Introduction Financial fraud detection is one of the most critical applications of machine learning in the banking and fintech industries. According to recent estimates, fraudulent transactions cost the global economy billions of dollars annually. In this fifth episode of our Mastering Financial Data Science with Kaggle series, we’ll dive deep into anomaly detection techniques specifically designed to identify fraudulent transactions in highly imbalanced datasets. In previous episodes, we explored feature engineering for time-series data and building credit risk models. Now we’ll leverage those foundations to tackle one of the most challenging problems in financial ML: detecting rare fraudulent events among millions of legitimate transactions. Understanding the Fraud Detection Challenge The Imbalanced Data Problem Credit card fraud detection presents a unique challenge: class imbalance. In real-world datasets, fraudulent transactions typically represent less than 0.1% to 2% of all transactions. This extreme imbalance creates several problems: Standard classification algorithms optimize for overall accuracy, which can achieve 99%+ by simply predicting
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