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 glass filled SMC composition having an effective amount of graphene for providing improved Izod impact strength, flexural strength and tensile properties over and SMC composition without graphene as an additive.

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: 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 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 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: A method of injection molding of a class A surface part comprising the steps of first providing a first injections mold cavity for forming a part geometry and a second injection mold cavity for molding of a final class A surface around the part geometry with a second material. Then a fiber reinforced polyamide with an effective amount of an ABS material is compounded and the first injection mold cavity is used to form a part geometry. Thereafter, a colored polyurethane material is injected around the part geometry in the second injection molded cavity for providing a class A surface coating on the part.

Abstract: There is a need for solutions for more effective and efficient natural language processing systems for short texts. This need can be addressed, for example, by a system configured to obtain an initial term-topic correlation data object for a plurality of digital documents, obtain a user-defined term-topic correlation data object for the plurality of digital documents, generate a refined term-topic correlation data object and a refined document-sentiment correlation data object for the plurality of digital documents based at least in part on the initial term-topic correlation data object and the user-defined term-topic correlation data object, obtain a user-defined document-topic correlation data object for the plurality of digital documents, and generate a refined document-topic correlation object for the plurality of digital documents based at least in part on the refined term-topic correlation data object and the user-defined document-topic correlation data object.

Abstract: There is a need for solutions for more effective and efficient natural language processing systems. This need can be addressed, for example, by a system configured to obtain a term correlation data object for a plurality of digital documents; determine, based at least in part on the term correlation data object, a term-topic correlation data object for the plurality of digital documents; determine, based at least in part on the term-topic correlation data object, a document-topic correlation data object for the plurality of digital documents; determine, based at least in part on the term-topic correlation data object, a document-sentiment correlation data object for the plurality of digital documents; generate the topic detection based at least in part on the document-topic correlation object; and generate the sentiment detection based at least in part on the document-sentiment correlation object.

Abstract: There is a need for more effective and efficient natural language processing (NLP) solutions. This need can be addressed by, for example, solutions for performing NLP analysis by utilizing joint topic-sentiment (JST) modeling.

Abstract: A system for reconciliation comprises a determination engine to determine whether data is structured or unstructured, a data structuring engine to structure the data, and a rule extraction engine to determine relations between pairs of values of a first set and a second set of data. The system further comprises a matching engine to generate a confidence score for each pair of the values, a categorization engine to classify the pairs of values into matched pairs and unmatched pairs, a validation engine to validate matching and classification of the pairs based on a user feedback, and a learning engine to store details pertaining to the validation of the matching and the classification over a period of time. The learning engine forwards the details to the rule extraction engine and the categorization engine to determine the relations between subsequent pairs of values and classify the pairs based on the stored details.

Abstract: There is a need for more effective and efficient natural language processing (NLP) solutions. This need can be addressed by, for example, solutions for performing NLP analysis by utilizing joint topic-sentiment (JST) modeling.

Abstract: There is a need for solutions for more effective and efficient natural language processing systems for short texts. This need can be addressed, for example, by a system configured to obtain an initial term-topic correlation data object for a plurality of digital documents, obtain a user-defined term-topic correlation data object for the plurality of digital documents, generate a refined term-topic correlation data object and a refined document-sentiment correlation data object for the plurality of digital documents based at least in part on the initial term-topic correlation data object and the user-defined term-topic correlation data object, obtain a user-defined document-topic correlation data object for the plurality of digital documents, and generate a refined document-topic correlation object for the plurality of digital documents based at least in part on the refined term-topic correlation data object and the user-defined document-topic correlation data object.

Abstract: There is a need for solutions for more effective and efficient natural language processing systems. This need can be addressed, for example, by a system configured to obtain a term correlation data object for a plurality of digital documents; determine, based at least in part on the term correlation data object, a term-topic correlation data object for the plurality of digital documents; determine, based at least in part on the term-topic correlation data object, a document-topic correlation data object for the plurality of digital documents; determine, based at least in part on the term-topic correlation data object, a document-sentiment correlation data object for the plurality of digital documents; generate the topic detection based at least in part on the document-topic correlation object; and generate the sentiment detection based at least in part on the document-sentiment correlation object.

Abstract: A system for reconciliation comprises a determination engine to determine whether data is structured or unstructured, a data structuring engine to structure the data, and a rule extraction engine to determine relations between pairs of values of a first set and a second set of data. The system further comprises a matching engine to generate a confidence score for each pair of the values, a categorization engine to classify the pairs of values into matched pairs and unmatched pairs, a validation engine to validate matching and classification of the pairs based on a user feedback, and a learning engine to store details pertaining to the validation of the matching and the classification over a period of time. The learning engine forwards the details to the rule extraction engine and the categorization engine to determine the relations between subsequent pairs of values and classify the pairs based on the stored details.

Abstract: A heat-activated sound and vibration damping sealant composition is provided. The composition includes dried automotive paint powder and an unsaturated polymer, where the paint powder reacts with the unsaturated polymer when the composition is heated to greater than 200° F. (80° C.). Upon heating, composition expands and increases in tensile strength. The composition may be provided in the form of a tape which is adhered to a substrate such as an automotive or appliance part and then exposed to elevated temperatures such as those encountered in automotive, appliance, or other commercial paint bake processes. Thereafter, the composition provides sound and vibration damping properties.

Abstract: A self-adhesive vibration damping tape for adhering to a substrate is provided. The tape is formed from a vibration damping composition comprising a synthetic rubber polymer, a thermoplastic polymer, a plasticizer, and an organic filler comprised of recycled automotive paint powder. The composition is preferably formed into a tape which includes a release liner on one surface and an aluminum foil facing on the other surface. In use, the tape is adhered on one surface to a substrate such as an automotive or appliance part to provide vibration damping properties as well as seal out the infiltration of air, dust and moisture.

Abstract: A self-adhesive vibration damping tape for adhering to a substrate is provided. The tape is formed from a vibration damping composition comprising a synthetic rubber polymer, a thermoplastic polymer, a plasticizer, and an organic filler comprised of recycled automotive paint powder. The composition is preferably formed into a tape which includes a release liner on one surface and an aluminum foil facing on the other surface. In use, the tape is adhered on one surface to a substrate such as an automotive or appliance part to provide vibration damping properties as well as seal out the infiltration of air, dust and moisture.