Patent Issued for Scoring Of Insurance Data (USPTO 10,672,078)

2020 JUN 12 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- From Alexandria, Virginia, NewsRx journalists report that a patent by the inventor Tagny Diesse, Patrick Christian (Chicago, IL), filed on May 19, 2014, was published online on June 15, 2020.

The patent’s assignee for patent number 10,672,078 is Allstate Insurance Company (Northbrook, Illinois, United States).

News editors obtained the following quote from the background information supplied by the inventors: “Insurance companies may maintain insurance data and account information for thousands or millions of customers. Each customer may be issued an insurance policy number and a premium amount based on the particular insurance policy. The premium amount for a particular insurance policy may be calculated based on a variety of factors, such as a territory factor (e.g., a zip code/location of an asset), age factor (e.g., number of months in the life of a person or asset), vehicle factor, claim history factor, and other factors known to a person having ordinary skill in the art.

“In order to calculate premium amounts and other rates, insurance companies may employ algorithms to score insurance data. In a traditional insurance system, each of the aforementioned insurance factors may be assessed, and the system obtains/assigns values for each factor. The system then engages in various calculations to determine an insurance rate/premium to assign to the insurance policy. There may be numerous insurance factors being assessed for thousands or millions of insurance policy-holding customers, resulting in a large amount of data (e.g., big data). The requisite calculations and scoring may entail processing the large amount of data and may necessitate substantial coordination with one or more systems and processor-intensive, time-consuming calculations. As such, there is room for improvement of these prior art systems and methods of scoring insurance rates/premiums.”

As a supplement to the background information on this patent, NewsRx correspondents also obtained the inventor’s summary information for this patent: “In light of the foregoing background, the following presents a simplified summary of the present disclosure in order to provide a basic understanding of some aspects of the invention. This summary is not an extensive overview of the invention. It is not intended to identify key or critical elements of the invention or to delineate the scope of the invention. The following summary merely presents some concepts of the invention in a simplified form as a prelude to the more detailed description provided below.

“Aspects of the disclosure address one or more of the issues mentioned above by disclosing methods, computer readable storage media, and apparatuses for providing a system architecture and/or tool configured for applying models to score insurance data. The disclosure describes a system comprising a master node, a plurality of nodes in at least one cluster connected to the master node, and one or more computing devices connected to the master node, wherein the master node is configured to perform one or more of the steps described herein. The disclosure also describes an apparatus comprising a processor and a memory storing computer-executable instructions that, when executed by the processor, cause the apparatus to perform one or more of the steps described herein.

“In addition, aspects of this disclosure provide a method that may include creating, by a computer processor, an input table in memory, loading, into the input table, insurance data of a plurality of customers, creating a first file directory path to an insurance scoring script and a second file directory path to a predictive model, and distributing, by the computer processor using a HIVE module, the insurance scoring script and the predictive model to each of a plurality of nodes in at least one cluster. The method may also include creating a results table in the memory, calling a function of the HIVE module to instruct each of the plurality of nodes to execute the insurance scoring script to generate scored results, and writing the scored results into the results table, wherein the scored results comprise insurance scores for the plurality of customers.

“Further, aspects of this disclosure provide an apparatus comprising a network interface configured to communicate, via a network, with at least one of a first computing device or a second computing device. The processor(s) may be configured to cause the apparatus to receive, from the first computing device or the second computing device, an insurance scoring script to score a distinct portion of insurance data and receive, from the first computing device or the second computing device, a predictive model. The processor(s) may also cause the apparatus to define model parameters for the predictive model, specify at least one variable, variable type, and categorical level corresponding to the insurance data, and score the distinct portion of the insurance data by applying the predictive model.

“Of course, the methods and systems of the above-referenced embodiments may also include other additional elements, steps, computer-executable instructions or computer-readable data structures. In this regard, other embodiments are disclosed and claimed herein as well. The details of these and other embodiments of the present invention are set forth in the accompanying drawings and the description below. Other features and advantages of the invention will be apparent from the description, drawings, and claims.”

