System and process for multivariate adaptive regression splines classification for insurance underwriting suitable for use by an automated system
First Claim
1. A process for underwriting an insurance application based on a plurality of previous insurance applications and their associated underwriting decisions comprises:
- creating a plurality of binary classifiers based on a structured methodology of multivariate adaptive regression splines (“
MARS”
), where the plurality of binary classifiers are arranged in a parallel network;
identifying a relevant set of MARS variables and parameters based on the plurality of previous insurance applications and their associated underwriting decisions;
performing at least one cross-validation technique and at least one re-sampling technique on the plurality of previous insurance applications and their associated underwriting decisions;
modifying the plurality of binary classifiers based on the performance of the at least one cross-validation technique and the at least one re-sampling technique; and
utilizing the validated parallel network for outputting a classification assignment for the at least one new insurance application.
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Abstract
A method and system for automating the decision-making process used in underwriting of insurance applications is described. While this approach is demonstrated for insurance underwriting, it is broadly applicable to diverse decision-making applications in business, commercial, and manufacturing processes. A structured methodology is used based on a multi-model parallel network of multivariate adaptive regression splines (“MARS”) models to identify the relevant set of variables and their parameters, and build a framework capable of providing automated decisions. The parameters of the MARS-based decision system are estimated from a database consisting of a set of applications with reference decisions against each. Cross-validation and development/hold-out combined with re-sampling techniques are used to build a robust set of models that minimize the error between the automated system'"'"'s decision and the expert human underwriter. Furthermore, this model building methodology can be used periodically to update and maintain the family of models if required to assure currency.
171 Citations
44 Claims
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1. A process for underwriting an insurance application based on a plurality of previous insurance applications and their associated underwriting decisions comprises:
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creating a plurality of binary classifiers based on a structured methodology of multivariate adaptive regression splines (“
MARS”
), where the plurality of binary classifiers are arranged in a parallel network;
identifying a relevant set of MARS variables and parameters based on the plurality of previous insurance applications and their associated underwriting decisions;
performing at least one cross-validation technique and at least one re-sampling technique on the plurality of previous insurance applications and their associated underwriting decisions;
modifying the plurality of binary classifiers based on the performance of the at least one cross-validation technique and the at least one re-sampling technique; and
utilizing the validated parallel network for outputting a classification assignment for the at least one new insurance application. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A process for underwriting an insurance application based on a plurality of previous insurance applications and their associated underwriting decisions comprises:
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digitizing the insurance application and the plurality of previous insurance application underwriting decisions;
generating a casebase of the plurality of previous insurance application underwriting decisions;
creating a plurality of binary classifiers based on a structured methodology of multivariate adaptive regression splines (“
MARS”
), where the plurality of binary classifiers are arranged in a parallel network;
identifying a relevant set of MARS variables and parameters based on the plurality of previous insurance applications and their associated underwriting decisions;
performing at least one cross-validation technique and at least one re-sampling technique on the plurality of previous insurance applications and their associated underwriting decisions, where the at least one resampling technique further comprises;
partitioning data from the previous insurance applications and their associated underwriting decisions into five groups of equal size;
removing one of the five groups; and
combining the remaining four groups in a development sample.;
modifying the plurality of binary classifiers based on the performance of the at least one cross-validation technique and the at least one re-sampling technique;
utilizing the validated parallel network for outputting a classification assignment for the at least one new insurance application; and
fusing the classification assignment for the at least one insurance application with at least one other classification assignment for the insurance application, where the at least one other classifier is generated by at least one other classifier. - View Dependent Claims (9, 10, 11)
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12. A computer readable medium having code for causing a processor to underwrite an insurance application based on a plurality of previous insurance application underwriting decisions, the medium comprising:
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code for creating a plurality of binary classifiers based on a structured methodology of multivariate adaptive regression splines (“
MARS”
), where the plurality of binary classifiers are arranged in a parallel network;
code for identifying a relevant set of MARS variables and parameters based on the plurality of previous insurance applications and their associated underwriting decisions;
code for performing at least one cross-validation technique and at least one re-sampling technique on the plurality of previous insurance applications and their associated underwriting decisions;
code for modifying the plurality of binary classifiers based on the performance of the at least one cross-validation technique and the at least one re-sampling technique; and
code for utilizing the validated parallel network for outputting a classification assignment for the at least one new insurance application. - View Dependent Claims (13, 14, 15, 16, 17, 18)
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19. A computer readable medium having code for causing a processor to underwrite an insurance application based on a plurality of previous insurance application underwriting decisions, the medium comprising:
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code for digitizing the insurance application and the plurality of previous insurance application underwriting decisions;
code for generating a casebase of the plurality of previous insurance application underwriting decisions;
code for creating a plurality of binary classifiers based on a structured methodology of multivariate adaptive regression splines (“
MARS”
), where the plurality of binary classifiers are arranged in a parallel network;
code for identifying a relevant set of MARS variables and parameters based on the plurality of previous insurance applications and their associated underwriting decisions;
code for performing at least one cross-validation technique and at least one re-sampling technique on the plurality of previous insurance applications and their associated underwriting decisions, where the at least one resampling technique further comprises;
