Kpmg Llp

Jpmorgan Chase Bank, N.a.

Rupa Shah

Christopher H L Wicher

Marisa Ferrara Boston

Kumar Rao Krishnagi

Vaibhav Vaibhav

Gaurav Chawla

Various methods, apparatuses/systems, and media for detecting login fraud based on a machine learning model are disclosed. A processor creates a machine learning model configured to be trained to generate a score based on user's biometrics data and a pattern of activity logs data of the user; trains the machine learning model with the user biometrics data and the pattern of activity logs data in real-time; receives user credentials data from the user for login attempt into a system; compares the received user credentials data with the biometrics data and the pattern of activity logs data of the user stored on a database and the machine learning model; and generates the score, in response to comparing, by utilizing the trained machine learning model. The score is a value that the processor compares with a predetermined threshold value to determine in real-time whether the login attempt is fraudulent.

System and method for detecting login fraud based on a machine learning model

Systems and methods disclosed herein are directed to blockchain-based training data management systems and methods for trusted improvements of models. Embodiments provide for the generation of metadata and smart contracts associated with certain data, using a blockchain to store the generated metadata and smart contracts, and curating training data for the improvement of the models utilizing the generated metadata and smart contract stored in the blockchain.

Blockchain-based training data management system and method for trusted model improvements

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Morristown, NJ, United States of America

Rupa Shah