Mastercard International Incorporated

Sonali Syngal

Yatin Katyal

Ankur Saraswat

Debasmita Das

Nishant Pant

Suhas Powar

Karamjit Singh

Bhargav Pandillapalli

Rajesh Kumar Ranjan

Nitish Kumar

Kamal Kant

Garima Arora

Kanishk Goyal

Kshitij Gangwar

Pritam Kumar Nath

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. Further, the method includes transmitting a recommendation message to an issuer server associated with the cardholder based on the account-level risk scores.

Methods and systems for predicting account-level risk scores of cardholders

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.

Detecting money laundering activities using drift in a trained Siamese neural network

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.

System for detection of entities associated with compromised records

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Gurgaon, India

Sonali Syngal