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 method for detecting fraudulent transactions includes generation of a graph including a plurality of nodes and a plurality edges between the plurality of nodes based on historical transaction data of a plurality of historical transactions. The plurality of nodes include a set of merchant nodes and a set of consumer nodes. A set of static features and a plurality of dynamic features are determined based on the historical transaction data and the generated graph, respectively. A neural network is trained based on the set of static features and the plurality of dynamic features for detection of transaction fraud. The neural network is used to detect a first transaction as one of a fraudulent transaction or a legitimate transaction based on first transaction data of the first transaction.

Abstract: Embodiments provide artificial intelligence methods and systems for evaluating vulnerability risks of issuer authorization system. Method performed by a server system includes accessing a set of payment transaction data including subset of fraudulent transaction data. Method includes generating via a machine learning model, set of synthetic transaction data based on the subset of fraudulent transaction data. Method includes accessing set of historical card velocity features and collating the set of synthetic transaction data and the set of historical card velocity features to generate set of enriched synthetic transaction data. Method includes extracting via a classifier, subset of feasible fraudulent transaction data from the set of enriched synthetic transaction data. Method includes generating simulated authorization model based on the set of payment transaction data.

Abstract: Various embodiments relate to methods and systems for generating adversarial samples. The method performed by a server system includes accessing a set of payment transaction samples from transaction database. The method includes initializing a plurality of encoders, weights of each of the plurality of encoders being randomly initialized. Further, the method includes computing a set of initial adversarial samples using the plurality of encoders based on the set of payment transaction samples. Further, the method includes optimizing the plurality of encoders to generate a plurality of evolved encoders. Further, method includes computing a plurality of fitness scores for the plurality of evolved encoders. Further, the method includes determining a top evolved encoder from the plurality of evolved encoders based on the plurality of fitness scores. Further, the method includes generating a set of final adversarial samples using the top evolved encoder based on the set of payment transaction samples.

Abstract: A method for determining the validity of a computational model using a blockchain and zero knowledge principles includes: storing, in a memory of a first computing system, a computational model; receiving, by a receiver of the first computing system, a blockchain data value from one block of a plurality of blocks comprising a blockchain, wherein the blockchain data value includes a data set; receiving, by the receiver of the first computing system, an expected accuracy value; applying, by a processor of the first computing system, the data set to the computational model to generate a result value; and determining, by the processor of the first computing system, a validity measurement for the computational model based on a comparison of the generated result value and the expected accuracy value.

Abstract: Embodiments provide methods and systems for merchant data cleansing in payment network. Method performed by server system includes accessing electronic payment transaction records from transaction database. Each electronic payment transaction record includes merchant data fields. Method includes determining set of electronic payment transaction records with ambiguous merchant data fields having matching probability scores less than predetermined threshold value computed by probabilistic matching model and identifying at least one issue for non-matching of each of set of electronic payment transaction records. Method includes determining data model based on at least one issue of each of set of electronic payment transaction records. Data model is one of: phone-to-city model, payment aggregator model, and merchant name normalization model.

Abstract: The disclosure relates to AI-based machine-learning and natural language modeling to identify semantic similarities between sets of content having natural language text. For example, a system may generate a relevance classification that indicates whether content such as articles are non-specifically relevant to charities without identifying a particular charity. If the content is non-specifically relevant to charities, the system may apply a natural language model to generate sentence embeddings based on the content and determine a level similarity between the sentence embeddings and a query embedding generated from a charity query. The charity query may itself be generated from a full description of the charity through an encoder-decoder architecture with reinforcement learning.

Abstract: Embodiments provide methods and systems for dynamic spend policy optimization. Method performed by server system includes receiving payment authorization request for payment transaction initiated by cardholder from acquirer. The payment authorization request includes transaction data. The method includes determining spend variables associated with cardholder based on transaction data and identifying at least one cardholder segment from a plurality of cardholder segments based on the spend variables and a clustering model. The at least one cardholder segment is associated with cardholder. The method includes accessing spend policy rules applicable to the payment transaction based on the transaction data and determining optimal spend threshold values corresponding to the spend policy rules applicable to the payment transaction based on the at least one identified cardholder segment and a reinforcement learning (RL) model.

