Patents by Inventor Sonali SYNGAL
Sonali SYNGAL has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 12217263Abstract: Embodiments provide artificial intelligence-based methods and systems for predicting account-level risk scores associated with cardholders. Method performed by server system includes accessing payment transaction data and cardholder risk data associated with cardholder. The payment transaction data includes transaction variables associated with past payment transactions performed at Point of Interaction (POI) terminals within a particular time window. Method includes generating cardholder profile data based on the transaction variables and the cardholder risk data. Method includes determining account-level risk scores associated with the cardholder based on cardholder profile data. Each account-level risk score of account-level risk scores is determined by a trained machine learning model. The account-level risk scores include a wallet reload risk score, an account reissuance risk score, and a transaction channel risk score.Type: GrantFiled: May 6, 2022Date of Patent: February 4, 2025Assignee: Mastercard International IncorporatedInventors: Bhargav Pandillapalli, Rajesh Kumar Ranjan, Ankur Saraswat, Kshitij Gangwar, Kamal Kant, Sonali Syngal, Suhas Powar, Debasmita Das, Pritam Kumar Nath, Nishant Pant, Yatin Katyal, Nitish Kumar, Karamjit Singh
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Patent number: 11880890Abstract: Siamese neural networks (SNN) are configured to detect differences between financial transactions for multiple financial institutions and transactions for a target party. A first neural network of the SNN tracks transactions (target transactions) for a particular customer or financial institution over time and provides a target output vector. Similarly, a second neural network of the SNN tracks transactions (baseline transactions) for all or a plurality of financial institutions (e.g., within a region) over the same period of time and provides a baseline output vector. The transactions for all or a plurality of financial institutions act as a baseline of transactions against which potentially fraudulent or money laundering activity may be compared. Because Siamese neural networks account for temporal changes based on the baseline of transactions, sudden changes in target transactions will only trigger an alarm if such changes (e.g., deviations or drifts) are relative to a baseline of transactions.Type: GrantFiled: February 8, 2021Date of Patent: January 23, 2024Assignee: MASTERCARD INTERNATIONAL INCORPORATEDInventors: Debasmita Das, Sonali Syngal, Ankur Saraswat, Garima Arora, Nishant Pant, Yatin Katyal
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Patent number: 11838301Abstract: The disclosure herein describes a system and method for predictive identification of breached entities. Identification number and expiration date pairs associated with compromised records in a source file are analyzed to identify a set of candidate entities having records at least partially matching the source file data pairs having events occurring during a selected time period. Probability vectors are calculated for records associated with each identified entity. A divergence value is calculated which represents a distance between probability distribution vectors for each entity and probability distribution vectors for the source file. A predicted breached entity is identified based on the divergence values. The predicted breached entity is notified of the predicted breach. The notification can include an identification of the breached entity, identification of breached records, predicted time of breach, and/or a recommendation to take action to mitigate the predicted breach.Type: GrantFiled: April 28, 2021Date of Patent: December 5, 2023Assignee: Mastercard International IncorporatedInventors: Sonali Syngal, Kanishk Goyal, Suhas Powar, Ankur Saraswat, Debasmita Das, Yatin Katyal
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Publication number: 20220374684Abstract: Embodiments provide electronic methods and systems for improving edge case classifications. The method performed by a server system includes accessing an input sample dataset including first labeled training data associated with a first class, and second labeled training data associated with a second class, from a database. Method includes executing training of a first autoencoder and a second autoencoder based on the first and second labeled training data, respectively. Method includes providing the first and second labeled training data along with unlabeled training data accessed from the database to the first and second autoencoders. Method includes calculating a common loss function based on a combination of a first reconstruction error associated with the first autoencoder and a second reconstruction error associated with the second autoencoder. Method includes fine-tuning the first autoencoder and the second autoencoder based on the common loss function.Type: ApplicationFiled: May 17, 2022Publication date: November 24, 2022Applicant: MASTERCARD INTERNATIONAL INCORPORATEDInventors: Sonali Syngal, Debasmita Das, Soumyadeep Ghosh, Yatin Katyal, Kandukuri Karthik, Ankur Saraswat
