Credit Card Fraud Detection using XGBoost, SMOTE, and Threshold Moving

AI & Data Science Data Science of the Day
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SMOTE is a method of dealing with severely unbalanced datasets. See how it helps improve XGBoost performance.
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It’s definitely an important issue that financial institutions face. Thanks for sharing your insights on fraud detection.

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I stumbled upon this post and I just had to comment because credit card fraud is such a pervasive problem these days. It’s great to see people like you using machine learning techniques to tackle this issue head-on. Your approach of using XGBoost, SMOTE, and threshold moving is definitely interesting. I’m not too familiar with the technical aspects of it, but it sounds like a well-thought-out strategy. I am cautious about the choice of payments. And I very often refill my account at vclub. So far there have been no cases of cheating for me. It’s a convenient way to make transactions and has been gaining popularity in recent years.

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I would love Emanuel Scoullos making an extended course on Synthetic Tabular Data Generation Using Tarnsformers.
He made an workshop once and it was fantastic. Unfortunately the topic is extensive and would be awesome roughly 12h of content exploring the topic of Fraud detection using Tabular transformer among other SOTA techiniques.