Computer-implemented multiple entity dynamic summarization systems and methods
First Claim
1. A computer-program product tangibly embodied in a non-transitory machine readable storage medium, and including instructions configured to cause a data processing apparatus to perform operations including:
- accessing a set of data, wherein the set of data includes information about multiple transactions;
computing a fraud score for each of the transactions, each of the fraud scores computed based on;
a portion of data in the set of data that corresponds to that transaction; and
a pre-determined fraud scoring model,wherein the fraud score represents a likelihood that a transaction falls within a pre-determined fraudulent category;
identifying a plurality of entities, wherein each of the entities is associated with a subset of transactions from the multiple transactions, and wherein, in each of the subsets, the transactions are associated with a common parameter;
generating a summary for each of the entities, wherein generating the summary includes identifying a fraudulent activity trend corresponding to the entity and wherein the fraudulent activity trend is determined using an analysis of the computed fraud scores;
storing each of the generated summaries in a computer memory; and
performing, on the computing device, a subsequent fraud detection on an additional transaction, wherein performing the subsequent fraud detection facilitates a decision for the additional transaction and includes;
receiving additional transaction data that represents the additional transaction;
associating the additional transaction with at least one of the identified entities; and
computing a fraud score for the additional transaction, wherein computing the fraud score for the additional transaction is based on the additional transaction data, the pre-determined fraud scoring model, and the summary of the at least one entity associated with the additional transaction.
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Accused Products
Abstract
Systems and methods are provided for operation upon data processing devices are provided for operating with a fraud detection system. As an example, a system and method can be configured for receiving, throughout a current day in real-time or near real-time, financial transaction data representative of financial transactions initiated by different entities. At multiple times throughout the day, a summarization of the financial transaction data (which has been received within a time period within the current day) is generated. The generated summarization is used to determine whether fraud has occurred with respect to a financial transaction contained in the received authorization data or with respect to a subsequently occurring financial transaction.
159 Citations
36 Claims
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1. A computer-program product tangibly embodied in a non-transitory machine readable storage medium, and including instructions configured to cause a data processing apparatus to perform operations including:
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accessing a set of data, wherein the set of data includes information about multiple transactions; computing a fraud score for each of the transactions, each of the fraud scores computed based on; a portion of data in the set of data that corresponds to that transaction; and a pre-determined fraud scoring model, wherein the fraud score represents a likelihood that a transaction falls within a pre-determined fraudulent category; identifying a plurality of entities, wherein each of the entities is associated with a subset of transactions from the multiple transactions, and wherein, in each of the subsets, the transactions are associated with a common parameter; generating a summary for each of the entities, wherein generating the summary includes identifying a fraudulent activity trend corresponding to the entity and wherein the fraudulent activity trend is determined using an analysis of the computed fraud scores; storing each of the generated summaries in a computer memory; and performing, on the computing device, a subsequent fraud detection on an additional transaction, wherein performing the subsequent fraud detection facilitates a decision for the additional transaction and includes; receiving additional transaction data that represents the additional transaction; associating the additional transaction with at least one of the identified entities; and computing a fraud score for the additional transaction, wherein computing the fraud score for the additional transaction is based on the additional transaction data, the pre-determined fraud scoring model, and the summary of the at least one entity associated with the additional transaction. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A computer-implemented method, comprising:
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accessing a set of data, wherein the set of data includes information about multiple transactions; computing, on a computing device, a fraud score for each of the transactions, each of the fraud scores computed based on; a portion of data in the set of data that corresponds to that transaction; and a pre-determined fraud scoring model, wherein the fraud score represents a likelihood that a transaction falls within a pre-determined fraudulent category; identifying a plurality of entities, wherein each of the entities is associated with a subset of transactions from the multiple transactions, and wherein, in each of the subsets, the transactions are associated with a common parameter; generating, on the computing device, a summary for each of the entities, wherein generating the summary includes identifying a fraudulent activity trend corresponding to the entity and wherein the fraudulent activity trend is determined using an analysis of the computed fraud scores; storing each of the generated summaries in a computer memory; and performing, on the computing device, a subsequent fraud detection on an additional transaction, wherein performing the subsequent fraud detection facilitates a decision for the additional transaction and includes; receiving additional transaction data that represents the additional transaction; associating the additional transaction with at least one of the identified entities; and computing a fraud score for the additional transaction, wherein computing the fraud score for the additional transaction is based on the additional transaction data, the pre-determined fraud scoring model, and the summary of the at least one entity associated with the additional transaction. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24)
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25. A system, comprising:
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a computer processor programmed to perform operations including; accessing a set of data, wherein the set of data includes information about multiple transactions; computing a fraud score for each of the transactions, each of the fraud scores computed based on; a portion of data in the set of data that corresponds to that transaction; and a pre-determined fraud scoring model, wherein the fraud score represents a likelihood that a transaction falls within a pre-determined fraudulent category; identifying a plurality of entities, wherein each of the entities is associated with a subset of transactions from the multiple transactions, and wherein, in each of the subsets, the transactions are associated with a common parameter; generating a summary for each of the identified entities, wherein generating the summary includes identifying a fraudulent activity trend corresponding to the entity and wherein the fraudulent activity trend is determined using an analysis of the computed fraud scores; storing each of the summaries in a computer memory; and performing a subsequent fraud detection on an additional transaction, wherein performing the subsequent fraud detection facilitates a decision for the additional transaction and includes; receiving additional transaction data that represents the additional transaction; associating the additional transaction with at least one of the identified entities; and computing a fraud score for the additional transaction, wherein computing the fraud score for the additional transaction is based on the additional transaction data, the pre-determined fraud scoring model, and the summary of the at least one entity associated with the additional transaction. - View Dependent Claims (26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36)
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Specification