Parallel path detection for fraudulent accounts in banks based on graph analysis

Zuxi Chen; ShiFan Zhang; XianLi Zeng; Meng Mei; Xiangyu Luo; Lixiao Zheng

doi:10.7717/peerj-cs.1749

Parallel path detection for fraudulent accounts in banks based on graph analysis

PeerJ Comput Sci. 2023 Dec 14:9:e1749. doi: 10.7717/peerj-cs.1749. eCollection 2023.

Authors

Zuxi Chen^{1

2}, ShiFan Zhang^{1

2}, XianLi Zeng³, Meng Mei⁴, Xiangyu Luo^{1

2}, Lixiao Zheng^{1

2}

Affiliations

¹ Huaqiao University, Fujian, China.
² Xiamen Key Laboratory of Data Security and Blockchain Technology, Xiamen, China.
³ Guilin University of Electronic Technology, Guangxi, China.
⁴ Tongji University, Shanghai, China.

Abstract

This article presents a novel parallel path detection algorithm for identifying suspicious fraudulent accounts in large-scale banking transaction graphs. The proposed algorithm is based on a three-step approach that involves constructing a directed graph, shrinking strongly connected components, and using a parallel depth-first search algorithm to mark potentially fraudulent accounts. The algorithm is designed to fully exploit CPU resources and handle large-scale graphs with exponential growth. The performance of the algorithm is evaluated on various datasets and compared with serial time baselines. The results demonstrate that our approach achieves high performance and scalability on multi-core processors, making it a promising solution for detecting suspicious accounts and preventing money laundering schemes in the banking industry. Overall, our work contributes to the ongoing efforts to combat financial fraud and promote financial stability in the banking sector.

Keywords: Big data in banking; Depth-first search; Fraudulent account detection; Parallel path detection algorithm.

Grants and funding

This work was funded by the Natural Science Foundation of Fujian Province (Grant No. 2021J01320), and the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (Grant No. 2022YFB4300504). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.