Abstract: Embodiments provide methods and systems for predicting panic situation in a region and detecting panic states of merchants in the region. Method performed by server system includes accessing payment transaction data associated with merchants from transaction database and identifying panic trigger indicating panic situation in region based on transaction features associated with merchants. In response to identifying panic trigger, method includes generating transaction features based on payment transactions of merchant over time duration and determining association between merchant and merchant cluster based on transaction features associated with merchant. Method includes predicting time-series transaction data associated with merchant based on deep neural network model and merchant cluster associated with merchant, and calculating error between predicted time-series transaction data and real time-series transaction data associated with merchant.

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.

Abstract: Embodiments provide methods and systems for detecting frauds in payment transactions made by payment instrument using spend patterns of multiple payment instruments associated with user. The method performed by server system includes receiving payment transaction data associated with first payment instrument including information of payment transaction performed at particular merchant. Method includes generating multivariate payment transaction sequence associated with one or more second payment instruments of user. Method includes predicting simulated univariate payment transaction sequence associated with the first payment instrument based on first neural network model and the multivariate payment transaction sequence. Method includes providing simulated univariate payment transaction sequence and real univariate payment transaction sequence of first instrument to second neural network model.

Abstract: Systems and computer-implemented methods of modeling card member data to classify a card member into one of a plurality of classifications based on interchange fees derived from the use of a card issued to the card member. The modeling may handle data distribution from one time period to another time period to address unavailability and/or variability of historical data, implement a neural network architecture based on transformers and discriminators for accurate data scaling, perform data filling for missing data, and fine-tuning for card types that have less card member data, which may result in enhanced performance and faster convergence resulting in reduced computational time. Such fine-tuning may leverage uniform standardization in the neural network to handle multiple card types, which is facilitated through the use of the transformers and discriminators for data scaling.

Abstract: Embodiments provide methods and systems for detecting frauds in payment transactions made by payment instrument using spend patterns of multiple payment instruments associated with user. The method performed by server system includes accessing payment transaction data associated with a plurality of customers from a transaction database. The method includes training a first generative adversarial network (GAN) model based on the payment transaction data and a plurality of probable fraud risk conditions. The first GAN model is trained to generate simulated customer fraud behaviors. The method includes filtering, by the server system, the simulated customer fraud behaviors based on a predetermined filtering criteria. The method includes generating, by the server system, fraud risk scores for the simulated customer fraud behaviors based on a fraud risk model. The method includes extracting fraud risk rules based on a set of simulated customer fraud behaviors from the simulated customer fraud behaviors.