Director of Product Marketing, Milena Babayev, chats online payment fraud, understanding the science inside the Mastercard Identity Engine with the Director of Field Data Science at Mastercard Identity, Sarah Strano.
Every time I make a purchase online, I often wonder how the retailer knows if I’m a real customer or a fraudster. Turns out that I’m not the only one as, according to Juniper Research, online merchants are predicted to lose $48B to online payment fraud globally this year.
Fraudsters constantly adapt their tactics to find new ways to commit online payment fraud. It’s relatively easy for someone to hack into my account and steal my name and my credit card information. But it’s much harder to mimic my behavior online and “act the part.” That’s where sophisticated fraud detection and fraud prevention comes in.
To understand how businesses predict fraudulent behaviors, I sat down with Sarah Strano, who heads up the Field Data Science team at Mastercard Identity, over virtual coffee to talk about the science of the Identity Network and how its models help customers find fraud, reduce customer friction and offer a better customer experience.
Milena Babayev: First and foremost, for the newbies here, can you tell me a bit about Ekata?
Sarah Strano: We enable businesses across the globe to link any digital interaction to the human behind it. Our goal is to empower businesses to combat fraud, build trust and enable frictionless transactions worldwide.
MB: How do you do that?
SS: All of our APIs and SaaS product suites are powered by the Identity Engine, where sophisticated data science and machine learning combines two technologies, the Identity Graph and the Identity Network, to help reduce customer friction and improve fraud prevention and analysis.
MB: Can you tell me more about the Identity Network?
SS: The Identity Network helps you understand how a digital identity from the Identity Graph is being used in digital transactions using machine learning models, predictions and behavioral patterns. For example, when did we see this email first in our network, how often is this email used in transactions, how many phone numbers were associated with this email in recent transactions, etc?
MB: What sets Identity Network risk models apart?
SS: Combined with our vast global training data of billions of interactions, our advanced machine learning capabilities, especially when it comes to feature engineering and experimentation using our network testbed, allows us to iterate at a rapid rate. Our training data is something I am particularly proud of because it allows customers to leverage our data science resources to detect unique global signals that help identify and detect good vs. bad users. These unique global signals are constantly fine-tuned to improve risk models that are integrated into a customers’ decisioning platform and rules engines.
At the end of the day, it’s important for organizations of all shapes and sizes to utilize risk scoring and identity verification to protect their revenue streams. This is where Mastercard Identity comes in! You need a solution you can trust to work behind the scenes, detecting fraudulent activities before they cause mayhem.
