Supplemental review process determination utilizing advanced analytics decision making model

A method to automatically identify electronic records to be routed to a supplemental insurance review process, the method performed via at least one processor and a non-tangible, computer-readable medium storing programmed instructions in an application computer server, the method comprising:

• extracting indeterminate historical data from raw files;
• storing, in a data store, the indeterminate historical data, determinate historical data originating from determinate data sources, and a batch of electronic records representing a potential new risk association with an entity, wherein each record in the batch of electronic records contains a record identifier and a set of record characteristic values including at least:
• one record characteristic value representing a characteristic of the entity,
• one record characteristic value representing an original source of the each record, and
• one record characteristic value representing third-party data;
• training a plurality of independent analytics decision making models using the stored indeterminate and determinate historical data, wherein the plurality of trained independent analytics decision making models apply statistical techniques including at least machine learning, and wherein at least one of the plurality of independent analytics decision making models is associated with a probability, p, of an electronic record representing a potential new risk undergoing a supplemental review process defined as:
• p
• =
• ln
• ⁢
• ⁢
• (
• p
• 1
• -
• p
• )
• =
• β
• 0
• +
• β
• 1
• ⁢
• x
• 1
• +
• …
• +
• β
• p
• ⁢
• x
• p
• ,
• wherein βi are weighing values and xi are associated with the record characteristic values;
• applying the plurality of independent analytics decision making models to the each record and its corresponding set of record characteristic values;
• combining, by a data fusion component, output from the plurality of independent analytics decision making models based on a priority assigned to each model;
• determining, based upon the combined output of the data fusion component, a score value associated with the each record;
• generating a ranked list of the score values associated with the each record beginning with those representing a potential new risk association most likely to benefit from the supplemental review process and ending with those least likely to benefit from the supplemental review process;
• transmitting the ranked list to an interactive user interface display via a communication port coupled to the back-end application computer server and the data store;
• automatically routing the each record in the ranked list having a score value above a pre-determined threshold to a remote administrator computer to undergo the supplemental review process;
• monitoring performance outcomes including at least one of risk profitability, risk quality and policy growth for the potential new risk association represented by the batch of electronic records to which the plurality of independent analytics decision making models were applied;
• reanalyzing the performance outcomes, the historical data, and the ranked list of score values associated with the each record identified for supplemental review to measure the accuracy of each of the trained independent analytics decision making models;
• and
• fine tuning the independent analytics decision making models and the data fusion component to improve the accuracy of identifying records for supplemental review.

A non-tangible, computer-readable medium of an automated back-end application computer server, the medium storing instructions that, when executed by the server, cause the processor to perform a method to automatically identify electronic records to be routed to a supplemental insurance review process, the method comprising:

• extracting indeterminate historical data from raw files;
• storing, in a data store, the indeterminate historical data, determinate historical data originating from determinate data sources, and a batch of electronic records representing a potential new risk association with an entity, wherein each record in the batch of electronic records contains a record identifier and a set of record characteristic values including at least:
• one record characteristic value representing a characteristic of the entity,
• one record characteristic value representing an original source of the each record, and
• one record characteristic value representing third-party data;
• training a plurality of independent analytics decision making models using the stored indeterminate and determinate historical data, wherein the plurality of trained independent analytics decision making models apply statistical techniques including at least machine learning, and wherein at least one of the plurality of independent analytics decision making models is associated with a probability, p, of an electronic record representing a potential new risk undergoing a supplemental review process defined as:
• p
• =
• ln
• ⁢
• ⁢
• (
• p
• 1
• -
• p
• )
• =
• β
• 0
• +
• β
• 1
• ⁢
• x
• 1
• +
• …
• +
• β
• p
• ⁢
• x
• p
• ,
• wherein βi are weighing values and xi are associated with the record characteristic values;
• applying the plurality of independent analytics decision making models to the each record and its corresponding set of record characteristic values;
• combining, by a data fusion component, output from the plurality of independent analytics decision making models based on a priority assigned to each model;
• determining, based upon the combined output of the data fusion component, a score value associated with the each record;
• generating a ranked list of the score values associated with the each record beginning with those representing a potential new risk association most likely to benefit from the supplemental review process and ending with those least likely to benefit from the supplemental review process;
• transmitting the ranked list to an interactive user interface display via a communication port coupled to the back-end application computer server and the data store;
• automatically routing the each record in the ranked list having a score value above a pre-determined threshold to a remote administrator computer to undergo the supplemental review process;
• monitoring performance outcomes including at least one of risk profitability, risk quality and policy growth for the potential new risk association represented by the batch of electronic records to which the plurality of independent analytics decision making models were applied;
• reanalyzing the performance outcomes, the historical data, and the ranked list of score values associated with the each record identified for supplemental review to measure the accuracy of each of the trained independent analytics decision making models;
• and
• fine tuning the independent analytics decision making models and the data fusion component to improve the accuracy of identifying records for supplemental review.