Patent Issued for Resource allocation (USPTO 11823276): Aetna Inc.
2023 DEC 08 (NewsRx) -- By a
The patent’s assignee for patent number 11823276 is
News editors obtained the following quote from the background information supplied by the inventors: “Certain institutions are required to have enough resources to cover any incurred or future costs. Sometimes, the existence of these allocated resources or funds is mandated by governing bodies, for example, country and state governments. In some examples, financial institutions are required to have a certain amount of reserve money as a percentage of deposits in order to be able to cover daily withdrawals or emergency withdrawals from their customers. In another example, insurance companies are required to hold enough reserve money to cover any incurred claims.
“The process of determining an amount of reserve money currently employed by insurance companies is conservative due to volatility in current reserving models. Sometimes information needed to accurately predict the required reserve amount is not received until months later. Current methodologies deal with this information lag by providing an incredible margin of safety that burdens institutions with requirements to hold a large amount of capital on hand in the form of reserves money. Large amount of capital on hand may adversely impact an institution’s ability to invest in the future.”
As a supplement to the background information on this patent, NewsRx correspondents also obtained the inventors’ summary information for this patent: “Embodiments of the disclosure provide a system and method of allocating a resource based on myriad input data. In some embodiments, the myriad input data include membership information, claims data, transactional data, etc. The myriad input data are sorted and organized in a meaningful association relationship before being applied to a resource allocation modeling algorithm. The resource allocation modeling algorithm provides estimated resources necessary for the application chosen. For example, an insurance company may use membership information, claims data, transactional data, etc., to estimate how much reserve money or funds it should hold to cover future claims within a certain timeframe.
“In one embodiment, a method for estimating reserves for an insurance carrier using a data platform configured to collect data from one or more source systems is provided. The method includes: collecting reserves relevant data from one or more data source systems over a system defined time period; converting the reserves relevant data into a reserves relevant data matrix, wherein the reserves relevant data matrix comprises a plurality of features based on the reserves relevant data that are organized based on the system defined time period; storing the reserves relevant data matrix at a reserves database of the data platform; executing a predictive model for each of the plurality of features of the reserves relevant data matrix to extrapolate a trend for each individual feature; and combining the trend for each individual feature to obtain a reserves estimate.
“In another embodiment, a method for geographically allocating reserves for an insurance carrier using a data platform configured to collect data from one or more source systems is provided. The method includes: collecting reserves relevant data from one or more data source systems from a plurality of geographic regions over a system defined time period; converting the reserves relevant data into a reserves relevant data matrix, wherein the reserves relevant data matrix comprises a plurality of features based on the reserves relevant data that are organized based on the plurality of geographic regions and the system defined time period; storing the reserves relevant data matrix at a reserves database of the data platform; executing a predictive model for each of the plurality of features of the reserves relevant data matrix to extrapolate a trend for each individual feature within a geographic region of the plurality of geographic regions; and combining the trend for each individual feature within the geographic region to obtain a reserves estimate for the geographic region.
“In a further embodiment, a user interface for interacting with reserves relevant data collected from reserves relevant data sources and being utilized for estimating reserves for an insurance carrier is provided. The user interface includes a predictive variable interface configured to display the reserves relevant data collected from the reserves relevant data sources, wherein the predictive variable interface displays the reserves relevant data over a selected time period. The user interface further includes a predictive model interface configured to display, over the defined time period, a predictive model performance and a predictive model variance for reserves estimates made based on the reserves relevant data over the selected time period.
“In yet another embodiment, a non-transitory computer readable medium containing computer executable instructions for estimating reserves for an insurance carrier using a data platform configured to collect data from one or more source systems is provided. The computer readable instructions, when executed by a processor, cause the processor to perform steps including: collecting reserves relevant data from one or more data source systems over a system defined time period; converting the reserves relevant data into a reserves relevant data matrix, wherein the reserves relevant data matrix comprises a plurality of features based on the reserves relevant data that are organized based on the system defined time period; storing the reserves relevant data matrix at a reserves database of the data platform; executing a predictive model for each of the plurality of features of the reserves relevant data matrix to extrapolate a trend for each individual feature; and combining the trend for each individual feature to obtain a reserves estimate.”
The claims supplied by the inventors are:
“1. A user interface for interacting with reserves relevant data collected from reserves relevant data sources and being utilized for making reserves estimates for an insurance carrier by applying a predictive model to the reserves relevant data, the user interface comprising: a predictive variable interface configured to: convert, by an extract, transform, and load (ETL) system, the reserves relevant data collected from the reserves relevant data sources over a specified time period into a first reserves relevant data matrix and a second reserves relevant data matrix, wherein the first reserves relevant data matrix includes individual member eligibility data combined with member claims-related data and the second reserves relevant data matrix includes data relevant to member interactions with the insurance carrier, and wherein the first and second reserves relevant data matrices comprise a plurality of features based on the reserves relevant data; combine the first reserves relevant data matrix with the second reserves relevant data matrix to form a third reserves relevant data matrix, wherein the third reserves relevant data matrix comprise the plurality of features associated with individual members and based on a system defined time period and geographic locations described by associated geographic information; and display a graph for each respective feature of the plurality of features from the reserves relevant data matrix, wherein each graph comprises a first curve and a second curve, the first curve plots data collected for the respective feature from the reserves relevant data sources, and the second curve plots a predicted trend for the respective feature; and a predictive model interface configured to display, over the specified time period, a predictive model performance and a predictive model variance for the reserves estimates made based on an application of the predictive model to the reserves relevant data over the specified time period, wherein the predictive model applies a modeling algorithm to the plurality of features of the third reserves relevant data matrix.
