Database optimization concepts in fast response environments
First Claim
1. A system, comprising:
- a processor; and
a non-transitory computer-readable storage medium having instructions stored thereon that are executable by the processor to cause the system to perform operations comprising;
receiving string data corresponding to a plurality of account characteristics for a new account for an electronic transaction service;
determining a match exists between a particular piece of the string data for the new account and respective particular pieces of string data for a plurality of established accounts for the electronic transaction service;
based on the match, analyzing the plurality of account characteristics for the new account relative to account characteristics for the plurality of established accounts;
without using transaction history data for the new account, and based on the analyzing, assigning the new account to a particular account cluster based on similarities in the plurality of account characteristics to account characteristics of the established accounts; and
using a machine learning model trained by account characteristics of historical transactions of the established accounts and fraud indications of the historical transactions, predicting a first fraud probability of a first new transaction attempted by the new account and a second fraud probability of a second new transaction attempted by the new account based on the assigned particular account cluster.
1 Assignment
0 Petitions
Accused Products
Abstract
Rapidly handling large data sets can be a challenge, particularly in situations where there are millions or even hundreds of millions of database records. Sometimes, however, a service level agreement necessitates that a service return a response to a query in a small amount of time. Database organization techniques can be used that reduce potentially large datasets to smaller groups (neighbors) based on uncommon but shared attributes, in various instances. Using a limited set of related records, queries can be answered using a focused approximation based on characteristics of various identified clusters of records in the set of related records. A particular record may also be associated with an existing cluster of records based on that record'"'"'s similarities to records in the cluster.
15 Citations
20 Claims
-
1. A system, comprising:
-
a processor; and a non-transitory computer-readable storage medium having instructions stored thereon that are executable by the processor to cause the system to perform operations comprising; receiving string data corresponding to a plurality of account characteristics for a new account for an electronic transaction service; determining a match exists between a particular piece of the string data for the new account and respective particular pieces of string data for a plurality of established accounts for the electronic transaction service; based on the match, analyzing the plurality of account characteristics for the new account relative to account characteristics for the plurality of established accounts; without using transaction history data for the new account, and based on the analyzing, assigning the new account to a particular account cluster based on similarities in the plurality of account characteristics to account characteristics of the established accounts; and using a machine learning model trained by account characteristics of historical transactions of the established accounts and fraud indications of the historical transactions, predicting a first fraud probability of a first new transaction attempted by the new account and a second fraud probability of a second new transaction attempted by the new account based on the assigned particular account cluster. - View Dependent Claims (2, 3, 4, 5, 6, 7)
-
-
8. A method, comprising:
-
receiving, at an analysis computer system, new account information for a new account corresponding to an electronic transaction service; analyzing, by the analysis computer system, a plurality of account characteristics included in the new account information; prior to receiving any transaction details regarding any electronic payment transactions made with the new account, assigning the new account to a particular account cluster based on similarities in the plurality of account characteristics to corresponding account characteristics of other accounts in the particular account cluster; and using a machine learning model trained by account characteristics of historical transactions of the established accounts and fraud indications of the historical transactions, predicting a first fraud probability to a first new transaction attempted by the new account and a second fraud probability to a second new transaction attempted by the new account based on the assigned particular account cluster. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15)
-
-
16. A non-transitory computer-readable medium having instructions stored thereon that are executable by a computer system to cause the computer system to perform operations comprising:
-
receiving an indication that a new account corresponding to an electronic transaction service has initiated a transaction; analyzing a plurality of account characteristics included in new account information for the new account; prior to receiving any transaction details regarding any electronic payment transactions made with the new account, assigning the new account to a particular account cluster based on similarities in the plurality of account characteristics to corresponding account characteristics of other accounts in the particular account cluster; using a machine learning model trained by account characteristics of historical transactions of the established accounts and fraud indications of the historical transactions, assigning a first fraud probability to a first new transaction attempted by the new account and a second fraud probability to a second new transaction based on the assigned particular account cluster; and determining whether to approve or deny the first or the second new transaction based on the first or the second predicted fraud probability. - View Dependent Claims (17, 18, 19, 20)
-
Specification