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 computing device for determining a new credit card number that is a continuation match with an old credit card number of a credit card account that has changed numbers comprises a processing element programmed to: receive transactional data for a plurality of credit card numbers, determine a plurality of old credit card numbers and a plurality of new credit card numbers, determine a plurality of clusters of new credit card numbers, convert the transactional data for each old credit card number and the associated cluster of new credit card numbers into snapshots with an image-like data format, train a modified siamese network with instances of snapshots of an old credit card number, a first new credit card number, and a second new credit card number, and use the modified siamese network to determine one new credit card number that is an upgrade of one old credit card number.

Abstract: The disclosure herein relates to AI-based methods and systems of using machine-learning to identify deceptive merchants in payment transactions such as recurring payment transactions. For example, the AI-based systems and methods may train and use an aggregate merchant matcher based on entity matching to identify merchant identifiers and/or acquirers that may be used by a merchant, train and use transaction classifiers to classify transactions as deceptive, recognize merchants based on an N-density aware transaction embedding learned from transaction data, and train and use a merchant classifier to classify merchants as deceptive.

Abstract: Embodiments provide methods and systems for predicting overall account-level risks of cardholders. The method performed by server system includes accessing payment transaction data associated with a cardholder from a transaction database. Method includes generating a set of transaction features based on a set of transaction indicators. The method includes determining a plurality of network risk scores associated with the cardholder based on the set of transaction features and a set of trained machine learning models. The plurality of network risk scores includes a payment capacity risk score, a contactless payment risk score, and a set of account-level risk scores. The method includes aggregating the plurality of network risk scores to calculate an overall account risk score associated with the cardholder based on a statistical model. The method also includes transmitting a notification to the issuer server associated with the cardholder based on the overall account risk score.

Abstract: In a computer simulation of an analog device in a digital circuit, a piece-wise linear lookup table is used to determine the channel resistance of the transistors in the analog device, allowing the node voltages to take on non-digital values. The piece-wise linear lookup table contains a set of channel resistances corresponding respectively to gate-to-source voltages. The program uses multi-terminal binary decision graphs (MTBDDs) to represent non-digital resistances, capacitances and voltages in the circuit as a function of symbolic inputs. The program can analyze circuits containing more than two voltage sources by modeling voltage sources with voltage dividers between the maximum and minimum voltages in the circuit.

Abstract: In a computer simulation of an analog device in a digital circuit, a piece-wise linear lookup table is used to determine the channel resistance of the transistors in the analog device, allowing the node voltages to take on non-digital values. The piece-wise linear lookup table contains a set of channel resistances corresponding respectively to gate-to-source voltages. The program uses multi-terminal binary decision graphs (MTBDDs) to represent non-digital resistances, capacitances and voltages in the circuit as a function of symbolic inputs. The program can analyze circuits containing more than two voltage sources by modeling voltage sources with voltage dividers between the maximum and minimum voltages in the circuit.