Patent Issued for Data analytics system to automatically recommend risk mitigation strategies for an enterprise (USPTO 11983673): Hartford Fire Insurance Company
2024 JUN 05 (NewsRx) -- By a
The patent’s inventors are Day, Jennifer (
This patent was filed on
From the background information supplied by the inventors, news correspondents obtained the following quote: “Electronic insurance claim records may be stored and utilized by an enterprise, such as an insurance company. For example, an insurance company may be interested in analyzing information about risk drivers and insurance claim outcomes in each insurance claim record to model insurance claim outcomes based on different risk drivers. In some cases, the insurance company might want to advise customers how different identified risk drivers affect insurance claim outcomes and advise customers on adopting risk mitigation strategies for affecting insurance claim outcomes. Accordingly, the insurance company may add value to insurance products sold to customers by helping customers identify risk drivers that are affecting their insurance claim outcomes and their insurance costs. Further, the insurance company may add value to insurance products sold to customers by helping customers employ risk mitigation strategies that improve their insurance claim outcomes and reduce their insurance costs. Human analysis of electronic records to identify risk drivers, however, can be a time consuming, error prone and subjective process-especially where there are a substantial number of records to be analyzed (e.g., thousands of electronic records might need to be reviewed) and/or there are a lot of factors that could potentially influence insurance claim outcomes. In addition, this type of information may be spread throughout a number of different computer systems (e.g., associated with different insurance companies, a human resources department, etc.).
“It would be desirable to provide systems and methods for mining data to identify risk factors and for developing risk mitigation strategies in a way that provides fast and accurate results.”
Supplementing the background information on this patent, NewsRx reporters also obtained the inventors’ summary information for this patent: “According to some embodiments, systems, methods, apparatus, computer program code and means are provided for mining data to identify risk factors and for developing risk mitigation strategies in a way that provides fast and accurate results and that allow for flexibility and effectiveness when responding to those results.
“The present application is directed to systems and methods adapted to automatically analyze insurance claim records, automatically identify risk drivers, automatically identify how these risk drivers affect insurance claim outcomes and automatically provide risk mitigation strategies that improve insurance claim outcomes.
“In one embodiment of the present application, a data analytics system includes a data mining engine, a predictive analytics engine and a claims insight platform. The data mining engine analyzes a plurality of insurance claim files to identify flags corresponding to risk drivers. The predictive analytics engine calculates a risk score for each of the plurality of insurance claim files based on identified flags corresponding to risk drivers. The claims insight platform selects a subset of the plurality of insurance claim files, calculates an average risk score for the subset of the plurality of insurance claim files, and determines an expected claim outcome based on the calculated average risk score for the subset of the plurality of insurance claim files.
“In some embodiments, a data analytics system may include a first risk relationship data store containing electronic records that represent a plurality of risk relationships between the enterprise and a first risk relationship provider. Similarly, a second risk relationship data store containing electronic records that represent a plurality of risk relationships between the enterprise and a second risk relationship provider. A back-end application computer server may include a data mining engine that analyzes a set of electronic records in the first and second risk relationship data stores to identify flags corresponding to risk drivers. A predictive analytics engine may then calculate a risk score associated with the set of electronic records based on the associated entity attribute values and the identified flags corresponding to risk drivers. An insight platform may automatically generate a recommended action for the enterprise to lower the calculated risk score.
“Some embodiments comprise: means for analyzing, by a data mining engine of the back-end application computer server, a set of electronic records in a first and a second risk relationship data store to identify flags corresponding to risk drivers, wherein the first risk relationship data store contains electronic records that represent a plurality of risk relationships between the enterprise and a first risk relationship provider, and the second risk relationship data store contains electronic records that represent a plurality of risk relationships between the enterprise and a second risk relationship provider; means for calculating, by a predictive analytics engine of the back-end application computer server, a risk score associated with the set of electronic records based on associated entity attribute values and the identified flags corresponding to risk drivers; and means for automatically generating, by an insight platform of the back-end application computer server, a recommended action for the enterprise to lower the calculated risk score.”
The claims supplied by the inventors are:
“1. A data analytics system implemented via a back-end application computer server, comprising: (a) a first risk relationship data store containing electronic records that represent a plurality of risk relationships between the enterprise and a first risk relationship provider, and, for each risk relationship, an electronic record identifier and a set of entity attribute values including an entity identifier; (b) a second risk relationship data store containing electronic records that represent a plurality of risk relationships between the enterprise and a second risk relationship provider, and, for each risk relationship, an electronic record identifier and a set of entity attribute values including an entity identifier; © the back-end application computer server, coupled to the risk relationship data store, including: a computer processor, and a computer memory, coupled to the computer processor and storing instructions that, when executed by the computer processor, cause the back-end application computer server to: analyze, by a data mining engine, a set of electronic records in the first and second risk relationship data stores to identify flags corresponding to risk drivers, wherein said analyzing is performed without retrieving other electronic records, and combining the retrieved information with third-party data received from a third-party data source, to reduce a number of electronic records transmitted via a distributed communication network, calculate, by a predictive analytics engine, a risk score associated with the set of electronic records based on the associated entity attribute values and the identified flags corresponding to risk drivers, and automatically generate, by an insight platform, a recommended action for the enterprise to lower the calculated risk score; (d) a communication port coupled to the back-end application computer server to facilitate a transmission of data with remote user devices to support interactive user interface displays via the distributed communication network; (e) an email server, coupled to the back-end application computer server, to automatically establish a channel of communication with an entity linked with the entity identifier and transmit a message, including an indication that the recommended action generated by the insight platform should be implemented, via the established channel of communication; and (f) a workflow application, coupled to the back-end application computer server, to automatically create a reminder that the recommended action generated by the insight platform should be implemented.
