Abstract: A system and a method to determine a technology used by a user is disclosed. The system receives user data comprising job titles, skills, and job summaries. Further, technology categorization data is generated based on the user data. The technology categorization data comprises technology category and technology subcategory mapped to a user department and a user division. The system further extracts a keyword and a set of buffer keywords from the user data. Subsequently, the system determines a context of the user data based on the set of buffer keywords of the keyword. The technology used by the user may be determined upon comparing the keyword and the context with a predefined pattern sheet. The system validates the technology with the technology categorization data.

Abstract: Embodiments provide methods and systems for predicting merchant level health intelligence. Method performed by server system includes accessing, from a transaction database, payment transaction data of a merchant for a period of time. The payment transaction data includes information of payment transactions between a plurality of cardholders and the merchant for the period of time. The method includes aggregating merchant transaction attributes based, at least in part, on the payment transaction data and calculating a merchant membership vector based, at least in part, on the merchant transaction attributes by applying a soft-clustering model over the merchant transaction attributes. The method further includes predicting a commercial credit score and a merchant delinquency rate associated with the merchant based, at least in part, on the merchant membership vector. The commercial credit score and the merchant delinquency rate are predicted based, at least in part, on a multi-task learning model.

Abstract: A classifier is trained to classify business supplier relationships using synthetic training data samples. Real training data samples are collected and transformed into sample encodings using an encoder. The real training data samples include feature data associated with health class indicators indicative of relationships between suppliers and service providers. A set of synthetic training data samples is generated from the sample encodings using a generator and discrimination feedback data is generated using a discriminator based on the real training data samples and the synthetic training data samples. The discrimination feedback data is used to train the generator. A classifier model is trained to classify suppliers with health class indicators using the set of synthetic training data samples. The use of the encoder, generator, and discriminator enables the generation of accurate synthetic training data that represents the source distribution of real data which are often partially observed.

Abstract: Aspects of the disclosure provide a computerized method and system that utilizes reference expense reports to build and train one or more neural network learning models that intelligently determine the riskiness of to-be-determined expense reports submitted for reimbursement. In examples, a determined riskiness may inform a reimbursement entity manager when determining whether to approve, reject, and/or flag for further review a to-be-determined expense report. In instances, computerized expense report resolution systems and methods may be further automated in order to omit user interactions with to-be-determined expense reports, such that an intelligent computer determines whether to approve, reject, and/or flag a to-be-determined expense report based on the intelligently determined riskiness of the to-be-determined expense report.

Abstract: A method for optimizing invoice payments according to supplier and buyer controls includes: receiving one or more received data message including invoice data, a buyer identification value, a supplier identification value, and a plurality of buyer optimization priorities, wherein the invoice data is associated with an invoice and includes an invoice amount and due date; identifying a plurality of supplier controls associated with the supplier identification value; identifying one or more buyer preferences associated with the buyer identification value; determining an optimal payment schedule for one or more payment transactions for the invoice based on the invoice data, the buyer optimization priorities, the plurality of supplier controls, and the one or more buyer preferences; transmitting a transmitted data message including the determined optimal payment schedule.

Abstract: Systems and computer-implemented methods are described for modeling card inactivity. For example, hierarchical modeling may be used in which a first level classifier may be trained and validated to predict whether a card will be inactive. For cards predicted to become inactive by the first level classifier, a second level classifier may be trained and validated to predict when the card will become inactive. The first level classifier may include a binary classifier that generates two probabilities that respectively predict that the card will and will not become inactive. The second level classifier may include a multi-class classifier that generates a first probability that the card will become inactive at a first time period (such as one or more months in the future) and a second probability that the card will become inactive at a second time period. The multi-class classifier may generate other probabilities corresponding to other time periods.

