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).

  • Patent number: 11880890
    Abstract: 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: Grant
    Filed: February 8, 2021
    Date of Patent: January 23, 2024
    Assignee: MASTERCARD INTERNATIONAL INCORPORATED
    Inventors: Debasmita Das, Sonali Syngal, Ankur Saraswat, Garima Arora, Nishant Pant, Yatin Katyal
  • Patent number: 11838301
    Abstract: 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: Grant
    Filed: April 28, 2021
    Date of Patent: December 5, 2023
    Assignee: Mastercard International Incorporated
    Inventors: Sonali Syngal, Kanishk Goyal, Suhas Powar, Ankur Saraswat, Debasmita Das, Yatin Katyal
  • Publication number: 20220374684
    Abstract: 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: Application
    Filed: May 17, 2022
    Publication date: November 24, 2022
    Applicant: MASTERCARD INTERNATIONAL INCORPORATED
    Inventors: Sonali Syngal, Debasmita Das, Soumyadeep Ghosh, Yatin Katyal, Kandukuri Karthik, Ankur Saraswat
  • Publication number: 20220358508
    Abstract: 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: Application
    Filed: May 6, 2022
    Publication date: November 10, 2022
    Inventors: 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
  • Publication number: 20220353275
    Abstract: 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: Application
    Filed: April 28, 2021
    Publication date: November 3, 2022
    Inventors: Sonali SYNGAL, Kanishk GOYAL, Suhas POWAR, Ankur SARASWAT, Debasmita DAS, Yatin KATYAL
  • Publication number: 20220253950
    Abstract: 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: Application
    Filed: February 8, 2021
    Publication date: August 11, 2022
    Inventors: Debasmita Das, Sonali Syngal, Ankur Saraswat, Garima Arora, Nishant Pant, Yatin Katyal
  • Publication number: 20210357282
    Abstract: 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: Application
    Filed: May 12, 2021
    Publication date: November 18, 2021
    Applicant: MASTERCARD INTERNATIONAL INCORPORATED
    Inventors: Sangam VERMA, Yatin KATYAL, Ankur SARASWAT, Sonali SYNGAL, Kandukuri KARTHIK