The claims supplied by the inventors are:

“What is claimed is:

“1. A system comprising: a master node comprising a memory; a plurality of nodes in at least one cluster connected to the master node; and one or more computing devices connected to the master node, wherein the master node is configured to: create an input table in the memory; load, into the input table, insurance data associated with a plurality of customers; create a first file directory path to an insurance scoring script stored on at least one of the one or more computing devices, a second file directory path to a first predictive model stored on at least one of the one or more computing devices, and a third file directory path to a second predictive model stored on at least one of the one or more computing devices; create a results table in the memory, the results table including the input table, a first additional column, and a second additional column; assign, using a complementary group rating model and the insurance data, the plurality of customers to a respective tier among a plurality of tiers; call a function of a Hive module that: divides the insurance data into a plurality of data portions, wherein the insurance data is divided into the plurality of data portions based on a respective tier such that each of the plurality of data portions comprises a portion of the insurance data that is associated with one or more customers in a same tier, and wherein the master node delivers each of the plurality of data portions to a respective one of the plurality of nodes; and instructs each of the plurality of nodes to execute, in parallel, the insurance scoring script to sequentially apply the first predictive model and the second predictive model to a specific portion of the plurality of data portions to generate a first plurality of scored results and a second plurality of scored results, respectively, wherein the first predictive model and the second predictive model are different, wherein at least one of the first predictive model and the second predictive model comprises a generalized linear model, a generalized boosted model, or a regression model, wherein the second predictive model generates the second plurality of scored results based on the first plurality of scored results generated by the first predictive model, and wherein the first plurality of scored results comprises insurance premiums or insurance policy renewal rates; compile and write the first plurality of scored results into the first additional column of the results table and the second plurality of scored results into the second additional column of the results table; and output the results table, comprising the first additional column and the second additional column, to at least one of the one or more computing devices.

“2. The system of claim 1, wherein the master node is further configured to receive a comma separated values file comprising the insurance data and parse the insurance data in the comma separated values file prior to loading the insurance data into the input table, and wherein the insurance data comprises insurance factors and a plurality of policy numbers corresponding to the plurality of customers.

“3. The system of claim 2, wherein the insurance factors comprise at least one of gender, age, area, income, type of home, cost of home, vehicle type, or claim history for each of the plurality of customers.

“4. The system of claim 1, wherein the insurance premiums or the insurance policy renewal rates are for a particular tier of the plurality of customers.

“5. A method comprising: creating, by a computer processor of a master node, an input table in a memory of the master node; loading, into the input table, insurance data of a plurality of customers; creating a first file directory path to an insurance scoring script, a second file directory path to a first predictive model, and a third file directory path to a second predictive model; creating, by the computer processor of the master node, a results table in the memory, the results table including the input table, a first additional column, and a second additional column; calling a function of a Hive module that divides the insurance data into a plurality of data portions and instructs each of a plurality of nodes in at least one cluster to execute, in parallel, the insurance scoring script that sequentially applies the first predictive model and the second predictive model to a specific portion of the plurality of data portions to generate a first plurality of scored results and a second plurality of scored results, respectively, wherein the insurance scoring script applies the first predictive model to generate insurance premiums or insurance policy renewal rates as the first plurality of scored results, wherein the first predictive model and second predictive model are different, wherein at least one of the first predictive model and the second predictive model comprises a generalized linear model, a generalized boosted model, or a regression model, wherein the second predictive model generates the second plurality of scored results based on the first plurality of scored results generated by the first predictive model, and wherein execution, in parallel, of the insurance scoring script comprises: simultaneously loading, by a first node among the plurality of nodes and a second node among the plurality of nodes, the first predictive model; simultaneously defining, by the first node and the second node, first model parameters for the first predictive model; and simultaneously generating, by the first node, a calculated score for each customer of a first portion of the plurality of data portions and generating, by the second node, a calculated score for each customer of a second portion of the plurality of data portions; compiling and writing, by the computer processor of the master node, the first plurality of scored results into the first additional column of the results table and the second plurality of scored results into the second additional column of the results table; and outputting the results table, comprising the first additional column and the second additional column, to one or more computing devices.

