![]() They might change the email, change the password. ![]() Say, when a hacker takes over an account, whether it's a bank account or a social media account, they usually follow some predetermined steps. There are also account takeover prevention. Of course, there is fraudulent detection and prevention. ![]() Here are just some of the use cases that I think could benefit a lot from real-time machine learning. This is a pretty incredible statement from the company that created Spark. More people are doing machine learning in production and most cases have to be streamed. However, earlier this year, Databricks CEO did a very interesting interview when he said that there has been explosions of machine learning use cases that don't make sense if they aren't in real-time. Previously, we wouldn't think that there are many use cases for real-time machine learning. Credit card fraudulent transactions prevention is a classic use case of real-time machine learning. We say that the bank is doing real-time machine learning. They predict whether a transaction is fraudulent or not before the transaction goes through. However, the bank was able to prevent this loss because the bank was doing real-time machine learning. If the transaction had gone through, I would have disputed the transaction, and the bank would have refunded me $300 causing the bank a net loss. I responded no, and the bank locked my credit card, preventing further transaction with this credit card. I only found out when the bank texted me, it looks like you're trying to spend $300 at a Safeway in San Jose, is that you? It wasn't me. Chip Huyen: Recently, my credit card was stolen. ![]()
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