TECHNIQUES FOR DETECTING UNDESIRABLE BATCH TRANSACTIONS

原文（中文）

Techniques are described herein for detecting undesirable batch transactions. A machine-learning model that has been trained to determine a likelihood that a given batch transaction is fraudulent may be obtained. The machine-learning model may be trained with a supervised learning algorithm and a training data set example of the training data set comprising a corresponding hash value generated from one or more data fields of at least one batch transaction header and a label indicating whether the training data set example is associated with a fraudulent batch transaction. The method may include generating a hash value based at least in part on providing a set of data field values of one or more batch transaction headers to a hashing algorithm as input. The method may include determining that the batch transaction is fraudulent based at least in part on output received from the machine-learning model.