Title: Using edge computing and blockchain technology to enhance the credit risk early warning system to realise real time prediction and secure transactions

Authors: Ting Zhang; Jie Peng

Addresses: Department of Information Engineering, Jiaozuo University, Jiaozuo, Henan, China ' Department of Information Engineering, Jiaozuo University, Jiaozuo, Henan, China

Abstract: Traditional Credit Risk Early Warning Systems (CREWSs) are confronted with real-time processing, data security, and forecasting accuracy issues. This study proposes an intelligent risk warning system with Edge Computing (EC) for real-time analysis and blockchain for transaction security. The framework employs Fuzzy Comprehensive Evaluation (FCE) and Logistic Regression (LR) to enhance data integrity and forecasting accuracy. Experimental findings reveal latency below 215 ms, improved risk prediction accuracy and improved reliability of early warning signals. Blockchain integration ensures fraud protection and tamper-proof, secure transactions, thus making the system more efficient and scalable for financial institutions. These findings validate that the integration of EC and blockchain improves financial risk management, offering a practical, secure platform for Supply Chain Finance (SCF) and banks.

Keywords: digital finance; edge computing; blockchain; credit risk warning.

DOI: 10.1504/IJIPT.2025.147737

International Journal of Internet Protocol Technology, 2025 Vol.18 No.2, pp.80 - 89

Received: 25 Feb 2025
Accepted: 08 Apr 2025
Published online: 28 Jul 2025 *

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