Backpropagation Artificial Intelligence with Grey System Theory
Guojun Hong, Wei Xiong
Information Security Education Journal, Vol. 12, No. 2, 2025.
DOI: https://doi.org/10.6025/isej/2025/12/2/41-48
Link: https://www.dline.info/isej/fulltext/v12n2/isejv12n2_1.pdf
Abstract:
The paper proposes an early warning system for corporate financial risk using an improved BP (Backpropagation)
neural network integrated with grey system theory. The author highlights the critical role of
small and medium enterprises (SMEs) in China's economy and their vulnerability to financial distress due to
poor financial management, inadequate internal controls, and external market pressures. To address this, a
three layer Grey BP neural network model is constructed, featuring 16 financial input indicators, 9 hidden
nodes, and 4 output categories representing different risk levels. The model is trained using financial data
from 2000-2004 and validated with 2005-2006 data from 93 firms. Experimental results show high prediction
accuracy, with an R-value exceeding 0.889, particularly for one year ahead (T-1) forecasts compared
to two year ahead (T-2). The study concludes that the Grey BP neural network effectively identifies
emerging financial risks, offering enterprise managers a reliable tool for proactive risk mitigation. Its adaptability,
self learning capabilities, and minimal data requirements make it especially suitable for dynamic,
complex economic environments. The research contributes to the growing body of work applying artificial
intelligence to financial early warning systems and demonstrates the practical value of hybrid models that
combine grey theory with neural networks for enhanced forecasting precision and generalization.