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: A growth predictor includes a monitor, a prediction engine, and a prioritization engine. The monitor receives or generates first information of a network already identified as a candidate money laundering (ML) network by an anti-money-laundering system. The prediction engine predicts second information indicative of a growth size of the ML network at a future time based on the first information. The prediction engine executes one or more predictive models to generate the second information indicative of growth size based on the first information, which indicates one or more changes that have occurred in the candidate ML network over a past period of time. The prioritization engine determines a priority of the candidate ML network based on the second information.

Abstract: Embodiments provide methods and systems for predicting chargeback behavioral data of an account holder. The method performed by a server system includes accessing payment transaction data associated with the account holder from a transaction database. The payment transaction data includes a set of transaction indicators corresponding to payment transactions performed by the account holder within a predetermined time period. The method further includes generating a set of transaction features based on the set of transaction indicators. Furthermore, the method includes computing, via a chargeback risk prediction model, a set of chargeback risk probability scores corresponding to one or more time intervals associated with the account holder based, at least in part, on the set of transaction features. The method also includes transmitting a notification to an issuer server associated with the account holder based, at least in part, on the set of chargeback risk probability scores.