“2. The user interface of claim 1, wherein the reserves relevant data comprises a plurality of data types and the user interface further comprises a leading indicators interface configured to display each of the plurality of data types against reserves data over the specified time period.
“3. The user interface of claim 2, wherein the plurality of data types comprises claim data over the specified time period.
“4. The user interface of claim 2, wherein the plurality of data types comprises member data over the specified time period, wherein the member data comprises date information for each of a plurality of member data points of the member data.
“5. The user interface of claim 4, wherein a format of the member data is converted by the user interface to member-by-date data, wherein the member-by-date data contains the plurality of member data points associated with the date information.
“6. The user interface of claim 2, wherein the plurality of data types comprises eligibility data over the specified time period.
“7. The user interface of claim 1, wherein the predictive model interface is further configured to display the reserves estimates made based on the reserves relevant data over the specified time period.
“8. The user interface of claim 2, wherein the plurality of data types comprises pre-certification data over the specified time period.
“9. The user interface of claim 2, wherein the plurality of data types comprises prescription data over the specified time period.
“10. The user interface of claim 2, wherein the plurality of data types comprises one or more of weather data, insurance carrier navigator data, and insurance carrier call log data.
“11. The user interface of claim 1, wherein the predictive model is one or more of: a linear regression, a non-linear regression, a support vector machine, a neural network, a decision tree, a random forest, or a time series analysis.
“12. A non-transitory computer readable medium storing instructions for configuring a computer as a user interface comprising a predictive variable interface and a predictive model interface, wherein the user interface interacts with reserves relevant data collected from reserves relevant data sources, and the reserves relevant data is utilized for making reserves estimates for an insurance carrier by applying a predictive model to the reserves relevant data, wherein, when the computer executes the instructions, the computer is configured to: convert, by an extract, transform, and load (ETL) system, the reserves relevant data collected from the reserves relevant data sources over a specified time period into a first reserves relevant data matrix and a second reserves relevant data matrix, wherein the first reserves relevant data matrix includes individual member eligibility data combined with member claims-related data and the second reserves relevant data matrix includes data relevant to member interactions with the insurance carrier, and wherein the first and second reserves relevant data matrices comprise a plurality of features based on the reserves relevant data; combine the first reserves relevant data matrix with the second reserves relevant data matrix to form a third reserves relevant data matrix, wherein the third reserves relevant data matrix comprise the plurality of features associated with individual members and based on a system defined time period and geographic locations described by associated geographic information; and display a graph for each respective feature of the plurality of features from the reserves relevant data matrix, wherein each graph comprises a first curve and a second curve, the first curve plots data collected for the respective feature from the reserves relevant data sources, and the second curve plots a predicted trend for the respective feature; and display, over the specified time period, a predictive model performance and a predictive model variance for the reserves estimates made based on an application of the predictive model to the reserves relevant data over the specified time period, wherein the predictive model applies a modeling algorithm to the plurality of features of the third reserves relevant data matrix.
“13. The non-transitory computer readable medium of claim 12, wherein the reserves relevant data comprises a plurality of data types, and wherein the non-transitory computer readable medium stores further instructions that, when executed by the computer, further configure the computer to display each of the plurality of data types against reserves data over the specified time period.
“14. The non-transitory computer readable medium of claim 13, wherein the plurality of data types comprises claim data over the specified time period.
“15. The non-transitory computer readable medium of claim 13, wherein the plurality of data types comprises member data over the specified time period, wherein the member data comprises date information for each of a plurality of member data points of the member data.
“16. The non-transitory computer readable medium of claim 15, wherein a format of the member data is converted by the user interface to member-by-date data, wherein the member-by-date data contains the plurality of member data points associated with the date information.
“17. The non-transitory computer readable medium of claim 13, wherein the plurality of data types comprises eligibility data over the specified time period.
“18. The non-transitory computer readable medium of claim 12, wherein the predictive model interface is further configured to display the reserves estimates made based on the reserves relevant data over the specified time period.
“19. The non-transitory computer readable medium of claim 13, wherein the plurality of data types comprises pre-certification data over the specified time period.”
There are additional claims. Please visit full patent to read further.
For additional information on this patent, see: Athalye, Radhika G. Resource allocation.
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