Exploring the Intricacies of Credit Card Fraud

Research Article
Open access

Exploring the Intricacies of Credit Card Fraud

Zihan Chen 1* , Zhiyuan Zhang 2
  • 1 XiWai International High School    
  • 2 Hong Kong Shue Yan University    
  • *corresponding author qzxx0173522@163.com
AEMPS Vol.170
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-80590-019-1
ISBN (Online): 978-1-80590-020-7

Abstract

This study analyzes 14,446 credit card transaction records from the western US to build a high-precision fraud detection system. Visual methods explore relations between transaction traits and fraud. "grocery_pos" and "shopping_net" are fraud-prone; CA has more frauds, with low-value transactions. People around 50 are at higher risk. Credit card fraud, different from traditional fraud, focuses on shopping. Random forest and logistic regression work well, with random forest at 98.26% accuracy. The project uses integrated models to offer real-time detection. In the dynamic environment of financial crime investigation, the strategies adopted by fraudsters are constantly evolving, which requires continuous improvement of analytical methods. Traditional rule - based fraud detection methods often fail to adapt to evolving fraud patterns, necessitating data-driven approaches to identify complex, non-linear relationships in transaction data. This dual-focus on model comparison and visual-analytic integration distinguishes the study, offering actionable insights for combating dynamic fraud patterns. Future plans include using multidimensional data and graph neural networks for better risk control.

Keywords:

Credit card fraud, Machine learning, Visual analysis, Random Forest Model, Risk prevention and control

Chen,Z.;Zhang,Z. (2025). Exploring the Intricacies of Credit Card Fraud. Advances in Economics, Management and Political Sciences,170,29-37.
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References

[1]. Jiang, S.S., et al., (2023) Credit Card Fraud Detection Based on Unsupervised Attentional Anomaly Detection Network. Systems, 11, 305. Retrieved from www.mdpi.com/2079-8954/11/6/305, https://doi.org/10.3390/systems11060305. Accessed 28 June 2023.

[2]. Alashwali, E., Chandrashekar, R. M., Lanyon, M., Cranor, L. F., (2024) Detection and Impact of Debit/Credit Card Fraud: Victims’ experiences. arXiv (Cornell University). Retrieved from https://doi.org/10.48550/arxiv.2408.08131

[3]. Button, Mark, et al, (2024). Online Frauds: Learning from Victims Why They Fall for These Scams. Australian & New Zealand Journal of Criminology, 47, 391–408, Retrieved from https://doi.org/10.1177/0004865814521224.

[4]. Breskuvienė, D., Gintautas D., (2024) Enhancing Credit Card Fraud Detection: Highly Imbalanced Data Case. Journal of Big Data, 11, 28-32. Retrieved from https://doi.org/10.1186/s40537-024-01059-5.

[5]. Zhang, Y.T., (2023) Root Cause Analysis of Credit Card Fraud. Advances in Economics Management and Political Sciences, 24, 300–310. Retrieved from https://doi.org/10.54254/2754-1169/24/20230454. Accessed 17 Mar. 2025.

[6]. Pundkar, Sumedh N., Mohd Z.i., (2023) Credit Card Fraud Detection Methods: A Review. E3S Web of Conferences, 453, 01015–01015. Retrieved from https://doi.org/10.1051/e3sconf/202345301015. Accessed 31 Jan. 2024.

[7]. Sishany, A., (2022) Mallak Al-Bashrah. Perceptual Exploration of Credit Cards’ Adoption: Customer Perspective. International Journal of Data and Network Science, 4, 407–416. Retrieved from https://doi.org/10.5267/j.ijdns.2020.x.003. Accessed 1 Aug. 2022.

[8]. Kemp, Steven, Nieves Erades P., (2023) Consumer Fraud against Older Adults in Digital Society: Examining Victimization and Its Impact. International Journal of Environmental Research and Public Health, 20, 5404. Retrieved from https://doi.org/10.3390/ijerph20075404.

[9]. Sonkavde, Gaurang, et al, (2023). Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications. International Journal of Financial Studies, 11, 94. Retrieved from www.mdpi.com/2227-7072/11/3/94, https://doi.org/10.3390/ijfs11030094.

[10]. Vynokurova, Olena, et al., (2021) Hybrid Machine Learning System for Solving Fraud Detection Tasks. IEEE Xplore, 1, 20-23. Retrieved from ieeexplore.ieee.org/abstract/document/9204244/.

Cite this article

Chen,Z.;Zhang,Z. (2025). Exploring the Intricacies of Credit Card Fraud. Advances in Economics, Management and Political Sciences,170,29-37.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume title: Proceedings of the 9th International Conference on Economic Management and Green Development

ISBN：978-1-80590-019-1(Print) / 978-1-80590-020-7(Online)
Editor：Florian Marcel Nuţă
Conference website: https://2025.icemgd.org/
Conference date: 26 September 2025
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.170
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Jiang, S.S., et al., (2023) Credit Card Fraud Detection Based on Unsupervised Attentional Anomaly Detection Network. Systems, 11, 305. Retrieved from www.mdpi.com/2079-8954/11/6/305, https://doi.org/10.3390/systems11060305. Accessed 28 June 2023.

[2]. Alashwali, E., Chandrashekar, R. M., Lanyon, M., Cranor, L. F., (2024) Detection and Impact of Debit/Credit Card Fraud: Victims’ experiences. arXiv (Cornell University). Retrieved from https://doi.org/10.48550/arxiv.2408.08131

[3]. Button, Mark, et al, (2024). Online Frauds: Learning from Victims Why They Fall for These Scams. Australian & New Zealand Journal of Criminology, 47, 391–408, Retrieved from https://doi.org/10.1177/0004865814521224.

[4]. Breskuvienė, D., Gintautas D., (2024) Enhancing Credit Card Fraud Detection: Highly Imbalanced Data Case. Journal of Big Data, 11, 28-32. Retrieved from https://doi.org/10.1186/s40537-024-01059-5.

[5]. Zhang, Y.T., (2023) Root Cause Analysis of Credit Card Fraud. Advances in Economics Management and Political Sciences, 24, 300–310. Retrieved from https://doi.org/10.54254/2754-1169/24/20230454. Accessed 17 Mar. 2025.

[6]. Pundkar, Sumedh N., Mohd Z.i., (2023) Credit Card Fraud Detection Methods: A Review. E3S Web of Conferences, 453, 01015–01015. Retrieved from https://doi.org/10.1051/e3sconf/202345301015. Accessed 31 Jan. 2024.

[7]. Sishany, A., (2022) Mallak Al-Bashrah. Perceptual Exploration of Credit Cards’ Adoption: Customer Perspective. International Journal of Data and Network Science, 4, 407–416. Retrieved from https://doi.org/10.5267/j.ijdns.2020.x.003. Accessed 1 Aug. 2022.

[8]. Kemp, Steven, Nieves Erades P., (2023) Consumer Fraud against Older Adults in Digital Society: Examining Victimization and Its Impact. International Journal of Environmental Research and Public Health, 20, 5404. Retrieved from https://doi.org/10.3390/ijerph20075404.

[9]. Sonkavde, Gaurang, et al, (2023). Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications. International Journal of Financial Studies, 11, 94. Retrieved from www.mdpi.com/2227-7072/11/3/94, https://doi.org/10.3390/ijfs11030094.

[10]. Vynokurova, Olena, et al., (2021) Hybrid Machine Learning System for Solving Fraud Detection Tasks. IEEE Xplore, 1, 20-23. Retrieved from ieeexplore.ieee.org/abstract/document/9204244/.

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