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 and server systems for improving accuracy of authorization optimizer are described herein. Method performed by server system includes receiving a Non-Sufficient Funds (NSF) error message from an acquirer server. Method includes accessing historical transaction data from a transaction database. The historical transaction data includes transaction related information associated with a plurality of users. Method includes generating a plurality of transaction features associated with the user based on the historical transaction data. Method includes determining via an authorization optimizer model, an optimal time slot from a plurality of time slots for the user based on the plurality of transaction features associated with the user. The optimal time slot indicates an optimal time window for the acquirer server to transmit an upcoming recurring payment request to the payment account of the user.

Abstract: A method for generating digital certificates for anonymous users in blockchain transactions includes: storing a blockchain comprised of a plurality of blocks, each block including a block header and transaction values, where each transaction value includes data related to a blockchain transaction including a sending address, recipient address, and transaction amount; receiving a certificate request from a computing device, the request including a user public key of a cryptographic key pair; identifying a subset of transaction values in the blockchain where the sending address or recipient address was generated using the user public key; determining a confidence level based on the data included in each transaction value included in the subset; generating a digital certificate based on the determined confidence level; and transmitting the generated digital certificate to the computing device.

Abstract: A method for preventing unauthorized repeat purchases of a product includes: storing a plurality of purchase data values, each related to a completed financial transaction and including a purchase identifier; receiving an authorization request for a new financial transaction from an external system, the authorization request including a specific identifier; identifying a number of purchase data values where the included purchase identifier matches the specific identifier; and transmitting an authorization response to the external system, the authorization response including (i) an approval of the new financial transaction if the number of purchase data values is below a predetermined number, or (ii) a denial of the new financial transaction if the number of purchase data values is equal to or above the predetermined number.

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 optimized validation of a blockchain transaction through the use of a parallel database and date management includes: receiving a new blockchain transaction that includes unspent transaction outputs; identifying, in a parallel database, a start date for a blockchain wallet involved in the new transaction; identifying a subset of blocks in the blockchain that have been added to the blockchain since the start date; validating the new blockchain transaction by validating the unspent transaction outputs using the subset of blocks; and, if the validation is successful, adding the new transaction to the blockchain, or, if the validation is unsuccessful, notifying the submitter of the failed validation.

Abstract: A method for risk scoring a blockchain transaction includes: storing blockchain data associated with a blockchain, the data including a plurality of blocks, each block including a block header and transaction values, each transaction value including a sending address, recipient address, and transaction amount; receiving a new transaction value related to a proposed blockchain transaction and a node identifier associated with a node included in a blockchain network associated with the blockchain, the new transaction value including a user address, transacting address, and blockchain amount; identifying transaction values in the blockchain where the sending address or recipient address is associated with a transacting cryptographic key pair also associated with the transacting address; determining a risk score for the proposed blockchain transaction based on at least the data included in each of the identified transaction values and the new transaction value; and transmitting the determined risk score.

Abstract: A method for conveyance of payment credentials through a third party data source includes: storing, in a memory of a computing device, at least a first public key and payment credentials associated with a transaction account; generating, by a generation module of the computing device, a machine-readable code encoded with at least the first public key; displaying, by a display device interfaced with the computing device, the generated machine-readable code; generating, by the generation module of the computing device, a key pair comprised of at least a private key and a second public key; encrypting, by an encryption module of the computing device, the payment credentials using the private key; and electronically transmitting, by a transmitting device of the computing device, the encrypted payment credentials to a publicly accessible data source.

Abstract: A method for offline transmission of blockchain details includes: storing, in a computing device, a first private key and a currency amount; receiving a first destination address associated with a blockchain network and a transaction amount; generating a second private key; generating a second destination address associated with the blockchain network using the second private key; generating a blockchain transaction including at least the first destination address, the transaction amount, the second destination address, and a remainder amount based on at least the currency amount and the transaction amount; signing the generated blockchain transaction using the first private key; executing a query to replace the first private key with the second private key, wherein replacement of the first private key includes deletion of the first private key from the computing device; and transmitting the generated blockchain 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: The present disclosure provides a description of exemplary systems and methods for initiating a simultaneous start of block formation in a distributed ledger. The methods and systems may include a processor which may receive a base key. The base key may be generated by the distributed ledger at a time of validation of a first hash for a first block of transactions. The processor may generate a second hash for a second block of transactions to be added to the distributed ledger using the base key as an input for the second hash. The processor may transmit the second hash having the base key as an input to a plurality of second processors.

