Financial transaction data security management based on blockchain technology
Published Online: Mar 24, 2025
Received: Nov 07, 2024
Accepted: Feb 07, 2025
DOI: https://doi.org/10.2478/amns-2025-0780
Keywords
© 2025 Minqing Liu, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Based on blockchain technology, this study constructs a corresponding financial transaction data security management model, and builds an Ethernet abnormal account detection method based on the account portrait technology of Ethernet financial transaction behavior. In order to verify the effectiveness of the model Ada-Katz proposed in this paper, this study conducts comparison experiments between it and the three methods of Netwalk, AddGraph, and TADDY. It also selects the data of 20 enterprises served by Z platform in the past three years, establishes a credit risk evaluation system including 26 qualitative and quantitative indicators, such as enterprise operation data, financial data, blockchain utility, etc., and conducts an in-depth analysis of the role of blockchain technology in reducing the risk of supply chain finance. The experimental results show that compared to the traditional method, the proposed method has a higher accuracy rate and can be used for financial anomaly detection in a variety of datasets. With the empowerment of blockchain technology, the model predicts that the probability of compliance of enterprises is 97.74%, which is much higher than the 58.29% probability of compliance of enterprises under the traditional supply chain finance model. It indicates that blockchain technology can improve the transparency, credibility, and efficiency of supply chain financial transactions, and promote the development of the supply chain financial market. The application of blockchain technology in financial transaction data security management helps prevent and control credit risks in financial transactions, resulting in efficient information sharing and true and reliable data.