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: 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: Embodiments provide methods and systems for reducing decline rates of transaction requests in card-on-file payment transactions. Method performed by server system includes accessing information of a card-on-file payment transaction for a cardholder. The information includes a payment account of the cardholder and a payment amount to be paid to a merchant account of a merchant. Method includes determining a hidden state associated with the cardholder based, at least in part, on a deep Markov model and the payment amount. The deep Markov model is trained based, at least in part, on past customer spending features associated with the cardholder. Method includes predicting a likelihood score of being the card-on-file payment transaction getting approved within a particular time window based, at least in part, on the hidden state associated with the cardholder and providing a notification to the merchant based, at least in part, on the likelihood score.

Abstract: The invention enables optimizing performance of a recommendation server. The invention comprises (i) receiving a first set of customer information corresponding to a first set of events recorded in a first time period in which the recommendation server operates in a first configuration state, (ii) generating a first performance evaluation score based on the first set of customer information, (iii) reconfiguring the recommendation server to operate in a second configuration state having a second performance evaluation score associated therewith, and wherein said second performance evaluation score is generated based on a second set of customer information corresponding to a second set of events recorded in a second time period in which the recommendation server operates in the second configuration state and (iv) transmitting to a terminal device, one or more electronic offers selected for transmission to the customer by the recommendation server operating in the second configuration state.

Abstract: Embodiments provide methods and systems for recommending one or more authorizing components to issuers and/or merchants for enhancing approval rates of payment processing requests. Method performed by server system includes receiving a payment authorization request for a payment transaction between a cardholder and a merchant in real time. The method includes identifying payment transaction features associated with the payment transaction based, at least in part, on the payment authorization request. The method further includes predicting a combination of one or more authorizing components to be applied to the payment transaction to obtain a product recommendation strategy for the payment transaction. The combination of one or more authorizing components is predicted based, at least in part, on a trained machine learning model and the payment transaction features.

Abstract: Embodiments provide electronic methods and systems for facilitating payment authorization for payment transactions initiated from an on-board device of an autonomous vehicle. The method performed by a server system includes receiving payment transaction request initiated from on-board device positioned in autonomous vehicle. The method further includes accessing authentication parameters received from on-board device, wherein authentication parameters include multisensory data captured using sensors positioned in autonomous vehicle, and generating authentication features based on authentication parameters and neural network models. The neural network models are trained based on historical multisensory data of one or more autonomous vehicles.

Abstract: A method for detecting fraudulent transactions includes generation of a graph including a plurality of nodes and a plurality edges between the plurality of nodes based on historical transaction data of a plurality of historical transactions. The plurality of nodes include a set of merchant nodes and a set of consumer nodes. A set of static features and a plurality of dynamic features are determined based on the historical transaction data and the generated graph, respectively. A neural network is trained based on the set of static features and the plurality of dynamic features for detection of transaction fraud. The neural network is used to detect a first transaction as one of a fraudulent transaction or a legitimate transaction based on first transaction data of the first transaction.

Abstract: A method for facilitating entity resolution is provided. A server generates a graph based on a first dataset comprising a first entity and a second dataset comprising a plurality of entities. Each node in the generated graph corresponds to the first entity or one of the plurality of entities. The server generates a plurality of embeddings for the plurality of nodes in the generated graph. Each of the plurality of embeddings represents an entity as a point in a d-dimensional embedding space. The server identifies a set of nearest neighbors for the first entity based on the plurality of embeddings. The server determines a similarity metric for each of the identified nearest neighbor with respect to the first entity. The server associates the first entity with a second entity of the second dataset that corresponds to a nearest neighbor in the set of nearest neighbors.

Abstract: Embodiments provide anti-money laundering methods, and systems for detecting potential money laundering financial transactions using artificial intelligence. The method performed by a server system includes receiving data elements associated with financial activities of users who are associated with at least one issuer. The data elements include transaction data associated with users. The method includes identifying graph features based on data elements, and creating temporal knowledge graph based on the graph features. The temporal knowledge graph represents a computer-based graph representation of the users as nodes and relations among the nodes as edges.

Abstract: The invention enables optimizing performance of a recommendation server. The invention comprises (i) receiving a first set of customer information corresponding to a first set of events recorded in a first time period in which the recommendation server operates in a first configuration state, (ii) generating a first performance evaluation score based on the first set of customer information, (iii) reconfiguring the recommendation server to operate in a second configuration state having a second performance evaluation score associated therewith, and wherein said second performance evaluation score is generated based on a second set of customer information corresponding to a second set of events recorded in a second time period in which the recommendation server operates in the second configuration state and (iv) transmitting to a terminal device, one or more electronic offers selected for transmission to the customer by the recommendation server operating in the second configuration state.