“6. The method of claim 5, wherein the writing comprises inserting all calculated scores into the first additional column of the results table.

“7. The method of claim 5, wherein the insurance data is divided into the plurality of data portions according to a number of the plurality of nodes in the at least one cluster.

“8. The method of claim 7, wherein dividing the insurance data is further based on a total file size of the insurance data.

“9. The method of claim 5, wherein the function of the Hive module comprises a Hive Transform function.

“10. The method of claim 5, wherein the insurance premiums or the insurance policy renewal rates are for a particular tier of the plurality of customers.

“11. The system of claim 1, wherein the first predictive model generates an insurance premium for each of the plurality of customers, wherein the second predictive model generates a renewal rate for each of the plurality of customers.

“12. The system of claim 11, wherein the first predictive model comprises the generalized boosted model, and wherein the insurance scoring script defines a number of trees for the first predictive model.

“13. The system of claim 1, wherein the first plurality of scored results comprises insurance premiums and the second plurality of scored results comprises renewal rates.

“14. A system comprising: a master node comprising a memory; a plurality of nodes in at least one cluster connected to the master node; and one or more computing devices connected to the master node, wherein the master node is configured to: create an input table in the memory; load, into the input table, insurance data of a plurality of customers; create a first file directory path to an insurance scoring script, a second file directory path to a first predictive model, and a third file directory path to a second predictive model; create a results table in the memory, the results table including the input table, a first additional column, and a second additional column; call a function of a Hive module that divides the insurance data into a plurality of data portions and instructs each of the plurality of nodes to execute, in parallel, the insurance scoring script that sequentially applies the first predictive model and the second predictive model to a specific portion of the plurality of data portions to generate a first plurality of scored results and a second plurality of scored results, respectively, wherein the insurance scoring script applies the first predictive model to generate insurance premiums or insurance policy renewal rates as the first plurality of scored results, wherein the first predictive model and second predictive model are different, wherein at least one of the first predictive model and the second predictive model comprises a generalized linear model, a generalized boosted model, or a regression model, wherein the second predictive model generates the second plurality of scored results based on the first plurality of scored results generated by the first predictive model, and wherein execution, in parallel, of the insurance scoring script comprises: simultaneously loading, by a first node among the plurality of nodes and a second node among the plurality of nodes, the first predictive model; simultaneously defining, by the first node and the second node, first model parameters for the first predictive model; and simultaneously generating, by the first node, a calculated score for each customer of a first portion of the plurality of data portions and generating, by the second node, a calculated score for each customer of a second portion of the plurality of data portions; compile and write the first plurality of scored results into the first additional column of the results table and the second plurality of scored results into the second additional column of the results table; and output the results table, comprising the first additional column and the second additional column, to one or more computing devices.

“15. The system of claim 14, wherein writing the first plurality of scored results comprises inserting all calculated scores into the first additional column of the results table.

“16. The system of claim 14, wherein the insurance data is divided into the plurality of data portions according to a number of the plurality of nodes in the at least one cluster.

“17. The system of claim 16, wherein dividing the insurance data is further based on a total file size of the insurance data.

“18. The system of claim 14, wherein the function of the Hive module comprises a Hive Transform function.

“19. The system of claim 14, wherein the insurance premiums or the insurance policy renewal rates are for a particular tier of the plurality of customers.”

For additional information on this patent, see: Tagny Diesse, Patrick Christian. Scoring Of Insurance Data. U.S. Patent Number 10,672,078, filed May 19, 2014, and published online on June 15, 2020. Patent URL: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=10,672,078.PN.&OS=PN/10,672,078RS=PN/10,672,078

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