Keeping users safe from fraud is a priority at Binance Our team of machine learning engineers and data scientists use AI-driven solutions to identify and respond to potential threats in real-time. Read on to learn how and why we use this process 👇
In this article, we’ll explain how our risk AI team uses a streaming pipeline to ensure real-time responses in addition to what goes on behind the scenes.
On the other hand, streaming pipelines process data in real time as they’re collected. This makes them ideal for situations that require a near-instant response; for example, detecting a hacker before they can withdraw funds from a stolen account. One thing you don’t want is a model designed to prevent fraud going stale. The consequences range from the model incorrectly labeling a legitimate transaction as fraudulent or failing to identify a compromised account. Hence, we use a streaming pipeline to ensure fraud prevention models are working with real-time data.Let’s take an example from our ATO model, which we train to identify accounts that criminals have hijacked with malicious intent.
In this case, batch computing over longer time periods, such as daily or hourly intervals, is acceptable despite the higher staleness resulting from waiting for data to arrive in data warehouses and for batch jobs to run periodically.Ultimately, the choice between batch and streaming pipelines should be made based on the specific requirements of the use case and the features in question.
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