System and process for a fusion classification for insurance underwriting suitable for use by an automated system
First Claim
1. A computer implemented system for underwriting an insurance application based on a plurality of previous insurance application underwriting decisions, the system comprising:
- a plurality of decision engines where each decision engine generates an output result;
a fusion engine, where the fusion engine;
compares the output results of the plurality of decision engines; and
fuses the plurality of output results into a single fused decision;
a measurement engine for measuring at least a consensus of the fused decision;
an estimation engine to estimate the degree of confidence in the fused engine based on the measured consensus;
a production decision engine, where the production decision engine generates a production output result; and
a comparison engine, where the comparison engine compares the production output result with the fused decision; and
where each of the decision engine output results comprises a classification designation, and the fusion of the output results is based at least in part on a correlation between the classification designations of each of the output results.
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Accused Products
Abstract
A method and system for fusing a collection of classifiers used for an automated insurance underwriting system and/or its quality assurance is described. Specifically, the outputs of a collection of classifiers are fused. The fusion of the data will typically result in some amount of consensus and some amount of conflict among the classifiers. The consensus will be measured and used to estimate a degree of confidence in the fused decisions. Based on the decision and degree of confidence of the fusion and the decision and degree of confidence of the production decision engine, a comparison module may then be used to identify cases for audit, cases for augmenting the training/test sets for re-tuning production decision engine, cases for review, or may simply trigger a record of its occurrence for tracking purposes. The fusion can compensate for the potential correlation among the classifiers. The reliability of each classifier can be represented by a static or dynamic discounting factor, which will reflect the expected accuracy of the classifier. A static discounting factor is used to represent a prior expectation about the classifier'"'"'s reliability, e.g., it might be based on the average past accuracy of the model, while a dynamic discounting is used to represent a conditional assessment of the classifier'"'"'s reliability, e.g., whenever a classifier bases its output on an insufficient number of points it is not reliable.
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Citations
20 Claims
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1. A computer implemented system for underwriting an insurance application based on a plurality of previous insurance application underwriting decisions, the system comprising:
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a plurality of decision engines where each decision engine generates an output result; a fusion engine, where the fusion engine; compares the output results of the plurality of decision engines; and fuses the plurality of output results into a single fused decision; a measurement engine for measuring at least a consensus of the fused decision; an estimation engine to estimate the degree of confidence in the fused engine based on the measured consensus; a production decision engine, where the production decision engine generates a production output result; and a comparison engine, where the comparison engine compares the production output result with the fused decision; and where each of the decision engine output results comprises a classification designation, and the fusion of the output results is based at least in part on a correlation between the classification designations of each of the output results. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A computer implemented process for underwriting an insurance application based on a plurality of previous insurance application underwriting decisions, the process comprising:
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generating a plurality of decision output results for the application, where each of the decision output results is generated by a separate decision engine; comparing the plurality of decision output results; fusing the plurality of decision output results based at least in part on the comparison; measuring the fused decision for at least a consensus; estimating a degree of confidence in the fused decision based on the measured consensus; generating a production output result; comparing the production output result with the fused decision; and where each of the decision output results comprises a classification designation, and the fusion of the decision output results is based at least in part on a correlation between the classification designations of each of the output results. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A computer implemented system that generates a classification designation based on a plurality of output results, the system comprising:
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a plurality of decision engines where each decision engine generates an output result; a fusion engine, where the fusion engine; compares the output results of the plurality of decision engines; and fuses the plurality of output results into a single fused decision; a production decision engine, where the production decision engine generates a production output result; a comparison engine, where the comparison engine compares the production output result with the single fused decision to generate a comparison result; and an output portion that outputs the comparison result. - View Dependent Claims (18, 19, 20)
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Specification