Intuit, Inc.

Inuit, Inc.

Henry Venturelli

Natalie De Shetler

Runhua Zhao

Guy Shtar

Itsik Yizhak Mantin

Nikolas Terani

Ron Bitton

Zeyu Li

Taylor Cressy

Damayanti Sengupta

Vinay Patlolla

Taylor J Cressy

Nicholas John Stang

Yael Mathov Gome

A method includes receiving, at a server from a user device, a user prompt segment to a large language model (LLM), obtaining an additional prompt segment from a prompt data source, identifying a electronic address in the prompt segment, replacing the electronic address with a placeholder to generate a updated prompt segment, generating a LLM prompt comprising the updated prompt segment and the user prompt segment, and sending the LLM prompt to the LLM. The method further includes receiving a response to the LLM prompt from the LLM, the response comprising the placeholder, replacing the placeholder with the electronic address to generate an updated response, and sending the updated response to the user device.

Malicious prompt management for large language models

A method may include generating a vector from unstructured data included in an untransformed transaction, and determining, for the vector, a cluster ID of cluster IDs by matching the vector with a matching cluster vector of cluster vectors. The method may further include generating a query using the cluster ID and the untransformed transaction, and transforming, using the cluster IDs, untransformed transactions to transformed transactions. The transformed transactions may each include a cluster ID. The method may further include generating, using the query, a query result from features of the transformed transactions, generating a fraud score using the query result, and presenting the fraud score and the cluster ID.

Embedding service for unstructured data

In general, in one aspect, one or more embodiments relate to a method including receiving, in a business rules engine, input data from disparate data sources. The input data describes a merchant and an application by the merchant to use an electronic payments system for processing transactions between the merchant and customers. Featurization is performed on the input data to form a machine readable vector. By applying the machine readable vector as input to a machine learning model in a machine learning layer, a risk score is predicted. The machine learning model is trained using training data describing use of the electronic payments system by other merchants. The risk score is an estimated probability of the merchant being unable to satisfy an obligation of using the electronic payments system. A business rules engine, based on the risk score, limits use of the electronic payments system by the merchant.

Machine learning-based determination of limits on merchant use of a third party payments system

Certain aspects of the present disclosure provide techniques for detecting fraudulent transactions in a transaction processing system. An example method generally includes receiving a request to process a transaction. An input data set including a vector representing the transaction and a plurality of vectors representing historical transactions is generated. The input data set is divided into a plurality of ragged tensors corresponding to non-overlapping time segments of variable length and having a plurality of vectors associated with dates within each time segment A reduced input data set is generated by generating, for each respective ragged tensor of the plurality of ragged tensors, a respective representative vector using max pooling over vectors in the ragged tensor. A fraudulent transaction score is generated based on the reduced input data set using a fraud detection model. The transaction is processed based, at least in part, on the fraudulent transaction score.

Method and system for detecting fraudulent transactions using a fraud detection model trained based on dynamic time segments

A method is disclosed. The method includes obtaining an access request associated with a user for a software application; obtaining a plurality of verification attributes associated with the user; generating a fraud score for the access request by feeding a supervised machine learning (ML) classifier with a feature vector for the user that is based on the plurality of verification attributes; selecting a first unsupervised ML anomaly detector of a plurality of unsupervised ML anomaly detectors based on the fraud score; generating an anomaly score for the access request by feeding the first unsupervised ML anomaly detector with an augmented feature vector for the user that is based on the plurality of verification attributes and the fraud score; and processing the access request based on the anomaly score.

Online fraud detection using machine learning models

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Los Angeles, CA, United States of America

Henry Venturelli