Abstract: Embodiments provide methods and systems for determination of creditworthiness of a first merchant. The method performed by a server system includes receiving invoice data of the first merchant from a merchant invoice database. The invoice data includes information of past invoices associated with the first merchant. Additionally, the method includes generating a homogeneous graph based, at least in part, on the information of past invoices. Further, the method includes determining a feature representation of the first merchant based on data features associated with the first merchant in the homogenous graph. Furthermore, the method includes calculating a credit risk score for the first merchant based on the credit risk score model.

Abstract: Methods and systems for determining a representation of a node associated with a graph are disclosed. The method performed by the server system includes accessing a multi-partite sub-graph, including a central node connected with leaf nodes through paths. The method includes extracting a central node feature set associated with the central node and a leaf node feature set associated with each leaf node of the leaf nodes. The method includes determining, by a ML model, a first node representation for the central node based on a distinct relation type, the central node feature set, and the leaf node feature set. The method includes determining a second node representation for the central node based on concatenating individual path feature vectors of each path. The method includes determining a final node representation for the central node based, at least in part, on the first node representation and the second node representation.

Abstract: Methods and systems for mitigating bias from Artificial Intelligence (AI) models during post-processing are disclosed. Method performed by a server system includes accessing first predicted probability score and second predicted probability score for each data sample which is associated with a particular predicted class label having a counterpart class label, a predicted loss category label. Method includes segregating a first set of data samples based on the first predicted probability score and a predefined margin threshold. Method includes segregating a second set of data samples based on the second predicted probability score and a first predefined loss threshold. Method includes segregating a third set of data samples based on the first set of data samples, the second set of data samples, and an overlap condition. Method includes transitioning the predicted class label of each of the third set of data samples to the counterpart class label.

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: Examples include a system that identifies relationships between records. Data corresponding to a plurality of records is accessed, wherein the data is related to different consumer cards. Data fields of the data are compared to identify one or more matches between the plurality of records. A plurality of relationships between the plurality of records is determined based at least on the identified one or more matches. A score is generated based on the determined plurality of relationships. The system produces risk assessment data with enhanced reliability and greater accuracy while reducing system resource usage.

Abstract: A computing system for applying post-authorization modeling tools to a payment transaction for enhancing a decision intelligence (DI) score is disclosed. The computing system is configured to: (i) build a pre-authorization and a post-authorization model to analyze backward velocities and forward velocities; (ii) receive an authorization message for a current transaction; (iv) input into the pre-authorization model current transaction data and the backward velocities for the current transaction to output a DI score; (v) based upon the DI score, authorize the current transaction; (vi) continuously monitor a data feed for a predefined period of time after authorizing the current transaction; (viii) update a data register to include the forward velocities for any post-authorization transactions received during the predefined period of time; (ix) update the DI score for the current transaction by inputting the forward velocities into the post-authorization model to transmit the updated DI score to the merchant.

Abstract: Methods and server systems for determining potential return transactions are described. A method performed by a server system includes receiving an authorization request. The authorization request includes a plurality of ongoing payment transaction attributes. Method includes accessing historical payment transaction datasets. Method includes generating a plurality of features for the cardholder. Method includes generating the first return probability score associated with the ongoing payment transaction. The first return probability score indicates a likelihood of the ongoing payment transaction being associated with a return request. Method includes determining a first return advice code based on the first return probability score and a set of predefined threshold values. Method includes facilitating transmission of an authorization response message includes the return advice code to an acquirer server.

Abstract: Methods and systems for re-training a Machine Learning (ML) model using predicted features from a training dataset are disclosed. A method performed by a server system includes accessing a training feature set and a testing feature set from a database. In response to identifying an inclusion of at least one new feature in the testing feature set, the method includes training a surrogate ML model to predict a value for the new feature based on the testing feature set and determining, by the surrogate ML model, a predicted value for the new feature for each training data sample in a training dataset based on the training feature set. The method further includes generating a new training feature set for each training data sample based on the predicted value and the training feature set. The method includes re-training the ML model based on the new training feature for each data sample.

Abstract: Embodiments provide methods and systems for training a transaction monitoring model based on a multi-component event-aware loss function. The method performed by a server system includes accessing historical transaction data of payment transactions associated with an acquirer server. Method includes determining acquirer features associated with the acquirer server and transaction features associated with an individual payment transaction based on the historical transaction data. Method includes generating, via an embedding layer, a latent representation corresponding to the individual payment transaction. Method includes training a fraud classifier and an acquirer classifier based on the latent representation and the multi-component event-aware loss function. Method includes computing the multi-component event-aware loss function based on execution of the fraud classifier and the acquirer classifier.

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: Embodiments provide methods and systems for determination of creditworthiness of a first merchant. The method performed by a server system includes receiving invoice data of the first merchant from a merchant invoice database. The invoice data includes information of past invoices associated with the first merchant. Additionally, the method includes generating a homogeneous graph based, at least in part, on the information of past invoices. Further, the method includes determining a feature representation of the first merchant based on data features associated with the first merchant in the homogenous graph. Furthermore, the method includes calculating a credit risk score for the first merchant based on the credit risk score model.

Abstract: An example network device includes a primary node and a secondary node. The primary node includes one or more processors implemented in circuitry and configured to receive a message from a collector device requesting to subscribe to statistics of a metrics streaming session; initiate a telemetry session for sending the statistics to the collector device; replicate data of the telemetry session to the secondary node; and send the data of the telemetry session to the collector device. In this manner, in the event of the switchover, the secondary node may act as the primary node and resume the telemetry session. That is, the secondary node, acting as a primary node following the switchover, may receive statistics data from one or more sensors related to the metrics streaming session, and send telemetry session data, representative of the statistics data, to the collector device as part of the telemetry session.