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US 2022/0027916 A1Published application
Self Learning Machine Learning Pipeline for Enabling Binary Decision Making
AssigneeSocure, Inc.
InventorsPablo Ysrrael ABREU, Omar GUTIERREZ, Lukas Dylan OSBORNE
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Abstract
The system and methodology of the present invention uses feedback data received as a result of previous binary decisions made and uses an applicable score as well as other data, in some cases, to update and optimize a rules base such that scoring is incrementally improved over time as more and more feedback data is provided to the system.
Claims21
A system configured to generate scores, the system comprising:
- a computer comprising a physical storage capability, and one or more processors configured to solely execute each of
- an active rules base contained within said physical storage capability, said active rules base defining at least a first model comprising a plurality of rules, and said model being operative to apply said plurality of rules to transaction data corresponding to a first, potential transaction and to generate at least a first score for said first, potential transaction based on the application of said plurality of rules of said at least a first model;
- a pending rules base contained within said physical storage capability;
- a rules base merger operative to receive and process feedback data subsequent to said scoring comprising said at least a first score and execution of said first, potential transaction, said processing comprising generation of training based on said processed feedback data;
- a model updater operative to (a) receive said processed feedback data from said rules base merger, (b) optimize at least one rule of said pending rules base according to said training, and in response to said processed feedback data matching one or more variables of said transaction data, and (c) create at least a second model comprising said optimized pending rules base, and which is operative to apply said at least one rule of said optimized pending rules base to said transaction data to generate a score therefor based on the application of said at least one rule of said optimized pending rules base of said at least a second model;
- a model assessor operative to solely feed said transaction data to each of said at least a first model and said at least a second model at a same time for scoring said first, potential transaction, and to directly determine whether said scoring of said first, potential transaction as generated by said at least a first model at said same time or said scoring of said first, potential transaction as generated by said at least a second model at said same time more accurately reflects said processed feedback data;
- and
- a model manager operative to substitute said at least a second model for said at least a first model in response to said scoring of said first, potential transaction as generated by said at least a second model at said same time more accurately reflecting said processed feedback data, wherein said at least a second model is then operative, upon being substituted, to generate a second score for a second, potential transaction.
5 more elements
(canceled)
A computer-implemented method of optimizing a model used in generating scores, the method being implemented in a computer system comprising one or more processors configured to execute computer program modules solely implementing steps comprising:
- receiving feedback data in response to a current active model being fed a first transaction request which had been scored, said feedback data comprising an indicator as to whether a transaction associated with said first, scored transaction request was actually fraudulent;
- generating training based upon the feedback data;
- updating at least one rule within a pending model (a) based upon said training, for said at least one rule, and (b) in response to said feedback data matching one or more variables of said first, scored transaction request;
- feeding said first, scored transaction request to said updated pending model and said current active model, at a same time, for scoring of said first, scored transaction request by each of said updated pending model and said current active model;
- assessing whether said scoring by said updated pending model at said same time or said scoring by said current active model at said same time more accurately reflects said indicator;
- and
- if said scoring by said updated pending model at said same time more accurately reflects said indicator, then substituting said updated pending model as a new current active model for scoring a second transaction request.
4 more elements
Dependencies are inferred from the claim text.