Paypal, Inc.

Yanfei Dong

Yuan Deng

Hewen Wang

Oscar Charles Edward Sanderson

Jessica Tjong

Chern Jie Lim

Ziyuan Pan

Yuzhen Zhuo

Xiaodong Yu

Jiazheng Zhang

Francesco Gelli

Ting Lin

Soujanya Poria

A method of categorizing text entries on a document can include determining, for each of a plurality of text bounding boxes in the document, respective text, respective coordinates, and respective input embeddings. The method may further include defining a graph of the plurality of bounding boxes, the graph comprising a plurality of connections among the plurality of bounding boxes, each connection comprising a first and second bounding box and zero or more respective intermediate bounding boxes. The method may further include determining a respective attention value for each connection according to a quantity of intermediate bounding boxes in the connection and, based on a the respective attention values and a transformer-based machine learning model applied to the respective input embeddings and respective coordinates, determining output embeddings for each bounding box and, based on the respective output embeddings, generating a bounding box label for each bounding box.

Content extraction based on hop distance within a graph model

A machine learning engine may be trained using artificial intelligence techniques and used according to techniques discussed herein. While an initial electronic transaction for a resource may be permitted, a subsequent related transaction to the initial electronic transaction may be analyzed in view of additional electronic information that was not available at the time of the initial transaction. Analysis of the subsequent related transaction, using the machine learning engine, may indicate a new classification related to the resource and/or the acquisition of the resource. Based on this new classification, usage of the resource may be restricted and/or denied, and the initial transaction for the resource may even be canceled retroactively.

Machine learning engine using following link selection

A system can comprise a processor that can facilitate performance of operations, comprising accessing a document comprising a plurality of text bounding boxes, wherein each respective text bounding box of the plurality of text bounding boxes comprises respective text, for each respective text bounding box, determining respective text bounding box coordinates and respective text bounding box input embeddings, based on the respective text bounding box coordinates, determining respective text bounding box positional encodings for each respective text bounding box, based on a transformer-based deep learning model applied to the respective text bounding box input embeddings, respective text bounding box coordinates, respective text bounding box positional encodings, and bias information representative of a modification to an attention weight of the transformer-based deep learning model, determining respective output embeddings for each respective text bounding box, and based on the respective output embeddings, generating respective bounding box labels for each respective bounding box.

Polar relative distance transformer

Systems and methods for data augmentation in a neural network system includes performing a first training process, using a first training dataset on a neural network system including an autoencoder including an encoder and a decoder to generate a trained autoencoder. A trained encoder is configured to receive a first plurality of input data in an N-dimensional data space and generate a first plurality of latent variables in an M-dimensional latent space, wherein M is an integer less than N. A sampling process is performed on the first plurality of latent variables to generate a first plurality of latent variable samples. A trained decoder is used to generate a second training dataset using the first plurality of latent variable samples. The second training dataset is used to train a first classifier including a first classifier neural network model to generate a trained classifier for providing transaction classification.

Data augmentation in transaction classification using a neural network

Methods and systems for training a computer-based classification model for classifying data are presented. The computer-based classification model is configured to classify data into one of a plurality of classifications. An initial training data set for training the classification model is obtained. In some embodiments, the training data within the initial training data set is grouped into multiple clusters, and training data within one or more clusters having corresponding ratio between a first classification and a second classification below a threshold ratio is removed from the initial training data set to generate the modified training data set. The modified training data set, instead of the initial training data set, is used to train the classification model.

Systems and methods for training a data classification model

A fraud detection model is used by a computer system to evaluate whether to grant a request to access a secure electronic resource. Before granting the request, the computer system evaluates the request using a multi-partite graph model generated using a plurality of previous requests. The multi-partite graph model includes at least a first set of nodes for sender accounts, a second set of nodes for recipient accounts, and a third set of nodes.

Detecting fraud using machine-learning

A fraud detection model is used by a computer system to evaluate whether to grant a request to access a secure electronic resource. The fraud detection model is generated by the computer using a plurality of previously received requests. Each request is associated with a sender account of the computer system and a recipient account to which the computer system previously sent a message containing a link to the secure electronic resource.

A machine learning engine for fraud detection following link selection may be trained using artificial intelligence techniques and used according to techniques discussed herein. A buyer account may be used to establish and generate a digital gift card having a particular value specified by the buyer. The digital gift card may then be conveyed to another account, such as an email address. The digital gift card may be provided with an online electronic process for redemption and use of the value, for example, by selecting a link and navigating to the process. When the claimer account attempts to utilize the value of the gift card by navigating to the process or otherwise engaging in the electronic process through a device, a risk and fraud analysis engine may execute to determine, based on real-time data of the claimer account, the buyer account, and/or device, whether the digital gift card was generated fraudulently or is being used fraudulently.

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Yanfei Dong