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From now until 2023, retailers around the world are careening towards more than $130 billion in losses as a result of card-not-present fraud, according to a report released in January 2020 by Juniper Research. Unfortunately, with more consumers turning to online shopping during the pandemic, that number will likely be much higher. The swift and exponential expansion of e-commerce businesses and alternative payment methods has undoubtedly brought major convenience to consumers, but with such speed, growth, and scale comes an equally large rise in vulnerability—a fact that cybercriminals are all too aware of, and all too ready to exploit.
Modern business leaders are being forced to face a harsh and unfortunate reality: the technology and tactics employed by fraudsters are constantly evolving. To stay a step ahead, online businesses need a fraud prevention solution that can effectively detect multiple types of fraud across a variety of channels—and enable trust and safety teams to scale operations beyond what they can do on their own. For many years, rules-based fraud prevention systems were the preferred method of fraud fighting, but as fraudsters have become more sophisticated, an increasing number of businesses have turned to machine learning (ML).
Unfortunately, not all machine learning solutions are created equally. Look for scale, sophistication, and finally, speed when choosing an ML-based fraud prevention solution.
Scale: It’s built upon vast and varied networks of data
The ability to leverage large volumes of high-quality data is required for a machine learning solution to be as accurate as online businesses need it to be. Simply put, the more data a model has access to, the more precise it can become—ultimately leading to exponentially greater growth and significantly less fraud.
Sophistication: It can deftly analyze and process enormous data sets
Risk signals are often buried within massive streams of data. An effective fraud prevention solution must be able to gather a multitude of signals from a single data point, understand their significance, deliver insights about them, and relay this information to an ensemble of global and custom models for increased accuracy. For example, an email address can be a single data point (e.g., “Have we seen this email address before?"), or it can be a dozen, including analysis of the domain and the username itself. A large data set and sophisticated algorithm are worth virtually nothing if an ML system isn't able to extract the necessary data points from them to help your business stop fraud.
Speed: It surfaces patterns and adjusts in real time
The criminals who commit fraud are always looking for new and innovative ways to circumvent prevention strategies, so an effective platform must be able to identify and respond to changing fraud patterns as they happen. To accurately fight fraud without sacrificing customer experience or business growth, scalability and sophistication need to happen at great speed—something that’s incredibly difficult to do, but that can be accomplished with a world-class machine learning fraud prevention solution in place.
Leverage real machine learning to stay a step ahead of fraud
The volume, velocity, and variety of transactions and fraud data impacting e-commerce businesses will continue to increase at an extremely rapid pace. Legacy fraud prevention systems are incapable of keeping up, leaving the companies that use them vulnerable, and putting them at a major disadvantage against competitors. It takes sophisticated machine learning to accurately analyze the vast streams of data generated from billions of transactions in real time, and give companies the tools they need to stand strong against digital fraud—without compromising what’s good for business.
To discover how Sift can help your organization predict and prevent more fraud, protect and delight customers, and fuel unstoppable growth, visit www.sift.com.