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.
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. This event is designed as a 'Speed Dating' format, focusing on key topics for under 15 minutes in order to maximize the insights. During this online meetup, you'll learn from our experts on how Expero and TigerGraph can unlock the potential in your organization. We will feature unique Expero lightning talks on ML & Business Visualization technology, followed by a short Q&A session.
Key Learning Topics:
- What Are the Key Challenges in Retail Fraud - Illustrate why Visualization, ML & TigerGraph still utilize ‘human in the loop’ for maximum accuracy and productivity
- Methods to reduce false positives by 10% - Review ML & Graph algorithm combination techniques with Graph and other platforms to reduce false positive signals
- Increase accuracy of Fraud identification and intervention - Strengthen and increase accuracy with combinations of ML technique and Graph technologies
- Creation of Fraud based ‘data products’ for Preventive & Predictive analytics - Access to different roles from Fraud Management, investigators to data and analytics teams
- Use of Visualization for ‘Explainable’ ML - Show practical uses and methods for fraud identification, complex dependency and case management
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.
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. This event is designed as a 'Speed Dating' format, focusing on key topics for under 15 minutes in order to maximize the insights. During this online meetup, you'll learn from our experts on how Expero and TigerGraph can unlock the potential in your organization. We will feature unique Expero lightning talks on ML & Business Visualization technology, followed by a short Q&A session.
Key Learning Topics:
- What Are the Key Challenges in Retail Fraud - Illustrate why Visualization, ML & TigerGraph still utilize ‘human in the loop’ for maximum accuracy and productivity
- Methods to reduce false positives by 10% - Review ML & Graph algorithm combination techniques with Graph and other platforms to reduce false positive signals
- Increase accuracy of Fraud identification and intervention - Strengthen and increase accuracy with combinations of ML technique and Graph technologies
- Creation of Fraud based ‘data products’ for Preventive & Predictive analytics - Access to different roles from Fraud Management, investigators to data and analytics teams
- Use of Visualization for ‘Explainable’ ML - Show practical uses and methods for fraud identification, complex dependency and case management
