Use Case Demo: Machine Learning Based Fraud Detection

Fraudulent actors are always looking for new ways to subvert legitimate transaction systems; traditional rules-based approaches are no longer sufficient (or efficient enough) to combat fraud. In this webinar, we’ll discuss best practices and examples on how machine learning can improve fraud detection capabilities. Data Scientists, Quants, and Analysts in the banking sector can benefit from expert best practices on tackling fraud detection. We’ll include a brief use case demo to concretely ground the discussion and discuss real-time considerations for detection. Kevin’s financial expertise and Will’s diverse implementation experience make them the perfect team to explore the host of factors that go into a machine learning fraud detection model. We’ll host a Q&A after the demo, so make sure to join us live. Kevin Graham is a Dataiku Account Executive with nearly 10 years of experience across financial services and technology. He started his career in Sales & Trading before moving into a technology sales capacity at Oracle and Merlon Intelligence. At Merlon, Kevin focused on how AI and machine learning could help solve complex challenges within financial crime compliance. He currently is part of a financial services focused sales team across the Eastern United States and Canada at Dataiku. Will Nowak is a solutions architect at Dataiku, where he helps Fortune 500 companies improve data science operations. Previously, he engineered machine learning models for several Y Combinator startups, learning the pitfalls and challenges to productionalizing machine learning. Will holds a bachelor’s in Math and Economics from Northwestern University and a Master’s in Organizational Leadership from Columbia University.