Machine Learning in Detecting Fraud: Literature Review

Tri Oktaf Kurniawati, Tarjo Tarjo, Bambang Haryadi, Zuraidah Mohd-Sanusi

Abstract


This research discusses the application of machine learning in detecting fraud, which is a complex issue that requires effective solutions. In recent years, the use of machine learning to detect various forms of fraud, such as transaction fraud and data manipulation, has been the subject of significant research. This research aims to explore  machine learning algorithms that can be used to detect fraud with high accuracy and identify the challenges faced in its implementation. Various algorithms, including decision trees, random forests, and neural networks, have been applied with results showing that random forests often provide the best accuracy. Nonetheless, challenges such as class imbalances and the complexity of cheating activities still need to be addressed. This study uses a descriptive-qualitative method with  a literature review  approach to Sinta's indexed articles from 2019 to 2023. The results show that machine learning can improve the accuracy and efficiency  of fraud detection, as well as contribute to the development of better solutions. Further research is suggested to explore other methods to overcome the challenges.

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References


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