Machine Learning for Financial Transaction Data: A Recommendation Use Case |
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![]() Dr Mahashweta Das, Visa Research, USA
Short Bio: Date:Aug 27, Meet Link: |
Abstract:
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.
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