Join us for an architectural dive into how financial services, banking and retail are using graph-enhanced machine learning to thwart fraud. Fraudsters are becoming increasingly sophisticated, organized and adaptive; traditional, rule-based solutions are not broad or nimble enough to deal with this reality. This session will cover several demonstrations and real-world technical examples including preventing credit card fraud, identifying money laundering and reducing false positives.
- Reference Architecture – See a framework for building intelligent applications that can sense and respond to increasingly complex fraud attempts.
- Boosting machine learning – Find out how you can combine machine learning with graph technology to improve predictive lift
- Graph algorithms – Hear an overview of algorithms to get started with and uses for fraud analysis
Brian’s focus has always been in the application of technology to concrete business problems. He started his career with "fingers on keyboards", writing code for government agencies, financial markets and commercial software companies. Now he enjoys consulting for clients to help them apply technology and processes to make their businesses better. He has a B.S. in Computer Science from Vanderbilt University and an M.S. in Computer Science from DePaul University.
Nav Mathur is responsible for solutions development and Go To Market activities for Solutions. Nav is seasoned in business development and brings over 20+ years of experience in consulting focused on Strategy, Planning and Transformational Business & IT Solutions. Previously, Nav was at Wipro heading sales for the Enterprise Architecture practice across North America. He was also in a similar role at TCS previously. Nav also had a long stint at Cambridge Technology Partners starting as a developer and eventually leading the North America Architecture Practice. Nav has a Masters degree in Software Engineering from Boston University and a Bachelor degree in Computer Engineering. He has also completed an executive management course from Babson College.
Dr. G loves data. His favorite part of work is daydreaming up innovative solutions to quantifiable problems and planning an implementation strategy. Building intelligent systems is his passion whether it’s automated derivatives trading bots, adaptive image processing algorithms, or autonomous musical composers. Whether deep learning is the optimal solution or not, helping customers succeed through solving their analytics problems is where Graham finds the most satisfaction.