Visa International Service Association

University of Texas System

Yiwei Cai

Peng Wu

Dan Wang

Claudia Carolina Barcenas Cardenas

Harishkumar Sundarji Majithiya

Roshni Ann Samuel

Claudia Barcenas

Youxing Qu

Himanshu Chauhan

Pei Yang

Susan Finnegan

Minghua Xu

Shengfei Gu

Qingguo Chen

Olawunmi George

Quingguo Chen

Provided is a method for fraud prevention using deep learning and survival models. 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 subperiod of a plurality of subperiods 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 subperiod may be associated with a probability that a fraudulent transaction will not occur by the respective subperiod. A system and computer program product are also disclosed.

Method, system, and computer program product for fraud prevention using deep learning and survival models

A computer-implemented method includes, for each of a set of training dates: receiving, for each of a sequence of dates including the training date, an input data array representing values of a predetermined set of date-dependent features; receiving a target output corresponding to an evaluation of a predetermined metric at the training date; and performing an update routine including processing the input data array for each date using first layers of a neural network, processing a resulting intermediate data array using second layers of the neural network to generate a network output, and updating values of parameters of the neural network in in a direction of a negative gradient of an error between the target output and the network output. The data processing system is then arranged to generate an embedding array by processing an input data array for each of a given sequence of dates using the first layers of the neural network.

Generating input data for a machine learning model

Provided are systems for authenticating an individual using image feature templates that include at least one processor to train a first machine learning model based on a training dataset of a plurality of images of a user, generate a plurality of image feature templates using the first machine learning model, wherein each image feature template of the plurality of image feature templates is associated with a positive authentication of the identity of the user during a time interval, generate a second machine learning model based on the plurality of image feature templates, generate a predicted image feature template using the second machine learning model, determine whether to authenticate the identity of the user based on an input image of the user, and perform an action based on determining whether to authenticate the identity of the user. Methods and computer program products are also provided.

System, method, and computer program product for evolutionary learning in verification template matching during biometric authentication

Methods, systems, and computer program products are provided for cleaning noisy data from unlabeled datasets using autoencoders. A method includes receiving training data including noisy samples and other samples. An autoencoder network is trained based on the training data to increase a first metric based on the noisy samples and to reduce a second metric based on the other samples. Unlabeled data including unlabeled samples is received. A plurality of third outputs is generated by the autoencoder network based on the plurality of unlabeled samples. For each respective unlabeled sample, a respective third metric is determined based on the respective unlabeled sample and a respective third output, and whether to label the respective unlabeled sample as noisy or clean is determined based on the respective third metric and a threshold. Each respective unlabeled sample determined to be labeled as noisy is cleaned.

System, method, and computer program product for cleaning noisy data from unlabeled datasets using autoencoders

Methods for generating fraud detection rules based on transaction data may include receiving historical transaction data, associating tags with each transaction, generating decision trees having root nodes and child nodes operably connected to the respective root nodes, determining at least one primary rule and at least one set of secondary rules associated with the at least one primary rule based on relationships between features of the transactions, assigning primary rules and sets of secondary rules to the at least one decision tree to populate the tree, extracting a plurality of rule sets including at least one primary rule and one or more secondary rules, determining an ordering of the plurality of rule sets; and determining a subset of rule sets from the ordered plurality of rule sets against which subsequently received transactions are compared against to determine if the subsequent transactions are fraudulent.

System, method, and computer program product for determining fraud rules

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Austin, TX, United States of America

Yiwei Cai