Kfar Saba, Israel

Zvika Weingarten


Average Co-Inventor Count = 7.0

ph-index = 1

Forward Citations = 8(Granted Patents)


Company Filing History:


Years Active: 2021-2023

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2 patents (USPTO):Explore Patents

Title: Zvika Weingarten: Innovator in Proactive Fraud Detection

Introduction

Zvika Weingarten is a notable inventor based in Kfar Saba, Israel. He has made significant contributions to the field of fraud detection through innovative methods and systems. With a total of two patents to his name, Weingarten's work focuses on enhancing customer service channels by proactively identifying potential fraudsters.

Latest Patents

Weingarten's latest patents include a method and system for proactive fraudster exposure in a customer service channel. This computer-implemented method analyzes call interactions in an interactions database using a Proactive Fraud Exposure (PFE) engine. The method generates a voiceprint for each call interaction and employs machine learning techniques to group these interactions into clusters based on their respective voiceprints. The clusters are then ranked and classified to produce a list of potential fraudsters, which can be reviewed by users for further action. Another patent details a similar approach, emphasizing the collection and analysis of call interactions across multiple service channels to enhance fraud detection capabilities.

Career Highlights

Zvika Weingarten is currently employed at Nice Ltd., a company known for its advanced technology solutions in customer engagement and fraud prevention. His work at Nice Ltd. has positioned him as a key player in the development of innovative fraud detection systems.

Collaborations

Weingarten collaborates with talented individuals such as Levan Michaeli and Itay Harel, contributing to a dynamic team focused on advancing technology in customer service and fraud prevention.

Conclusion

Zvika Weingarten's contributions to proactive fraud detection demonstrate his commitment to innovation in customer service technology. His patents reflect a deep understanding of machine learning and voiceprint analysis, paving the way for more secure customer interactions.

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