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

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.

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.

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.

Credit card fraud detection can be significantly enhanced by using XGBoost, SMOTE, and Threshold Moving. XGBoost, a powerful machine learning algorithm, helps create a robust predictive model by combining decision trees through boosting. Since fraudulent transactions are much fewer than legitimate ones, SMOTE (Synthetic Minority Over-sampling Technique) is applied to address class imbalance by generating synthetic samples for the minority class, ensuring the model learns effectively from both classes. Threshold Moving further improves performance by adjusting the decision threshold to make the model more sensitive to detecting fraud, reducing false negatives. Together, these techniques lead to a more accurate and efficient fraud detection system.