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 predicting merchant level health intelligence. Method performed by server system includes accessing, from a transaction database, payment transaction data of a merchant for a period of time. The payment transaction data includes information of payment transactions between a plurality of cardholders and the merchant for the period of time. The method includes aggregating merchant transaction attributes based, at least in part, on the payment transaction data and calculating a merchant membership vector based, at least in part, on the merchant transaction attributes by applying a soft-clustering model over the merchant transaction attributes. The method further includes predicting a commercial credit score and a merchant delinquency rate associated with the merchant based, at least in part, on the merchant membership vector. The commercial credit score and the merchant delinquency rate are predicted based, at least in part, on a multi-task learning model.

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 are described for modeling card inactivity. For example, hierarchical modeling may be used in which a first level classifier may be trained and validated to predict whether a card will be inactive. For cards predicted to become inactive by the first level classifier, a second level classifier may be trained and validated to predict when the card will become inactive. The first level classifier may include a binary classifier that generates two probabilities that respectively predict that the card will and will not become inactive. The second level classifier may include a multi-class classifier that generates a first probability that the card will become inactive at a first time period (such as one or more months in the future) and a second probability that the card will become inactive at a second time period. The multi-class classifier may generate other probabilities corresponding to other time periods.

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: Aspects of the disclosure provide a computerized method and system that utilizes reference expense reports to build and train one or more neural network learning models that intelligently determine the riskiness of to-be-determined expense reports submitted for reimbursement. In examples, a determined riskiness may inform a reimbursement entity manager when determining whether to approve, reject, and/or flag for further review a to-be-determined expense report. In instances, computerized expense report resolution systems and methods may be further automated in order to omit user interactions with to-be-determined expense reports, such that an intelligent computer determines whether to approve, reject, and/or flag a to-be-determined expense report based on the intelligently determined riskiness of the to-be-determined expense report.

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.

Abstract: Embodiments of the present disclosure discloses method and system for determining classification of text. The present disclosure discloses to receive text from plurality of texts and generating a pair of vector representation of the text using trained model parameters of a pair of LSTM units. The trained model parameters are obtained based on training of classification system using plurality of similar pair of texts and plurality of dissimilar pair of texts from the plurality of texts. Further, pair of vector representations are combined using a combiner operator to obtain a combined vector representation. The combiner operator is selected from a plurality of combiner operators based on the training using accuracy of classifier of classification system. The combined vector representation is provided to the classifier for determining classification of text. The present disclosure enhances the performance and generalisation of a classifier in cases of a multi-class classification.

Abstract: Embodiments of the present disclosure discloses method and system for determining classification of text. The present disclosure discloses to receive text from plurality of texts and generating a pair of vector representation of the text using trained model parameters of a pair of LSTM units. The trained model parameters are obtained based on training of classification system using plurality of similar pair of texts and plurality of dissimilar pair of texts from the plurality of texts. Further, pair of vector representations are combined using a combiner operator to obtain a combined vector representation. The combiner operator is selected from a plurality of combiner operators based on the training using accuracy of classifier of classification system. The combined vector representation is provided to the classifier for determining classification of text. The present disclosure enhances the performance and generalisation of a classifier in cases of a multi-class classification.

Abstract: A method of improving accuracy of measurements of at least one motion sensor included in an electronic device, including receiving a candidate measurement associated with the electronic device from the at least one motion sensor; detecting an electronic device state associated with the electronic device, the electronic device state including one from among a static state and a motion state; computing a compensation parameter based on the candidate measurement and the electronic device state; and correcting the candidate measurement based on the computed compensation parameter.

Abstract: A method and system for determining the position of a User Equipment (UE) is described. The method comprises determining signal strength of signals received from a plurality of signal sources in an area in which the UE is located; and estimating position information for a location of the UE in the area based on a position model and the signal strength.

Abstract: A method of improving accuracy of measurements of at least one motion sensor included in an electronic device, including receiving a candidate measurement associated with the electronic device from the at least one motion sensor; detecting an electronic device state associated with the electronic device, the electronic device state including one from among a static state and a motion state; computing a compensation parameter based on the candidate measurement and the electronic device state; and correcting the candidate measurement based on the computed compensation parameter.