Variable Matrices for Machine Learning

Patent metadata

Field	Value
Publication number	US-2025021814-A1
Application number	US-202318455134-A
Country	US
Kind code	A1
Filing date	Aug 24, 2023
Priority date	Jul 13, 2023
Publication date	Jan 16, 2025
Grant date	—

Abstract

Techniques are disclosed for generating multi-dimensional variable matrices for predicting abnormality of electronic communications. After receiving the request for a new communication from a given entity, a system retrieves attributes of the entity. The system generates, a variable matrix for the entity with a variable dimension corresponding to a number of variables determined for the entity based on the entity's attributes and an entity dimension corresponding to a number of other entities with which the entity performed communications. The system inputs the variable matrix into a trained machine learning model and determines, based on an abnormality score output by the model, whether the communication requested by the entity corresponds to anomalous behavior. The disclosed techniques may advantageously provide a greater distribution of data for an entity using a multi-dimensional variable matrix for anomaly prediction e.g., using machine learning relative to traditional techniques that compress the distribution to a one-dimensional statistic.

First claim

What is claimed is:

1 . A method, comprising:
receiving, by a server system from a given entity, a request to initiate a new electronic communication; retrieving, by the server system from an attribute database, attributes for the given entity; generating, by the server system, a variable matrix for the given entity, wherein the variable matrix includes a variable dimension that corresponds to a number of variables determined for the given entity based on the attributes for the given entity and an entity dimension that corresponds to a number of other entities with which the given entity has performed electronic communications; generating, by the server system using a trained machine learning model, an abnormality score for the given entity, wherein the abnormality score is generated by the trained machine learning model based on the variable matrix; and determining, by the server system based on the abnormality score, whether the new electronic communication requested by the given entity corresponds to anomalous behavior.

2 . The method of claim 1 , wherein generating the variable matrix for the given entity is performed by:
calculating, based on the attributes for the given entity, a plurality of variables; generating a graph of the plurality of variables, wherein a first dimension of the graph indicates the other entities and a second dimension of the graph indicates values of the plurality of variables, and wherein lines connecting points on the graph correspond to different ones of the plurality of variables; and generating, based on the graph of the plurality of variables, the variable matrix for the given entity, wherein the lines connecting points on the graph represent to different ones of the plurality of variables, and wherein columns of the variable matrix correspond to the lines of the graph.

3 . The method of claim 1 , wherein generating the variable matrix includes:
transforming, prior to inputting the variable matrix into the trained machine learning model, the variable matrix, such that columns of the variable matrix correspond to the variable dimension and rows of the variable matrix correspond to the entity dimension; and normalizing, by the server system, variable values included in the transformed variable matrix such that the trained machine learning model does not include a bias for entities having the largest variable values.

4 . The method of claim 1 , further comprising:
performing, by the server system based on determining that the new electronic communication requested by the given entity corresponds to anomalous behavior, one or more preventative actions with respect to the given entity requesting to initiate the new electronic communication.

5 . The method of claim 1 , further comprising:
sorting, by the server system, variables included in the variable matrix in descending order from largest variable value to smallest variable value.

6 . The method of claim 1 , wherein the trained machine learning model is an image classification model, the method further comprising:
generating, by the server system based on the variable matrix, an image of lines, wherein the lines correspond to columns of the variable matrix which correspond to the variables determined for the given entity, and wherein generating the abnormality score for the given entity based on the variable matrix includes inputting the image of lines into the image classification model.

7 . The method of claim 1 , wherein the electronic communications are electronic transactions between the given entity that is a user having a user account with the server system and other entities that are counterparties with which the user account is performing electronic transactions, and wherein the abnormality score output by the trained machine learning model for the new electronic communication corresponds to a jump in one or more variables for the user account over a given time interval.

8 . The method of claim 7 , wherein a plurality of variables included in the variable matrix include variables for the user and the counterparties calculated for different time intervals, and wherein the plurality of variables include one or more variable types of the following types of variables: transaction amount, transaction count, transaction type, internet protocol (IP) address of devices involved in transactions, hardware attributes of computing devices involved in electronic transactions, account login frequency, transaction date, and transaction timestamp.

9 . The method of claim 1 , further comprising:
training, by the server system, a first machine learning model using a first set of variable matrices and a second machine learning model using a second, different set of variable matrices, wherein the first set of variable matrices includes variables for a sender and a receiver in respective electronic communications, and wherein the second, different set of variable matrices includes variables for a sender and not a receiver in respective electronic communications; and generating, by the server system, a combined machine learning model by ensembling the first and second machine learning models, wherein generating the abnormality score and the determining are performed for other new electronic communications using the combined machine learning model.

10 . A non-transitory computer-readable medium having instructions stored thereon that are executable by a server system to perform operations comprising:
receiving, from a given entity, a request to initiate an electronic communication; retrieving, from a variable database, variables for the given entity and variables for a plurality of other entities with which the given entity has performed electronic communications; generating a variable matrix for the given entity, wherein the variable matrix includes a variable dimension that corresponds to a number of variables determined for the given entity based on the variables for the given entity and an entity dimension that corresponds to a number of the plurality of other entities; generating, based on the variable matrix, an image of lines, wherein the lines correspond to columns of the variable matrix which correspond to the variables determined for the given entity; inputting the image of lines into a trained image classification model; and determining, based on an abnormality score output by the trained image classification model for the image of lines, whether there are abnormal patterns in behavior of one or more of the plurality of other entities.

11 . The non-transitory computer-readable medium of claim 10 , wherein the operations further comprise:
sorting, by the server system, variables included in the variable matrix in descending order from largest variable value to smallest variable value; and transforming, by the server system prior to inputting the variable matrix into the trained image classification model, the variable matrix, such that columns of the variable matrix correspond to the variable dimension and rows of the variable matrix correspond to the entity dimension.

12 . The non-transitory computer-readable medium of claim 10 , wherein the operations further comprise:
normalizing, by the server system, variables included in the variable matrix such that the trained image classification model assigns equal weight to different entities corresponding to variables having different values.

13 . The non-transitory computer-readable medium of claim 10 , wherein the operations further comprise:
performing, based on determining that the electronic communication requested by the given entity corresponds to anomalous behavior, one or

Assignees

• Paypal Inc

Inventors

• Lin Ying
• Song Shiwen
• Yu Yingying

Classifications

• G06N20/00

  Machine learning · CPC title

• G06F17/16

  Matrix or vector computation {, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization (matrix transposition G06F7/78)} · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N3/08 Primary

  Learning methods · CPC title