Inspecting Credit Card Fraud Identification Via Data Mining Classification Methods And Machine Learning Algorithms
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Abstract
Increased global fraud cases and significant losses for both the financial sector and people are brought about by the quick adoption of online-based transactional activity. While credit card fraud is one of the most common and concerning financial industry crimes, internet shoppers are concerned about it more than any other. To investigate the patterns and traits of suspicious and non-suspicious transactions using normalised and anomaly data, data mining techniques were mostly used. Nevertheless, classifiers were utilised in machine learning (ML) techniques to automatically determine which transactions were fraudulent and which were not. Thus, by figuring out the patterns in the data, the combination of data mining and machine learning algorithms was able to distinguish between real and pretend transactions.
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