The quick spurt in online transactions has also brought with it a parallel surge in fraud. With digital payments, the scope for fraud remains very high. Credit card fraud, among others, can cause heavy losses to customers and erode the confidence of consumers in Internet transactions. Furthermore, the detection of fraudsters in the online world poses big challenges. For one thing, there is an imbalance in data: fraud transactions are very few compared to genuine transactions. In this research paper, we intend to classify fraud through a supervised machine learning method. SVM is utilized for classification purposes. Based on the available dataset, we performed data analysis to obtain valuable information concerning fraud detection. To solve the problem of data imbalance, we first preprocessed the raw data by randomly choosing some legitimate transactions and normalizing the features. We also used feature selection and scaling methods to improve the accuracy of the model. After training the SVM on the sanitized dataset, we tested the model using performance measures like accuracy, precision, recall, and F1-score. These measures are especially important when working with skewed data. The findings show that the SVM model can detect fraudulent transactions with high precision and a good rate of recall. This shows that it can assist in reducing false alarms while being able to detect most fraud cases. In addition, we touch upon the tradeoff between false negatives and false positives because both pose very significant implications within financial institutions.

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