Analysis of the Impact of Financial Technology on Fraud Behavior

Boyang Chen, Fangfei Liu and Ziye Zhang ()
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Boyang Chen: Southwestern University of Finance and Economics, School of Accounting
Fangfei Liu: Southwestern University of Finance and Economics, School of Finance
Ziye Zhang: Shanghai University, SILC BUSINESS SCHOOL

A chapter in Proceedings of the 2025 3rd International Academic Conference on Management Innovation and Economic Development (MIED 2025), 2025, pp 164-172 from Springer

Abstract: Abstract With the rapid development of financial technology (FinTech), the financial system has enhanced transaction efficiency and convenience, while also bringing more complex risk challenges, especially the continuous evolution and increasing concealment of financial fraud methods. Traditional manual review and risk control methods are gradually revealing their limitations in dealing with these new types of fraud, failing to meet the demands of the modern financial environment. This study, through a comprehensive review of relevant literature, examines the application of technologies such as artificial intelligence (AI), machine learning (ML), and blockchain in financial fraud detection. It is found that these technologies significantly improve the accuracy, efficiency, and model interpretability of fraud detection, and have a positive impact on the security of enterprises and individuals. However, the research also indicates that these technologies still face challenges in data quality, real-time performance, and adaptability. Moreover, although the development of FinTech has provided fraudsters with new means of committing crimes, the ability to combat fraud can be effectively enhanced through technological innovation and institutional improvement. In the future, it is necessary to strengthen international cooperation and information sharing to build a global anti-fraud network, ensuring the healthy development of FinTech.

Keywords: Financial Technology; Fraud Behavior; Artificial Intelligence; Machine Learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.2991/978-94-6463-835-6_19

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