Paypal, Inc.

Aditya Jain

Yogesh Kandlur

Piyush Neupane

Teddy Vincent Toms

Kiran Kumar Sridhar

Balaji Raja

Visak Chockalingom

There are provided systems and methods for identifying transaction processing retry attempts based on machine learning models for transaction success. A service provider, such as an electronic transaction processor for digital transactions, may detect a failure of data processing for a transaction when processed with a separate data processing system, such as a card processing system for payment cards. In order to minimize cost and wasted resources for retrying transactions that are likely to further fail, a machine learning model may be implements that generates a predictive score for whether a failed transaction is likely to be successful if retried with the data processing system. The predictive score may be used to predict a probability of success, which may then be used with a cost function to determine a cost to retry the failed transaction and a cost to stop a retry of the failed transaction.

Identifying transaction processing retry attempts based on machine learning models for transaction success

Systems and techniques for facilitating card authentication technique selection via machine learning are provided. In various embodiments, a processor can access an attribute vector associated with a financial payment card. In various instances, the processor can determine, via execution of a machine learning model, whether to authenticate the financial payment card with a zero-dollar authentication technique or instead with a tokenization authentication technique, based on the attribute vector. In various cases, the processor can execute the zero-dollar authentication technique with respect to the financial payment card, when the processor determines that the financial payment card should be authenticated with the zero-dollar authentication technique. In various aspects, the processor can execute the tokenization authentication technique with respect to the financial payment card, when the processor determines that the financial payment card should be authenticated with the tokenization authentication technique.

Card authentication technique selection via machine learning

Methods, systems, and computer program products for secure cardless cash withdrawal are disclosed. For example, a computer-implemented method may include detecting presence of a user device at an automated teller machine (ATM) by one or more sensors, requesting an authorization indicator for a cash request in response to the detecting, receiving the authorization indicator associated with the cash request at the ATM where presentation of the authorization indicator at any one of a plurality of ATMs would authorize the cash request, and distributing funds associated with the cash request in response to receiving the authorization indicator.

Secure cardless cash withdrawal

Methods, systems, and computer program products for secure cardless cash withdrawal are provided. A computer-implemented method may include identifying one or more cash machines near a computing device of a user, receiving a cash request from the computing device, generating a one-time authorization code on a secure server for the cash request when the computing device is detected by a sensor of a selected cash machine, providing the one-time authorization code to the computing device for display to the user when the computing device is detected within a distance allowing the user to operate the cash machine, validating a user-provided code received from the selected cash machine in view of the one-time authorization code, instructing the selected cash machine to distribute the requested cash when the user-provided code is validated, and invalidating the one-time authorization code when the cash request is completed.

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San Jose, CA, United States of America

Aditya Jain