Emc IP Holding Company LLC

Alma Zohar

Alex Zaslavsky

Anatoly Gendelev

Eyal Kolman

Kineret Raviv

Shay Amram

Christina Tkachenko

Techniques of risk-based authentication involve adjusting a risk engine used by a recipient entity based on feedback acquired from multiple entities. Along these lines, both a recipient risk engine and one or more donor risk engines perform risk-based authentication for which respective feedback is generated. The feedback indicates whether certain transaction requests predicted to be fraudulent are confirmed to be fraudulent. The recipient risk engine is then adjusted based on the feedback created for itself, the feedback created for any of the donor risk engines, or some combination thereof.

Maintaining a risk model using feedback directed to other risk models

Techniques of identifying fraud detection rule strength involve varying the rendering of a graph from transaction data. Along these lines, a rules server computer provides a general graph from a group of transaction entries defining a group of fraudulent and authentic transactions on an electronic display. A user defines selection criteria that the rules server computer applies to the group of transaction entries to generate a subgroup of transaction entries. From the subgroup of transaction entries, the rules server computer provides a focused graph on the electronic display from the subgroup of transaction entries defining a subgroup of the group of fraudulent and authentic transactions. A ratio of the number of fraudulent transactions to the number of authentic transactions represented in the focused graph identifies the strength of the selection criteria for use in a fraud detection rule.

Rendering transaction data to identify fraud detection rule strength

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Raanana, Israel

Alma Zohar