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US 2025/0156872 A1Published application
Automatic fraud detection using machine learning
AssigneeCapital One Services, LLC
InventorVishal Bahirwani
Life of this patent
Estimating the term…
Abstract
Aspects described herein may automatically detect first-person fraud. A computing device may receive activity instances associated with a first user at different times, and aggregate, via an application programming interface (API), the instances by normalizing attributes associated with the activity instances that indicate fraud. The computing device may input the normalized attributes into a machine model to predict a likelihood of a future fraud instance. The computing device may send, based on the likelihood exceeding a threshold, an alert. In this way, fraud instances may be detected promptly.
Claims20
A method comprising:
- receiving, by a first computing device from a second computing device, a first activity instance, associated with a first user, occurred at a first time;
- receiving, by the first computing device from a third computing device, a second activity instance, associated with the first user, occurred at a second time;
- aggregating, via an application programming interface (API), the first activity instance and the second activity instance by normalizing:
- one or more first attributes associated with the first activity instance;
- and
- one or more second attributes associated with the second activity instance;
- determining, by inputting the one or more first normalized attributes and the one or more second normalized attributes into a machine model, a first likelihood of a future fraud instance associated with the first user, wherein the machine learning model is trained to output, based on an input of normalized attributes associated with each of a plurality of fraud instances, a likelihood of a future fraud instance of a user associated with the plurality of input fraud instances;
- and
- sending, based on the first likelihood exceeding a threshold, an alert.
6 more elements
A system comprising:
- a first computing device;
- and
- a second computing device;
- wherein the first computing device is configured to:
- receive, from a second computing device, a first activity instance, associated with a first user, occurred at a first time;
- receive, from a third computing device, a second activity instance, associated with the first user, occurred at a second time;
- aggregate, via an application programming interface (API), the first activity instance and the second activity instance by normalizing:
- one or more first attributes associated with the first activity instance;
- and
- one or more second attributes associated with the second activity instance;
- determine, by inputting the one or more first normalized attributes and the one or more second normalized attributes into a machine model, a first likelihood of a future fraud instance associated with the first user, wherein the machine learning model is trained to output, based on an input of normalized attributes associated with each of a plurality of fraud instances, a likelihood of a future fraud instance of a user associated with the plurality of input fraud instances;
- and
- send, based on the first likelihood exceeding a threshold, an alert;
- and
- wherein the second computing device is configured to:
- send, to the first computing device, the first activity instance.
13 more elements
A non-transitory computer-readable medium storing computer instructions that, when executed by one or more processors, cause a first computing device perform of actions comprising:
- receiving, from a second computing device, a first activity instance, associated with a first user, occurred at a first time;
- receiving, from a third computing device, a second activity instance, associated with the first user, occurred at a second time;
- aggregating, via an application programming interface (API), the first activity instance and the second activity instance by normalizing:
- one or more first attributes associated with the first activity instance;
- and
- one or more second attributes associated with the second activity instance;
- determining, by inputting the one or more first normalized attributes and the one or more second normalized attributes into a machine model, a first likelihood of a future fraud instance associated with the first user, wherein the machine learning model is trained to output, based on an input of normalized attributes associated with each of a plurality of fraud instances, a likelihood of a future fraud instance of a user associated with the plurality of input fraud instances;
- and
- sending, based on the first likelihood exceeding a threshold, a request to take a remedial action that comprises at least one of:
- denial of a future transaction request;
- or
- suspending an account associated with the first user.
9 more elements
Dependencies are inferred from the claim text.