Retail Fraud Round Table with TigerGraph

The focus of this webinar is to identify how Machine Learning, Visualization and new technology can directly increase the accuracy and output of systems and how including the ‘Human in the Loop’ can get you ahead of fraud.

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Retail Fraud is on the rise and it affects all of us, from increased product prices, disputed charges on our credit cards, to billions of dollars in lost revenue. These use cases are becoming more sophisticated and include: credit card fraud, cascaded transactions, loyalty fraud, synthetic or stolen ID, financial crimes, insider threat, and compliance & audit. In addition, seemingly unrelated retail fraud is becoming a conduit for rings of thieves that can attack via transactions or credit cards used for legitimate goods and services. This sophistication and our current world health events have increased the need for real-time analytics and real-time ‘prevent and intervene’ strategies.

User Audience

Services & capabilities

Project Details

Technologies

October 28, 2020

Retail Fraud Round Table with TigerGraph

The focus of this webinar is to identify how Machine Learning, Visualization and new technology can directly increase the accuracy and output of systems and how including the ‘Human in the Loop’ can get you ahead of fraud.

Tags:

Graph

Machine Learning

Fraud

Retail Fraud is on the rise and it affects all of us, from increased product prices, disputed charges on our credit cards, to billions of dollars in lost revenue. These use cases are becoming more sophisticated and include: credit card fraud, cascaded transactions, loyalty fraud, synthetic or stolen ID, financial crimes, insider threat, and compliance & audit. In addition, seemingly unrelated retail fraud is becoming a conduit for rings of thieves that can attack via transactions or credit cards used for legitimate goods and services. This sophistication and our current world health events have increased the need for real-time analytics and real-time ‘prevent and intervene’ strategies.

User Audience

Services

Project Details

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