Anurag Tangri is a Lead Data Scientist at Visa. He was a Big Data Engineer for almost a decade but developed a great interest in AI domain along the way and ended up joining Visa Research to continue his passion for AI. He loves applying AI techniques to his day-to-day job to create innovative products using VISA data. In his current role, he collaborates closely with Business and Product teams at VISA to create data and AI-powered products. Prior to VISA, he has worked at companies like Yahoo and Groupon and has 7+ years of experience in Payments domain.
AI is becoming omni-present and is influencing the Payments industry in a big way. At VISA, AI driven-products are changing the way we do our business. Merchants are one of the core entities in any payments network. Millions of merchants are observed to be added to the payments ecosystem every month. Some of these are indeed new businesses but a significant fraction, are merchants that have created a new identity with changed attributes. For Visa, it is essential & highly beneficial to have an oversight of how a merchant is in our network. Being able to do so on a continuous basis leads itself to several use-cases as risk-mitigation, loyalty programs etc.
At VISA, we're using AI, big data tools and our suite of internal products to detect merchant changes. Our AI model currently leverages the scale and depth of VISA data along with a suite of AI techniques to track a merchant with very high accuracy. Accuracy and timeliness are of utter importance because not knowing the merchant and its whereabouts can lead to incorrect merchant offers and delays in merchant queries. In this talk, we will share details about our AI model that looks at merchant patterns over regular intervals. We will discuss the specialized data engineering used and several aspects of the model architecture that includes the traditional Machine Learning and consumer behavior pattern based approaches, continuing onto unsupervised learning techniques using near-duplicate algorithms like Locality Sensitivity Hashing from Spark ML.