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An Analysis of the Most Used Machine Learning Algorithms for Online Fraud Detection

Author

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  • Elena-Adriana MINASTIREANU
  • Gabriela MESNITA

Abstract

Today illegal activities regarding online financial transactions have become increasingly complex and borderless, resulting in huge financial losses for both sides, customers and organizations. Many techniques have been proposed to fraud prevention and detection in the online environment. However, all of these techniques besides having the same goal of identifying and combating fraudulent online transactions, they come with their own characteristics, advantages and disadvantages. In this context, this paper reviews the existing research done in fraud detection with the aim of identifying algorithms used and analyze each of these algorithms based on certain criteria. To analyze the research studies in the field of fraud detection, the systematic quantitative literature review methodology was applied. Based on the most called machine-learning algorithms in scientific articles and their characteristics, a hierarchical typology is made. Therefore, our paper highlights, in a new way, the most suitable techniques for detecting fraud by combining three selection criteria: accuracy, coverage and costs.

Suggested Citation

  • Elena-Adriana MINASTIREANU & Gabriela MESNITA, 2019. "An Analysis of the Most Used Machine Learning Algorithms for Online Fraud Detection," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 23(1), pages 5-16.
    • Handle: RePEc:aes:infoec:v:23:y:2019:i:1:p:5-16
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    References listed on IDEAS

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    1. Catalina Lucia COCIANU & Hakob GRIGORYAN, 2015. "An Artificial Neural Network for Data Forecasting Purposes," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 19(2), pages 34-45.
    2. Luminita STATE & Catalina COCIANU & Cristian USCATU & Marinela MIRCEA, 2013. "Extensions of the SVM Method to the Non-Linearly Separable Data," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 17(2), pages 173-182.
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    Cited by:

    1. Hamid Bekamiri & Seyedeh Fatemeh Ghasempour Ganji & Biagio Simonetti & Seyed Amin Hosseini Seno, 2021. "A New Model to Identify the Reliability and Trust of Internet Banking Users Using Fuzzy Theory and Data-Mining," Mathematics, MDPI, vol. 9(9), pages 1-16, April.

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