Applying AI to secure the payments ecosystem
Who is this presentation for?
- Developers, data scientists, machine learning engineers, AI engineers, architects, and executives
Today’s cybercriminals are well organized and well resourced. Criminals invest in artificial intelligence and computing power the same way we do, helping to grow their market into a $600 billion enterprise-strength industry—or almost one percent of global GDP.
As the network connecting 3.3B cards, 54M merchants, and 15,600 financial institutions, Visa is uniquely positioned to detect data breaches affecting merchants in a timely manner. For merchants in the ecommerce space, where financials can be stolen via malware injected into the merchant websites, at scale, the threat posed by malicious actors continues to mount. Between 2015 and 2017, the percentage of merchant attacks occurring in the ecommerce channel went from 27 percent to 76 percent. And as two-thirds of breaches go undetected for a month or more, reducing the window of exposure is vital. Visa uses AI, big data, and its suite of risk products to monitor the ecommerce universe and detect breaches faster.
Chiranjeet and Shubham provide a high-level overview of the problem formulation, specialized data engineering, and several aspects of the model architecture, which includes traditional machine learning and deep learning, graph analytics, and semisupervised learning techniques such as generative adversarial network (GAN).
- A basic understanding of machine learning and deep learning concepts
What you'll learn
- Learn the application of AI techniques and the deployment of AI-based payment product
Chiranjeet Chetia is a lead data scientist at Visa. With the scale of data Visa observes every day, he’s immersed in finding meaning and value from this data. To this end, he often collaborates with stakeholders across business and technology at Visa to conduct proof of concepts with the end goal of creating data- and AI-powered products or services for Visa. He has 10+ years of experience in the payments domain, most of it in the realm of ecommerce. Previously, he had various stints from managing SMB merchant risk to managing global collections strategy at PayPal. He holds an MS degree in statistics from Virginia Tech, where he also was a provost bioinformatics fellow.
Shubham Agrawal is a lead data scientist at Visa Research, where he has made significant contribution in conceptualizing and building intelligent systems that harness the power of Visa data and enhances Visa’s product offerings. He holds a MS degree in operations research from University of Texas at Austin and has 8+ years of experience in the payment domain. Shubham has a passion for innovation through experimenting with new ideas and concepts that led him to author multiple papers and patents related to this field.
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