A Comparative Study of Machine Learning Methods in Financial Fraud Detection

Zishan Liu ()
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Zishan Liu: Beijing University of Technology

A chapter in Proceedings of the 2024 2nd International Conference on Finance, Trade and Business Management (FTBM 2024), 2024, pp 389-397 from Springer

Abstract: Abstract Financial fraud detection has become increasingly crucial with the rise of digital finance, where fraudulent activities are growing more sophisticated and concealed. This paper provides a comparative analysis of various machine learning methods applied to financial fraud detection, evaluating their effectiveness in different scenarios. Supervised learning techniques such as Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines (SVM) are examined for their performance, model complexity, and interpretability. Unsupervised methods like K-Means and DBSCAN are also considered, focusing on their ability to identify fraud patterns in unstructured data. Deep learning models, including Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Graph Neural Networks (GNN), are evaluated for their capacity to handle large-scale, complex datasets but also face challenges related to data requirements and computational costs. The paper highlights the strengths and limitations of each approach, offering insights into their practical applications and areas for future research in enhancing fraud detection models’ adaptability, interpretability, and efficiency.

Keywords: Financial Fraud Detection; Machine Learning; Supervised Learning; Unsupervised Learning; Deep Learning (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-546-1_44

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DOI: 10.2991/978-94-6463-546-1_44

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