Intuit, Inc.

Uri Lapidot

Liron Hayman

Noah Eyal Altman

Shir Meir Lador

Yaron Moshe

Gabriel Goldman

A method for detecting fraudulent financial transactions in information technology networks involves obtaining a multitude of features associated with a financial transaction conducted over an information technology network by an unknown transaction party. The multitude of features includes clickstream data obtained from the unknown transaction party. The clickstream data is associated with data of the financial transaction being entered by the unknown transaction party. The method further involves obtaining a first fraud indicator using a machine learning classifier operating on the multitude of features, obtaining a second fraud indicator using a rule-based classifier operating on the multitude of features, obtaining a fraud prediction for the financial transaction, using the first fraud indicator and the second fraud indicator, and taking an action, in response to the fraud prediction.

Method and system for detecting fraudulent transactions in information technology networks

A method for increasing computer network security. A first request to attach a first user account for a software program to an external secure account is received. The first request includes a first set of user credentials for accessing the external secure account and a first unverified email address, the external secure account controlled by a second external computer. After receiving the first request, features having metrics is received. The metrics describe at least creation of the first user account and include at least the first unverified email address. A machine learning model processing the features calculates a probability score that the first request is fraudulent. The probability score is compared to a threshold to form a comparison result. A security action is performed with respect to attaching the first user account to the external secure account in response to the comparison result.

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Hod Hasharon, Israel

Uri Lapidot