That’s where active learning comes in!

Active learning is like a game of 20 Questions, but instead of trying to guess what object someone has in mind, we’re asking our algorithm questions about the data it’s seeing. And by “questions,” I mean “labels.” Labeling data means telling the machine which transactions are fraudulent and which ones aren’t.

But here’s where things get interesting (or maybe just confusing). Instead of labeling every single transaction, we only label a small subset of them like 10% or so. This is called “streaming active learning,” because the data keeps coming in while our algorithm learns from it. And by “learns” I mean “gets better at detecting fraud.”

So let’s say you make a purchase using your credit card, and our machine learning algorithm sees that transaction. It might ask itself some questions (or rather, its programmers wrote code to do this) like:
– Is the amount of money spent in line with what the customer usually spends?
– Has the location of the purchase changed recently?
– Are there any other unusual patterns in the data?

If the answer is yes to any of these questions, our algorithm might flag that transaction as potentially fraudulent. But if everything looks normal (or at least within a certain range of what’s considered “normal”), then it moves on to the next transaction and keeps learning from new data.

And here’s where things get really cool because we only label a small subset of transactions, our algorithm can learn from them without getting overwhelmed by too much information. This is called “semi-supervised learning,” because we’re using both labeled (or “supervised”) and unlabeled data to train the model.

So basically, our streaming active learning strategy for real-life credit card fraud detection works like this: 1) Collect data in real time from various sources (like banks or merchants), 2) Label a small subset of that data using human experts or other methods, and 3) Feed the labeled data into our machine learning algorithm to learn from it. And by “learn” I mean “get better at detecting fraud.”

And there you have it a simplified explanation of how streaming active learning strategies can be used for real-life credit card fraud detection!