“2. The system of claim 1, wherein at least one of the first and second risk relationship data stores is associated with one of: (i) health insurance, (ii) prescription pharmacy insurance, (iii) workers’ compensation insurance, (iv) paid family leave insurance, (v) disability insurance, (vi) short term disability insurance, (vii) long term disability insurance, (viii) paid time off, (ix) sick leave, (x) employee sentiment, (xi) human resources data, and (xii) wearable Internet of Things (“IoT”) sensors.
“3. The system of claim 1, wherein the automatically generated recommendation is associated with at least one of: (i) insurance pricing, (ii) a deductible, (iii) a limit, (iv) an insurance plan design, (v) insurance claim management, (vi) a service, (vii) a prevention strategy, and (viii) a recovery strategy.
“4. The system of claim 1, wherein the predictive analytics engine implements a predictive model to calculate the likelihood of certain events occurring on the basis of risk drivers identified for each of the plurality of electronic records; and wherein the risk score is based on the calculated likelihood of certain events occurring.
“5. The system of claim 1, wherein the insight platform accesses a database of insurance claim records, each insurance claim record including associated risk score and claim outcome; and wherein the insight platform determines an expected claim outcome for the calculated risk score by analyzing the claim outcomes of insurance claim records having risk scores that are substantially the same as the calculated risk score.
“6. The system of claim 1, wherein the insight platform automatically generates an electronic message requesting confirmation that the recommended action has been implemented.
“7. The system of claim 1, wherein the insight platform generates an insurance claim record corresponding to each of the plurality of electronic records, each insurance claim record including an associated risk score and an expected claim outcome.
“8. A computerized data analytics method implemented via a back-end application computer server, comprising: analyzing, by a computer processor executing a data mining engine of the back-end application computer server, a set of electronic records in a first and a second risk relationship data store to identify flags corresponding to risk drivers, wherein the first risk relationship data store contains electronic records that represent a plurality of risk relationships between the enterprise and a first risk relationship provider, and the second risk relationship data store contains electronic records that represent a plurality of risk relationships between the enterprise and a second risk relationship provider, wherein said analyzing is performed without retrieving other electronic records, and combining the set of electronic records with third-party data received from a third-party data source, to reduce a number of electronic records transmitted via a distributed communication network; calculating, by a predictive analytics engine of the back-end application computer server, a risk score associated with the set of electronic records based on associated entity attribute values and the identified flags corresponding to risk drivers; automatically generating, by an insight platform of the back-end application computer server, a recommended action for the enterprise to lower the calculated risk scores; automatically establishing, by an email server coupled to the back-end application computer server, a channel of communication with an entity linked with the entity identifier and transmitting a message, including an indication that the recommended action generated by the insight platform should be implemented, via the established channel of communication; and automatically creating, by a work flow application coupled to the back-end application computer server, a reminder that the recommended action generated by the insight platform should be implemented.
“9. The method of claim 8, wherein at least one of the first and second risk relationship data stores is associated with one of: (i) health insurance, (ii) prescription pharmacy insurance, (iii) workers’ compensation insurance, (iv) paid family leave insurance, (v) disability insurance, (vi) short term disability insurance, (vii) long term disability insurance, (viii) paid time off, (ix) sick leave, (x) employee sentiment, (xi) human resources data, and (xii) wearable Internet of Things (“IoT”) sensors.
“10. The method of claim 8, wherein the automatically generated recommendation is associated with at least one of: (i) insurance pricing, (ii) a deductible, (iii) a limit, (iv) an insurance plan design, (v) insurance claim management, (vi) a service, (vii) a prevention strategy, and (viii) a recovery strategy.
“11. The method of claim 8, wherein the predictive analytics engine implements a predictive model to calculate the likelihood of certain events occurring on the basis of risk drivers identified for each of the plurality of electronic records; and wherein the risk score is based on the calculated likelihood of certain events occurring.
“12. The method of claim 8, wherein the insight platform accesses a database of insurance claim records, each insurance claim record including associated risk score and claim outcome; and wherein the insight platform determines an expected claim outcome for the calculated risk score by analyzing the claim outcomes of insurance claim records having risk scores that are substantially the same as the calculated risk score.
“13. The method of claim 8, wherein the insight platform automatically generates an electronic message requesting confirmation that the recommended action has been implemented.
“14. The method of claim 8, wherein the insight platform generates an insurance claim record corresponding to each of the plurality of electronic records, each insurance claim record including an associated risk score and an expected claim outcome.”
There are additional claims. Please visit full patent to read further.
For the URL and additional information on this patent, see: Day, Jennifer. Data analytics system to automatically recommend risk mitigation strategies for an enterprise.
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