Financial transaction fraud costs hundreds of billions in losses worldwide and affects all industries as online sales became the dominant channel with the COVID-19 pandemic. Current fraud detection solutions are struggling to keep up, especially in detecting online fraud.

While bot detection as a goal is nothing new, to the extent that a project like this would have been impossible without drawing on the prior and vital work referenced above, there were a few topics within the problem space that I thought could be further explored. The first was scale and reduction. I wouldn't describe my data science expertise at any level above "hobbyist", and as such, processing power and Twitter API access were both factors I had to keep in mind. I knew I wouldn't be able to replicate the accuracy of models by larger, more established groups, so instead one of the things I set out to investigate was how scalable and accurate of a classification model could be made given these limitations. The second was the type of user data used in classification.

Regardless of the Financial Services sector - Trading, Asset Management, Banking, Wealth Management, and many others - have increased pressure for real-time analytics with complex connected data. The view of complex dependencies, historic data, risk and compliance has never been more important for all executives and financial practitioners alike. The focus of this webinar is to identify how Graph and Machine Learning can directly increase accuracy, avoid fines and compliance issues, and drive revenue. This event is designed as a'Speed Dating' format with focus on key topics for under 15 minutes in order to maximize the insights. During this online meet up, you'll learn from our experts on how Expero and TigerGraph technology can unlock the potential in your organization.

Learn what graph is and how it can create a unified view across digital and physical supply chain processes, yielding more prescriptive supply chain decision making. Use Graph ML to improve demand forecasting and production planning, while reducing inventory and operating costs. Focus areas include: inventory, routing, predictive analytics, device & fleet management, materials, and more.