Abstract: A method for dynamic selection of transactions for inclusion in new blocks added to a blockchain includes: receiving, by a blockchain node in a blockchain network that manages a blockchain, a plurality of proposed transactions; sorting, by the blockchain node, each proposed transaction into one of a plurality of transaction groups; identifying, by the blockchain node, a selection of proposed transactions based on one or more dynamic selection criteria, where the selection of proposed transactions includes one or more proposed transactions from one or more of the plurality of transactions groups based on the one or more dynamic selection criteria; generating, by the blockchain node, a new block including a new block header and the identified selection of proposed transactions; and distributing, by the blockchain node, the generated new block to a plurality of additional nodes included in the blockchain network.

Abstract: Embodiments provide methods and systems for removing temporal biases in classification tasks. Method performed by server system includes accessing a transaction graph associated with a particular time duration and determining a set of local features and aggregate features associated with each node based on labeled data. Method includes generating via a machine learning model, a set of intermediate node representations associated with each of the plurality of nodes based on the set of local features and the set of aggregate features. Method includes generating via a fraud model and a timestep model, a fraud classification loss, and a timestep classification loss based on the set of intermediate node representations. Method includes determining an adversarial loss value based on the fraud classification loss and the timestep classification loss. Method includes determining a set of optimized parameters for the machine learning model based on the adversarial loss value.

Abstract: Embodiments provide methods and systems for identifying a re-routed transaction. Method performed by processor includes retrieving a plurality of transaction windows from a transaction database. Each transaction window includes a transaction declined under a restricted MCC. The method includes accessing a plurality of features associated with each transaction of each transaction window from the transaction database. The method includes predicting an output dataset of a plurality of reconstructed transaction windows based on feeding the input dataset to a trained neural network model. The method includes computing a corresponding reconstruction loss value for each transaction of each transaction window. The method includes comparing the corresponding reconstruction loss value for each transaction with a pre-determined threshold value.

Abstract: Methods, systems and computer program products for selectively providing authenticated data corresponding to a payment card holder at a point-of-sale (POS) terminal are described. The method can include receiving from a terminal device, payment card account information comprising a payment card account identifier and user authentication information. Responsive to the received user authentication information matching one of a plurality of predefined authentication keys associated with the payment card account identifier, a predefined function is selected from among at least first and second predefined functions associated with the payment card account identifier—wherein the selection of the predefined function from among the first and second predefined functions is based on an association between the selected predefined function and an authentication key that has been matched with the received user authentication information.

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: A method for optimized validation of a blockchain transaction through the use of a parallel database and date management includes: receiving a new blockchain transaction that includes unspent transaction outputs; identifying, in a parallel database, a start date for a blockchain wallet involved in the new transaction; identifying a subset of blocks in the blockchain that have been added to the blockchain since the start date; validating the new blockchain transaction by validating the unspent transaction outputs using the subset of blocks; and, if the validation is successful, adding the new transaction to the blockchain, or, if the validation is unsuccessful, notifying the submitter of the failed validation.

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 method for on-behalf ATM processing via blockchain includes: receiving, by a receiver of an automated teller machine (ATM), a transaction identifier; receiving, by an input device interfaced with the ATM, a withdrawal amount; identifying, by the ATM, a blockchain transaction in one of a plurality of blocks comprising a blockchain, wherein the blockchain transaction includes at least the transaction identifier and a current balance; and processing, by the ATM, withdrawal of the withdrawal amount based on the current balance, where processing includes dispensing, by a dispenser interfaced with the ATM, currency equivalent to the withdrawal amount if the withdrawal amount is less than or equal to the current balance or displaying, by a display device interfaced with the ATM, a message indicating an insufficient balance if the withdrawal amount is greater than the current balance.