Machine Learning for Financial Transaction Data: A Recommendation Use Case

Dr Mahashweta Das, Visa Research, USA

Short Bio:
Mahashweta Das is a Sr. Staff Research Scientist at Visa Research where she works on challenging real problems at the crossroads of tech and payment industry. At Visa Research, she is leading research and development efforts in Recommendation. She is also employed as a Part-Time Lecturer at Northeastern University, Silicon Valley campus. Previously, she worked as a Research Scientist at Hewlett Packard Labs where she designed and developed big data analytics solutions for HPE’s ‘The Machine’. She has held summer positions at Yahoo! Research, Technicolor Research Lab, and IBM Research. Mahashweta received her Ph.D. in Computer Science from the University of Texas at Arlington in 2013. Her research interests include machine learning, deep learning, data mining, and algorithms. She has published over fifteen refereed articles at premier international research conferences and journals and regularly serves on the program committee of these conferences. Her PhD dissertation received Honorable Mention at ACM SIGKDD 2014 Doctoral Dissertation Award.

Date:Aug 27,
Time: 7:30 PM IST

Meet Link:


Visa is a leading global payments technology company that provides consumers, merchants, businesses, financial institutions, and governments with the best way to pay and be paid. Visa handles more than ten trillion dollars of payments annually, thereby accruing humongous amounts of transaction data that reflects how consumers around the world spend money. This gold mine of data motivates us to employ advanced machine learning (ML) and artificial intelligence (AI) techniques to solve critical real problems for the company such as fraud detection. We harness the power of AI and deep learning to personalize consumer and merchant experiences. We also leverage AI to design and develop a range of behavioural biometric technologies, cross-border payment solutions, etc. In this talk, we focus on recommendation. How can we build data-driven AI solutions that learn consumers’ personalized preferences and recommend restaurants, tourist spots, travel itineraries, hotels, etc.? Specifically, we consider the problem of personalized restaurant recommendation using financial transaction data with little-to-no domain knowledge. We discuss the challenges associated with building such a recommendation engine and how we address some of them. We present a novel context-aware recommendation solution and validate its effectiveness over the related state-of-the-art.