Methods, systems, and computer program products for fraud prevention using deep learning and survival models

A deep learning, computer technology, applied in neural learning methods, biological neural network models, computing, etc., can solve problems such as inefficient operation, inability to provide fraud possibility, inability to consider time, etc.

Pending Publication Date: 2022-04-22
VISA INT SERVICE ASSOC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Additionally, such models cannot provide any indication of the likelihood of fraud at different times in the future, or account for changes in the likelihood of fraud at different times (e.g., sub-periods) within the time period
Furthermore, such models can only consider whether fraud occurred within a...

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  • Methods, systems, and computer program products for fraud prevention using deep learning and survival models
  • Methods, systems, and computer program products for fraud prevention using deep learning and survival models
  • Methods, systems, and computer program products for fraud prevention using deep learning and survival models

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[0059] For purposes of description below, the terms "end", "upper", "lower", "right", "left", "vertical", "horizontal", "top", "bottom", "transverse", " "Portrait" and its derivatives shall refer to the orientation of the disclosed subject matter as it is shown in the drawings. It should be understood, however, that the disclosed subject matter may employ various alternative variations and step sequences, except where the contrary is expressly specified. It is also to be understood that the specific devices and processes shown in the drawings and described in the following specification are merely exemplary embodiments or aspects of the disclosed subject matter. Accordingly, specific dimensions and other physical characteristics associated with the embodiments or aspects disclosed herein are not to be considered as limiting unless otherwise indicated.

[0060] No aspect, component, element, structure, act, step, function, instruction etc. used herein should be construed as cr...

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Abstract

A method of fraud prevention using deep learning and survival models is provided. The method may include receiving, with at least one processor, transaction data associated with a plurality of transactions of at least one payment account; at least one attempted attack may be detected based on the transaction data. A fraud risk score for each of a plurality of sub-periods in a time period following the at least one attempted attack may be generated based on the transaction data using a deep learning model and a survival model. The fraud risk score for each respective sub-period may be associated with a probability that the respective sub-period does not have a fraudulent transaction. A system and a computer program product are also disclosed.

Description

technical field [0001] The disclosed subject matter generally relates to methods, systems and products for fraud prevention, and in some specific embodiments or aspects, to methods, systems and computer program products for fraud prevention using deep learning and survival models. Background technique [0002] Service provider systems (eg, servers, etc.) in electronic networks can process a large number of events (eg, messages) on a daily basis. For example, a transaction service provider system in an electronic payment processing network may process thousands of transactions (eg, transaction messages, such as authorization requests and / or authorization responses) per second. Some of these transactions may be fraudulent, but determining which transactions may and / or are actually fraudulent may be difficult. For example, 100,000 to 300,000 account identifiers (eg, Primary Account Numbers (PAN), etc.) may be compromised per day. An attack may refer to an attempt to conduct a...

Claims

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Application Information

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IPC IPC(8): G06Q40/02G06Q10/06G06N3/04G06N3/08
CPCG06Q10/0635G06N3/08G06N3/045G06Q40/03G06Q30/0185G06Q40/12G06N20/00G06Q20/102G06Q20/34G06Q20/4016G06Q20/405G06Q20/3278G06Q20/204G06Q20/20G06N3/044
Inventor 吴朋杨沛蔡一伟克劳迪娅·卡罗里那·巴塞纳斯·卡德纳斯
Owner VISA INT SERVICE ASSOC
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