with the corresponding desired output value(s) that the model is intended to predict;
• and
• (2) a calculator to generate, on the basis of the training data, a plurality of segment models, that together comprise an overall model, wherein each segment model is associated with a specific segment of the training data, wherein the generation of said plurality of segment models comprises an optimization process comprising:
• a) generating alternate training data segments and associated segment models, each said generated segment having been evaluated to determine whether it satisfies at least one statistical constraint;
• and
• b) selecting a final plurality of segment models and associated segments from among the alternates evaluated that satisfy said statistical constraints.

A computerized method for constructing segmentation-based models that satisfy constraints on the statistical properties of the segments, the method comprising:

• presenting, to a computer, a collection of training data records comprising examples of input values that are available to a model, together with the corresponding desired output value(s) that the model is intended to predict;
• and
• based on said training data, automatically generating on said computer, a plurality of segment models, that together comprise an overall model, wherein each segment model is associated with a specific segment of the training data, said generating comprising performing optimization comprising:
• generating alternate training data segments and associated segment models, each said generated alternate training data segment having been determined to satisfy at least one statistical constraint;
• and
• selecting a final plurality of segment models and associated segments from among the alternates evaluated that satisfy said statistical constraints.

A method of at least one of managing and providing consultation for financial decisions, said method comprising at least one of generating, transmitting, receiving, and forwarding a report executed by a computer, said computer having executed a program of instructions to perform a method for constructing segmentation-based models that satisfy constraints on the statistical properties of the segments, the method executed by said machine comprising:

• (1) presenting, to a computer, a collection of training data records comprising examples of input values that are available to a model, together with the corresponding desired output value(s) that the model is intended to predict;
• and
• (2) based on said training data, automatically generating, on said computer, a plurality of segment models, that together comprise an overall model, wherein each segment model is associated with a specific segment of the training data, said generating comprising performing optimization comprising:
• a) generating alternate training data segments and associated segment models;
• b) evaluating at least one generated segment to determine whether it satisfies at least one statistical constraint;
• and
• c) selecting a final plurality of segment models and associated segments from among the alternates evaluated that satisfy said statistical constraints.