Abstract: A method may include extracting, from a transaction, a routing transit number (RTN) and an account number, embedding the RTN to obtain an RTN vector, embedding the account number to obtain an account number matrix, combining, using a trained machine learning model, the RTN vector and the account number matrix to obtain a combined matrix, and classifying the account number as invalid. The classifying may include applying the trained machine learning model to the combined matrix.

Abstract: Certain aspects of the present disclosure provide techniques for identifying incorrect account numbers using machine learning. One example method includes receiving, over a network from a user device, a transaction including an account number and providing the account number to a machine learning model. The method further includes obtaining output from the machine learning model including a confidence score for the account number and transmitting, over the network to the user device, the confidence score. The method further includes receiving, over the network from the user device, a response to transmitting the confidence score and processing the transaction based on the response. The method further includes updating a transaction database based on success or failure of the transaction.

Abstract: Certain aspects of the present disclosure provide techniques for identifying incorrect account numbers using machine learning. One example method includes receiving, over a network from a user device, a transaction including an account number and providing the account number to a machine learning model. The method further includes obtaining output from the machine learning model including a confidence score for the account number and transmitting, over the network to the user device, the confidence score. The method further includes receiving, over the network from the user device, a response to transmitting the confidence score and processing the transaction based on the response. The method further includes updating a transaction database based on success or failure of the transaction.

Abstract: A method may include extracting, from a transaction, a routing transit number (RTN) and an account number, embedding the RTN to obtain an RTN vector, embedding the account number to obtain an account number matrix, combining, using a trained machine learning model, the RTN vector and the account number matrix to obtain a combined matrix, and classifying the account number as invalid. The classifying may include applying the trained machine learning model to the combined matrix.