A Comparative Study of Machine Learning Methods in Financial Fraud Detection

DOI

10.2991/978-94-6463-546-1_44How to use a DOI?

Keywords

Financial Fraud Detection; Machine Learning; Supervised Learning; Unsupervised Learning; Deep Learning

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.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zishan Liu
PY  - 2024
DA  - 2024/10/27
TI  - A Comparative Study of Machine Learning Methods in Financial Fraud Detection
BT  - Proceedings of the 2024 2nd International Conference on Finance, Trade and Business Management (FTBM 2024)
PB  - Atlantis Press
SP  - 389
EP  - 397
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-546-1_44
DO  - 10.2991/978-94-6463-546-1_44
ID  - Liu2024
ER  -