Abstract:
A method and system for merging heterogeneous data types are provided. The method includes receiving a first corpus of first data, the first data includes an indicator of an interaction between a first element of the first corpus of first data and a second element of the first corpus of first data, receiving a second corpus of second data, the second data includes an indication of an interaction between a third element of the second corpus of second data and a fourth element of the second corpus of data, and generating a third matrix using correlations of the first and second elements with correlations of the third and fourth elements.
Abstract:
An authentication platform for authenticating an online user is provided. The authentication platform includes a memory device including an authentication profile and at least one processor coupled to the memory device. The at least one processor is programmed to receive an authentication request message for a transaction. The authentication request message includes authentication data. The at least one processor is also programmed to extract the authentication data from the authentication request message and determine if the ACS is available to process the transaction. If the ACS is unavailable, the at least one processor is further programmed to generate, based at least in part on the extracted authentication data, risk-based authentication (RBA) result data including a risk score and transmit an authentication response message based on the RBA result data.
Abstract:
A method and system for detecting fraudulent network events in a payment card network are provided. A plurality of scored payment card transaction authorization requests is received, originating from a plurality of merchants, and data structures for each of a plurality of merchant groups are generated. Each data structure sorts the scored authorization requests into fraud score range stripes. The data structures are parsed over a plurality of time periods, and at least one cumulative metric is calculated for each merchant group for each of the time periods in each fraud score range stripe. A plurality of ratio striping values is determined for each merchant group, and a set of feature inputs is generated using the ratio striping values. A second fraud detection model is applied to the scored authorization requests. Parameters of the second fraud detection model are configured to change based on the generated set of feature inputs.
Abstract:
An authentication platform for authenticating an online user is provided. The authentication platform includes a memory device including an authentication profile and at least one processor coupled to the memory device. The at least one processor is programmed to collect a plurality of authentication data from a plurality of transactions initiated by users attempting to perform a transaction over a computer network, analyze the plurality of authentication data to detect a plurality of insights representing relationships between data elements of different authentication requests of the plurality of authentication requests, wherein the insights represent a likelihood of fraud or potential approval with the corresponding authentication requests, determine a plurality of rules from the plurality of insights, filter the plurality of rules based on one or more thresholds, and provide the filtered plurality of rules to analyze a plurality of subsequent real-time transactions for detecting legitimate authentication requests.
Abstract:
A system for identifying complete account identifiers from partial account identifiers is provided. The system includes an account identification computing device including at least one processor and a memory device in communication with the at least one processor. The processor is configured to receive transaction data including unique merchant identifiers, build a merchant table using the transaction data, and receive a list including partial account identifiers. The processor is further configured to determine, for each unique merchant identifier, a number of candidate account identifiers and calculate at least one metric based on the number of candidate account identifiers. The processor is further configured to identify a source unique merchant identifier and match at least one candidate account identifier to a complete account identifier by matching one of the partial account identifiers to the at least one candidate account identifier.
Abstract:
A method and system for merging heterogeneous data types are provided. The method includes receiving a first corpus of first data, the first data includes an indicator of an interaction between a first element of the first corpus of first data and a second element of the first corpus of first data, receiving a second corpus of second data, the second data includes an indication of an interaction between a third element of the second corpus of second data and a fourth element of the second corpus of data, and generating a third matrix using correlations of the first and second elements with correlations of the third and fourth elements.
Abstract:
A system for identifying complete account identifiers from partial account identifiers is provided. The system includes an account identification computing device including at least one processor and a memory device in communication with the at least one processor. The processor is configured to receive transaction data including unique merchant identifiers, build a merchant table using the transaction data, and receive a list including partial account identifiers. The processor is further configured to determine, for each unique merchant identifier, a number of candidate account identifiers and calculate at least one metric based on the number of candidate account identifiers. The processor is further configured to identify a source unique merchant identifier and match at least one candidate account identifier to a complete account identifier by matching one of the partial account identifiers to the at least one candidate account identifier.
Abstract:
A common point of purchase (CPP) system for identifying a common point of purchases involved in fraudulent or unauthorized payment transactions is provided. The CPP system includes a common point of purchase (CPP) computing device that is configured to receive transaction data and build a table in a database, the table including recording pairing merchant and account identifiers. The CPP computing device is also configured to receive a list of flagged account identifiers and compare the list to the table. The CPP computing device is further configured to compute a metric and build a merchant profile for each merchant, for display at a client system.
Abstract:
The methods described herein are configured to obtain a first record pattern associated with the unidentified entity and select a second record pattern associated with an entity identifier of a known entity. Based on the first record pattern matching the second record pattern, the entity identifier of the known entity is associated to the unidentified entity to indicate that the unidentified entity and the known entity are the same. Determining the entity identifier of the unidentified entity enables the linking of separate identifier systems of data structures to facilitate communication and/or interaction between the data structures.
Abstract:
A system for identifying complete account identifiers from partial account identifiers is provided. The system includes an account identification computing device including at least one processor and a memory device in communication with the at least one processor. The processor is configured to receive transaction data including unique merchant identifiers, build a merchant table using the transaction data, and receive a list including partial account identifiers. The processor is further configured to determine, for each unique merchant identifier, a number of candidate account identifiers and calculate at least one metric based on the number of candidate account identifiers. The processor is further configured to identify a source unique merchant identifier and match at least one candidate account identifier to a complete account identifier by matching one of the partial account identifiers to the at least one candidate account identifier.