US 2019/0295087 A1Published application

System and method for detecting fraud in online transactions by tracking online account usage characteristics indicative of user behavior over time

AssigneeMicrosoft Technology Licensing, LLC

InventorsYuting Jia, Shoou-Jiun Wang, Jayaram NM Nanduri

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Abstract

Methods, systems, and computer program products are provided for tracking user actions made via a user account, and to accurately detect fraudulent transactions made therewith. Information associated with the user actions such as, for example, device ID, device IP address, and device IP location, is captured and stored. Stored information is used to create features. The features are assembled into an n-dimensional vector, and a measure similarity between that vector and a previously created n-dimensional vector can be computed. The measure of similarity may be used to assess the probability that the present transaction is fraudulent. Alternatively, one or more n-dimensional vectors, and/or the computed measure of similarity may be used as input to a machine learning model. The output of machine learning model also may be used to assess the probability that the present transaction is fraudulent.

Claims20

Dependent claims

A fraud detection system, comprising:

one or more processors;
and
one or more memory devices accessible to the one or more processors, the one or more memory devices storing software components for execution by the one or more processors, the software components including:
a data collection component configured to collect and store at least one usage attribute associated with one or more user actions conducted via a user account;
a user behavior vector generation component configured to generate at least one feature based at least in part on the at least one usage attribute, the at least one feature reflecting user behavior over a first period of time, and to compute a first user behavior vector using the at least one feature;
the data collection component being further configured to collect and store at least one additional usage attribute associated with one or more additional user actions conducted via the user account;
the user behavior vector generation component being further configured to generate at least one additional feature based at least in part on the at least one additional usage attribute, the at least one additional feature reflecting user behavior over a second period of time, and to compute a second user behavior vector using the at least one additional feature;
and
a fraud detection component configured to compare the first and second user behavior vectors to generate a measure of similarity there between, and to determine if a transaction associated with the user account is fraudulent based at least on the measure of similarity.

6 more elements

A computer-implemented method for detecting fraud in an online commerce system, comprising:

collecting at least one usage characteristic associated with one or more user actions conducted on the online commerce system via a user account;
determining at least one first feature based on each of the collected at least one usage characteristic, the at least one first feature reflecting a statistic associated with the at least one usage characteristic over a first period of time;
computing a first usage vector using the at least one first feature;
collecting at least one additional usage characteristic associated with one or more additional user actions conducted via the user account;
determining at least one second feature based on each of the collected at least one additional usage characteristic, the at least one second feature reflecting a statistic associated with the at least one additional usage characteristic over a second period of time;
computing a second usage vector using the at least one second feature;
comparing the first and second usage vectors to determine a measure of similarity there between;
and
determining whether a transaction associated with the user account is fraudulent based at least on the measure of similarity.

6 more elements

Dependencies are inferred from the claim text.