partitioning data from the previous insurance applications and their associated underwriting decisions into five groups of equal size;
removing one of the five groups; and
combining the remaining four groups in a development sample.;
code for modifying the plurality of binary classifiers based on the performance of the at least one cross-validation technique and at least one re-sampling technique;
code for utilizing the validated parallel network for outputting a classification assignment for the at least one new insurance application; and
code for fusing the classification assignment for the at least one insurance application with at least one other classification assignment for the insurance application, where the at least one other classifier is generated by at least one other classifier. - View Dependent Claims (20, 21, 22)
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23. A system to underwrite an insurance application based on a plurality of previous insurance application underwriting decisions, the system comprising:
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means for creating a plurality of binary classifiers based on a structured methodology of multivariate adaptive regression splines (“
MARS”
), where the plurality of binary classifiers are arranged in a parallel network;
means for identifying a relevant set of MARS variables and parameters based on the plurality of previous insurance applications and their associated underwriting decisions;
means for performing at least one cross-validation technique and at least one re-sampling technique on the plurality of previous insurance applications and their associated underwriting decisions;
means for modifying the plurality of binary classifiers based on the performance of the at least one cross-validation technique and the at least one re-sampling technique; and
means for utilizing the validated parallel network for outputting a classification assignment for the at least one new insurance application. - View Dependent Claims (24, 25, 26, 27, 28, 29)
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30. A system to underwrite an insurance application based on a plurality of previous insurance application underwriting decisions, the system comprising:
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means for digitizing the insurance application and the plurality of previous insurance application underwriting decisions;
means for generating a casebase of the plurality of previous insurance application underwriting decisions;
means for creating a plurality of binary classifiers based on a structured methodology of multivariate adaptive regression splines (“
MARS”
), where the plurality of binary classifiers are arranged in a parallel network;
means for identifying a relevant set of MARS variables and parameters based on the plurality of previous insurance applications and their associated underwriting decisions;
means for performing at least one cross-validation technique and at least one re-sampling technique on the plurality of previous insurance applications and their associated underwriting decisions, where the at least one resampling technique further comprises;
partitioning data from the previous insurance applications and their associated underwriting decisions into five groups of equal size;
removing one of the five groups; and
combining the remaining four groups in a development sample;
means for modifying the plurality of binary classifiers based on the performance of the at least one cross-validation technique and the at least one re-sampling technique;
means for utilizing the validated parallel network for outputting a classification assignment for the at least one new insurance application; and
means for fusing the classification assignment for the at least one insurance application with at least one other classification assignment for the insurance application, where the at least one other classifier is generated by at least one other classifier. - View Dependent Claims (31, 32, 33)
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34. A system to underwrite an insurance application based on a plurality of previous insurance application underwriting decisions, the system comprising:
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a classifier module for creating a plurality of binary classifiers based on a structured methodology of multivariate adaptive regression splines (“
MARS”
), where the plurality of binary classifiers are arranged in a parallel network;
an identification module for identifying a relevant set of MARS variables and parameters based on the plurality of previous insurance applications and their associated underwriting decisions;
a processor for performing at least one cross-validation technique and at least one re-sampling technique on the plurality of previous insurance applications and their associated underwriting decisions, and where the classifier module modifies the plurality of binary classifiers based on the performance of the at least one cross-validation technique and at least one re-sampling technique to build a robust set of binary classifiers; and
an output module utilizing the validated parallel network for outputting a classification assignment for the at least one new insurance application. - View Dependent Claims (35, 36, 37, 38, 39, 40)
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41. A system to underwrite an insurance application based on a plurality of previous insurance application underwriting decisions, the system comprising:
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a digitizer for digitizing the insurance application and the plurality of previous insurance application underwriting decisions;
a storage module for creating a casebase of the plurality of previous insurance application underwriting decisions, where the processing occurs based at least in part on the contents of the casebase;
a classifier module for creating a plurality of binary classifiers based on a structured methodology of multivariate adaptive regression splines (“
MARS”
), where the plurality of binary classifiers are arranged in a parallel network;
an identification module for identifying a relevant set of MARS variables and parameters based on the plurality of previous insurance applications and their associated underwriting decisions;
a processor for performing at least one cross-validation technique and at least one re-sampling technique on the plurality of previous insurance applications and their associated underwriting decisions, where the classifier module modifies the plurality of binary classifiers based on the performance of the at least one cross-validation technique and at least one re-sampling technique to build a robust set of binary classifiers and where the at least one resampling technique further comprises;
partitioning data from the previous insurance applications and their associated underwriting decisions into five groups of equal size;
removing one of the five groups; and
combining the remaining four groups in a development sample;
an output module utilizing the validated parallel network for outputting a classification assignment for the at least one new insurance application; and
a fuser for fusing the classification assignment for the at least one insurance application with at least one other classification assignment for the insurance application, where the at least one other classifier is generated by at least one other classifier. - View Dependent Claims (42, 43, 44)
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Specification