Intelligent clustering of account communities for account feature adjustment

Patent metadata

Field	Value
Publication number	US-2022051125-A1
Application number	US-202016990823-A
Country	US
Kind code	A1
Filing date	Aug 11, 2020
Priority date	Aug 11, 2020
Publication date	Feb 17, 2022
Grant date	—

Abstract

There are provided systems and methods for intelligent clustering of account communities for account feature adjustment. A user may utilize an online account with the service provider to process transactions electronic. However, initially the account may be unverified or otherwise untrusted, and limits on electronic transaction processing may be imposed on the account. To provide intelligent feature adjustment, the service provider may utilize a machine learning technique to cluster verified accounts into communities based on their data representations. Thereafter, when a transaction by an unverified account violates a limit imposed on the unverified account, the service provider may process the unverified account using a machine learning model trained for account correlations to the community clusters in order to determine a corresponding community cluster of accounts and adjust a feature based on behaviors and traits of the verified accounts in the community cluster.

First claim

What is claimed is:

1 . A system comprising:
a non-transitory memory; and one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:
receiving a request for a transaction associated with an account for an online transaction processor associated with the system, wherein the request comprises transaction data for the transaction;
determining that the transaction would cause a transaction limit associated with the account to be exceeded;
identifying feature data for the transaction based on at least one of the transaction data or account data for the account;
determining, using the identified feature data and a machine learning clustering technique, a first community cluster of a plurality of community clusters that corresponds to the identifier feature data, wherein the plurality of community clusters correspond to a plurality of verified accounts of the online transaction processor;
determining an increased limit for the transaction limit of the account based on the first community cluster; and
processing the transaction based on the transaction data and the increased limit.

2 . The system of claim 1 , wherein the processing the transaction comprises determining that the transaction complies with the increased limit, and wherein the operations further comprise:
updating the account with the increased limit; and processing the transaction based on the transaction data and the increased limit.

3 . The system of claim 1 , wherein the processing the transaction comprises determining that the transaction meets or exceeds the increased limit, and wherein the operations further comprise:
declining the transaction based on the transaction data and the increased limit; and transmitting a notification to the account that comprises verification operations for at least one of verifying the account or approving the transaction.

4 . The system of claim 1 , wherein the increased limit increases at least one of a maximum transaction amount, a maximum number of transaction within a time period, or a maximum account balance.

5 . The system of claim 1 , wherein prior to the receiving the request, the operations further comprise:
accessing verified account data for the plurality of verified accounts; performing a feature extraction on the verified account data; and determining the plurality of community clusters using the machine learning clustering technique and the performing the feature extraction.

6 . The system of claim 5 , wherein the determining the plurality of community clusters uses an unsupervised training operation with the verified account data.

7 . The system of claim 6 , wherein the operations further comprise:
retraining the plurality of community clusters using unverified account data for a plurality of unverified accounts of the online transaction processor.

8 . The system of claim 5 , wherein the operations further comprise:
receiving updated verified account data for the plurality of verified accounts after a period of time; processing the updated verified account data using the machine learning clustering technique; detecting a community shift by at least one of the plurality of community clusters from the processing the updated verified account data; and retraining at least the one of the plurality of community clusters based on the community shift.

9 . The system of claim 5 , wherein the plurality of verified accounts correspond to at least one of profile data for the plurality of verified accounts, device data for devices associated with the plurality of verified accounts, or past transaction data for past transactions processed by the plurality of verified accounts.

10 . The system of claim 1 , wherein the machine learning clustering technique comprises one of a k-means clustering, a mean-shift clustering, a density-based clustering, or another machine learning clustering operation.

11 . The system of claim 1 , wherein the determining the first community cluster comprises:
determining, for the account using the machine learning clustering technique, a subset of the plurality of community clusters associated with the account based on the plurality of community clusters and the feature data; and determining the first community cluster based on one of a shortest vector distance or a rule for community cluster assignment for the plurality of community clusters.

12 . The system of claim 1 , wherein the account comprises an unverified account and the account data comprises at least one of social network data, one or more past transactions by the unverified account, one or more geo-locations associated with the unverified account, or device data for one or more devices used by the unverified account, and wherein the transaction comprises one of a user purchase transaction or a merchant withdrawal transaction.

13 . A method comprising:
obtaining account data for a plurality of accounts of an online transaction processor; determining feature data from the account data using a feature extraction operation with the account data, wherein the feature extraction operation is associated with a machine learning operation that clusters a plurality of account communities; clustering the plurality of accounts in a vector space using the feature data and the machine learning operation; and generating the plurality of account communities for the plurality of accounts based on the clustering.

14 . The method of claim 13 , wherein the plurality of accounts comprise at least one of verified accounts of the online transaction processor or unverified accounts of the online transaction processor.

15 . The method of claim 13 , further comprising:
determining an unverified account of the online transaction processor, wherein the unverified account is associated with unverified account data; associating the unverified account with one of the plurality of account communities based on the unverified account data and the machine learning operation; and determining a change to an account feature for the unverified account based on the one of the plurality of account communities associated with the unverified account.

16 . The method of claim 15 , further comprising:
determining an update to the unverified account data at a lifecycle stage of the unverified account; determining whether the one of the plurality of account communities associated with the unverified account is changed based on the update; and determining whether the change to the account feature is updated based on the determining whether the one of the plurality of account communities is changed.

17 . The method of claim 15 , wherein the account feature is associated with one of an increased transaction amount, an increased withdrawal limit, a marketing program, a risk compliance standard, a credit limit, or an account sanction.

18 . The method of claim 15 , wherein the unverified account data further comprises a number of account verification attempts for an account verification process.

19 . The method of claim 13 , wherein the account data comprises one of a user name, an account address, a payment card registration, an email address, an IP address, a network provider, an operating system, a device location, a device type, a sender account number and/or name, a recipient account number and/or name, a transaction amount, a product description, a transaction type, or a funding source.

Assignees

• Paypal Inc

Inventors

• Ho Chun Kiat
• Tso Jerry
• Jaswal Junaid Zaheer
• Song Lin
• Soenaryo Edwin Boaz
• Wong Shek Hei
• Singalreddy Harsha
• Tang Quan Jin Ferdinand
• Xie Kai

Classifications

• G06F18/22

  Matching criteria, e.g. proximity measures · CPC title

• G06F18/211 Primary

  Selection of the most significant subset of features · CPC title

• G06Q20/4014 Primary

  Identity check for transactions · CPC title

• G06F18/23213

  with fixed number of clusters, e.g. K-means clustering · CPC title

• G06F18/214

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title