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Publication number: 20220358508Abstract: Embodiments provide artificial intelligence-based methods and systems for predicting account-level risk scores associated with cardholders. Method performed by server system includes accessing payment transaction data and cardholder risk data associated with cardholder. The payment transaction data includes transaction variables associated with past payment transactions performed at Point of Interaction (POI) terminals within a particular time window. Method includes generating cardholder profile data based on the transaction variables and the cardholder risk data. Method includes determining account-level risk scores associated with the cardholder based on cardholder profile data. Each account-level risk score of account-level risk scores is determined by a trained machine learning model. The account-level risk scores include a wallet reload risk score, an account reissuance risk score, and a transaction channel risk score.Type: ApplicationFiled: May 6, 2022Publication date: November 10, 2022Inventors: Bhargav Pandillapalli, Rajesh Kumar Ranjan, Ankur Saraswat, Kshitij Gangwar, Kamal Kant, Sonali Syngal, Suhas Powar, Debasmita Das, Pritam Kumar Nath, Nishant Pant, Yatin Katyal, Nitish Kumar, Karamjit Singh
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Publication number: 20220353275Abstract: The disclosure herein describes a system and method for predictive identification of breached entities. Identification number and expiration date pairs associated with compromised records in a source file are analyzed to identify a set of candidate entities having records at least partially matching the source file data pairs having events occurring during a selected time period. Probability vectors are calculated for records associated with each identified entity. A divergence value is calculated which represents a distance between probability distribution vectors for each entity and probability distribution vectors for the source file. A predicted breached entity is identified based on the divergence values. The predicted breached entity is notified of the predicted breach. The notification can include an identification of the breached entity, identification of breached records, predicted time of breach, and/or a recommendation to take action to mitigate the predicted breach.Type: ApplicationFiled: April 28, 2021Publication date: November 3, 2022Inventors: Sonali SYNGAL, Kanishk GOYAL, Suhas POWAR, Ankur SARASWAT, Debasmita DAS, Yatin KATYAL
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Publication number: 20220253950Abstract: Siamese neural networks (SNN) are configured to detect differences between financial transactions for multiple financial institutions and transactions for a target party. A first neural network of the SNN tracks transactions (target transactions) for a particular customer or financial institution over time and provides a target output vector. Similarly, a second neural network of the SNN tracks transactions (baseline transactions) for all or a plurality of financial institutions (e.g., within a region) over the same period of time and provides a baseline output vector. The transactions for all or a plurality of financial institutions act as a baseline of transactions against which potentially fraudulent or money laundering activity may be compared. Because Siamese neural networks account for temporal changes based on the baseline of transactions, sudden changes in target transactions will only trigger an alarm if such changes (e.g., deviations or drifts) are relative to a baseline of transactions.Type: ApplicationFiled: February 8, 2021Publication date: August 11, 2022Inventors: Debasmita Das, Sonali Syngal, Ankur Saraswat, Garima Arora, Nishant Pant, Yatin Katyal
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Publication number: 20210357282Abstract: Embodiments provide methods and systems of predicting server failures. A method may include accessing distinct log clusters representing instructions executed in server, applying first density machine learning model over input vector of distinct log clusters, with length equal to number of distinct log clusters, for obtaining first prediction output, applying first sequential machine learning model over time length sequence of distinct log clusters for obtaining second prediction output, applying second density machine learning model over input vector for obtaining third prediction output, applying second sequential machine learning model over time length sequence of distinct log clusters for obtaining fourth prediction output, aggregating first, second, third and fourth prediction outputs by ensemble model, and predicting likelihood of next log clusters to have anomalous behavior based on the aggregating. First density and first sequential models are trained by normal logs.Type: ApplicationFiled: May 12, 2021Publication date: November 18, 2021Applicant: MASTERCARD INTERNATIONAL INCORPORATEDInventors: Sangam VERMA, Yatin KATYAL, Ankur SARASWAT, Sonali SYNGAL, Kandukuri KARTHIK