Abstract: Methods and server systems for computing fraud risk scores for various merchants associated with an acquirer described herein. The method performed by a server system includes accessing merchant-related transaction data including merchant-related transaction indicators associated with a merchant from a transaction database. Method includes generating a merchant-related transaction features based on the merchant-related indicators. Method includes generating via risk prediction models, for a payment transaction with the merchant, merchant health and compliance risk scores, merchant terminal risk scores, merchant chargeback risk scores, and merchant activity risk scores based on the merchant-related transaction features. Method includes facilitating transmission of a notification message to an acquirer server associated with the merchant.

Abstract: Methods, apparatus, and processor-readable storage media for implementing a message management framework using machine learning techniques are provided herein. An example computer-implemented method includes processing a message comprising identifying at least one message type of the message; determining, based at least in part on the at least one identified message type, one or more message failures by applying one or more machine learning-based rules to at least a portion of the message; determining one or more remedial actions by processing, using one or more machine learning techniques, the at least one identified message type, the one or more determined message failures, and multiple attributes of the message; and performing one or more automated actions based at least in part on the one or more determined remedial actions.

Abstract: Methods, apparatus, and processor-readable storage media for implementing a message management framework using machine learning techniques are provided herein. An example computer-implemented method includes processing a message comprising identifying at least one message type of the message; determining, based at least in part on the at least one identified message type, one or more message failures by applying one or more machine learning-based rules to at least a portion of the message; determining one or more remedial actions by processing, using one or more machine learning techniques, the at least one identified message type, the one or more determined message failures, and multiple attributes of the message; and performing one or more automated actions based at least in part on the one or more determined remedial actions.