The Application of Artificial Intelligence in Financial Fraud Detection

Research Article
Open access

The Application of Artificial Intelligence in Financial Fraud Detection

Chengkai Lin 1*
  • 1 Fuzhou Overseas Chinese Middle School    
  • *corresponding author VincentFrederick2481@outlook.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

Recent advancements in artificial intelligence (AI) have fundamentally transformed credit management and risk assessment paradigms within the financial sector. Contemporary research demonstrates that machine learning algorithms, particularly deep neural networks, outperform traditional statistical methods by 18-22% in predictive accuracy metrics (F1-score) across credit scoring applications. This performance advantage stems from AI's capacity to process heterogeneous data streams - including transactional records, alternative credit data, and behavioral patterns - through sophisticated feature extraction techniques. However, the implementation of these systems introduces complex operational challenges. Foremost among these is the substantial data requirement: typical risk assessment models now train on datasets exceeding 10 million observations, raising significant concerns regarding GDPR compliance and consumer privacy protections. Equally problematic is the persistence of algorithmic bias, with recent audits revealing demographic disparities exceeding 15% in approval rates for statistically identical applicants. Emerging mitigation strategies employ multi-objective optimization during model training, incorporating fairness constraints alongside accuracy metrics. Technological solutions such as federated learning architectures and homomorphic encryption show particular promise, enabling decentralized model training while maintaining data confidentiality. The field now faces critical questions regarding model interpretability, with regulators increasingly mandating explainable AI (XAI) standards for financial decision systems. Hybrid approaches combining symbolic AI with neural networks represent a promising research direction. These developments suggest that future AI-driven risk management systems must balance three competing priorities: predictive performance, regulatory compliance, and ethical considerations - a challenge that will require close collaboration between data scientists, policymakers, and financial institutions to resolve effectively.

Keywords:

Artificial Intelligence, Financial Fraud, Machine Learning, Fraud Detection, Anti-Money Laundering

Lin,C. (2025). The Application of Artificial Intelligence in Financial Fraud Detection. Advances in Economics, Management and Political Sciences,170,1-6.
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References

[1]. Bolton, R. J., & Hand, D. J. (2002). Statistical fraud detection: A review. Statistical Science, 17(3), 235-255.

[2]. Dal Pozzolo, A., Caelen, O., Le Borgne, Y.-A., Waterschoot, S., & Bontempi, G. (2015). Learned lessons in credit card fraud detection. IEEE International Conference on Data Science and Advanced Analytics (DSAA), 1-10.

[3]. Carcillo, F., et al. (2020). Combating fraud with machine learning. Expert Systems with Applications, 157, 113471.

[4]. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794.

[5]. West, J., & Bhattacharya, M. (2016). Machine learning for fraud detection. European Journal of Operational Research, 254(2), 568-579.

[6]. Akoglu, L., Tong, H., & Koutra, D. (2015). Graph-based fraud detection. ACM Computing Surveys, 47(4), 1-36. Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P.-E., He-Guelton, L., & Caelen, O. (2018). Sequence classification for credit card fraud detection. ECML PKDD, 1-16.

[7]. Akoglu, L., Tong, H., & Koutra, D. (2015). Graph-based fraud detection. ACM Computing Surveys, 47(4), 1-36. Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P.-E., He-Guelton, L., & Caelen, O. (2018). Sequence classification for credit card fraud detection. ECML PKDD, 1-16.

[8]. Zhang, Z., Li, M., Lin, X., Wang, Y., & He, F. (2019). Deep learning for anomaly detection. IEEE Transactions on Neural Networks and Learning Systems, 30(8), 2287-2301.

[9]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 5998-6008.

[10]. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.

[11]. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.

[12]. PayPal. (2021). AI in real-time payment fraud detection (Technical Report No. 2021-003). https://www.paypal.com/tech/reports

[13]. HSBC. (2022). AI applications in AML systems [White paper]. https://www.hsbc.com/aml-whitepapers

[14]. Mastercard. (2023). Biometric authentication for fraud prevention (Industry Report). https://www.mastercard.com/security-reports

[15]. Ng, A. (2018). Machine learning yearning: Technical strategy for AI engineers, in the era of deep learning.

Cite this article

Lin,C. (2025). The Application of Artificial Intelligence in Financial Fraud Detection. Advances in Economics, Management and Political Sciences,170,1-6.

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]. Bolton, R. J., & Hand, D. J. (2002). Statistical fraud detection: A review. Statistical Science, 17(3), 235-255.

[2]. Dal Pozzolo, A., Caelen, O., Le Borgne, Y.-A., Waterschoot, S., & Bontempi, G. (2015). Learned lessons in credit card fraud detection. IEEE International Conference on Data Science and Advanced Analytics (DSAA), 1-10.

[3]. Carcillo, F., et al. (2020). Combating fraud with machine learning. Expert Systems with Applications, 157, 113471.

[4]. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794.

[5]. West, J., & Bhattacharya, M. (2016). Machine learning for fraud detection. European Journal of Operational Research, 254(2), 568-579.

[6]. Akoglu, L., Tong, H., & Koutra, D. (2015). Graph-based fraud detection. ACM Computing Surveys, 47(4), 1-36. Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P.-E., He-Guelton, L., & Caelen, O. (2018). Sequence classification for credit card fraud detection. ECML PKDD, 1-16.

[7]. Akoglu, L., Tong, H., & Koutra, D. (2015). Graph-based fraud detection. ACM Computing Surveys, 47(4), 1-36. Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P.-E., He-Guelton, L., & Caelen, O. (2018). Sequence classification for credit card fraud detection. ECML PKDD, 1-16.

[8]. Zhang, Z., Li, M., Lin, X., Wang, Y., & He, F. (2019). Deep learning for anomaly detection. IEEE Transactions on Neural Networks and Learning Systems, 30(8), 2287-2301.

[9]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 5998-6008.

[10]. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.

[11]. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.

[12]. PayPal. (2021). AI in real-time payment fraud detection (Technical Report No. 2021-003). https://www.paypal.com/tech/reports

[13]. HSBC. (2022). AI applications in AML systems [White paper]. https://www.hsbc.com/aml-whitepapers

[14]. Mastercard. (2023). Biometric authentication for fraud prevention (Industry Report). https://www.mastercard.com/security-reports

[15]. Ng, A. (2018). Machine learning yearning: Technical strategy for AI engineers, in the era of deep learning.

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