Discover how our feature engineering pipeline creates strong, consistent features to detect fraudulent withdrawals on the Binance platform 👇
— we recently built an automated feature engineering pipeline that funnels raw data into reusable online features that can be shared across all risk-related models.
Consider this real-world scenario: A crypto exchange — in this case, Binance — is trying to detect fraudulent withdrawals before money leaves the platform. One possible solution is to add a feature to your model that detects time lapsed since the user’s last specific operation . It would look something like this:The number of keys required to calculate and update features in an online feature store is impractical.
Unfortunately, there’s one glaring issue: using such a batch pipeline is highly time-consuming. This makes finishing within one hour unfeasible when you’re the world’s largest crypto exchange dealing with approximately a hundred million users and a TPS limit for writes. It’s imperative that our system flags any suspicious withdrawals as soon as possible. Any added delay, even by a few minutes, means more time for a malicious actor to get away with their crimes.We find produces relatively smaller errors than .
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