Data mining based underwriting profitability analysis
First Claim
1. A computer implemented method of underwriting profitability analysis comprising the steps of:
- accessing existing databases of historical policy and claims;
initiating data mining techniques to the accessed data to extract rules that describe policy holders with homogeneous claim frequency and claim severity characteristics;
using the extracted rules to classify policy holders into distinct risk groups, each with its own set of characteristics, including pure premium; and
identifying sub-populations of policy holders that distinctly deviate from the expected normal pure premium to allow the insurance business analysts to interactively adjust the eligibility criteria and examine altered characteristics of the covered risk groups.
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Accused Products
Abstract
A computer implemented method of underwriting profitability analysis delivers the analytic process to a wide cross section of insurance decision makers. The underwriting profitability analysis system leverages an existing investment in databases and improves underwriting business processes. Data mining techniques are applied to historical policy and claims to extract rules that describe policy holders with homogeneous claim frequency and severity characteristics. These rule sets are used to classify policy holders into distinct risk groups, each with its own set of characteristics, including pure premium. Breaking up a book of business into segments allows identification of sub-populations of policy holders that distinctly deviate from the expected normal pure premium. This identification allow the insurance business analysts to interactively adjust eligibility criteria and examine altered characteristics of the covered segments until satisfactory. The system is implemented on a client server using network centric language technology.
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Citations
7 Claims
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1. A computer implemented method of underwriting profitability analysis comprising the steps of:
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accessing existing databases of historical policy and claims; initiating data mining techniques to the accessed data to extract rules that describe policy holders with homogeneous claim frequency and claim severity characteristics; using the extracted rules to classify policy holders into distinct risk groups, each with its own set of characteristics, including pure premium; and identifying sub-populations of policy holders that distinctly deviate from the expected normal pure premium to allow the insurance business analysts to interactively adjust the eligibility criteria and examine altered characteristics of the covered risk groups. - View Dependent Claims (2, 3)
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4. A computer implemented method of underwriting profitability analysis comprising the steps of:
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accessing existing databases of historical polic and claims; initiating data mining techniques to the accessed data to extract rules that describe policy holders with homogeneous claim frequency and claim severity characteristics; using the extracted rules to classify policy holders into distinct risk groups, each with its own set of characteristics, including pure premium; and identifying sub-populations of policy holders that distinctly deviate from the expected normal pure premium to allow the insurance business analysts to interactively adjust the eligibility criteria and examine altered characteristics of the covered risk groups, wherein the step of accessing existing databases comprises the steps of; displaying databases for selection by a user; responding to a user selection of a database by requesting data from data storage and computing univariate statistics; updating and parsing meta-data and returning updated and parsed meta-data in response to the data request; and generating and displaying a data summary of the returned updated and parsed meta-data. - View Dependent Claims (5, 6, 7)
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Specification