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Review Papers | Computer Science | India | Volume 13 Issue 2, February 2024 | Popularity: 5.4 / 10

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A Review on Fraud Detection Using Machine Learning and Deep Learning

T. Madhavappa, Bachala Sathyanarayana

Abstract: Financial companies and cardholders both face large financial losses as a result of online fraud. This research study looks for such frauds using information from the general public, information on significant class problems, high false alarm rates, and changes in the manner of fraud. Various machine learning approaches, including the extreme learning method, support vector machine, random forest, XG boost (Extreme Gradient Boosting), decision tree, and logistic regression have been utilized to acquire credit card identity. However due to their low accuracy, fraud losses must be minimized using state-of-the-art deep learning algorithms. Utilizing different datasets for fraud detection, this analysis examines machine learning with deep learning methods. Since identifying credit card fraud is essential, our research makes use of both widely-used machine learning techniques and deep learning.. Many parameters, including specificity, recall, precision, accuracy, misclassification, and F1 score, are used to evaluate their efficiency. According to the results, deep learning and machine learning methods are useful for detecting fraud.

Keywords: Training, Industries, Machine Learning Algorithms, Data Analysis, Costs, Deep Learning Algorithms

Edition: Volume 13 Issue 2, February 2024

Pages: 438 - 443

DOI: https://www.doi.org/10.21275/SR24114141555

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