Evaluation of Risk Assessment and Early Warning in Digital Finance Using Ifso-Bpcnn

Abstract

As early warning system for digital finance hazards increased, this study considers both macro and market factors. By providing a neural network prognosis of undesirable events, early warning systems can aid in averting commercial and economic catastrophes. In several industries, including banking, artificial intelligence (AI) has advanced quickly. This study proposed an innovative model called Improved Fish Swarm Optimization based on Back Propagation Neural Network (IFSO-BPCNN).  An overview of primary alert technology, their evolution along with their application in many contexts—particularly in the financial and economic sectors and how the proposed model used in risk assessment are presented in this work. It looks into how primary alert systems might be incorporated to forecast and identify unfavorable actions, particularly in the corporate, financial as well as economic sectors. By offering a digital finance study of the creation and application of primary alert technology for social and economic growth, the paper adds to the body of current literature. Data from 2012 to 2024 were used to evaluate the system's effectiveness, allowing for dynamic early warning assessments of digital financial threats. The scores having an exactness of 98.7, 97.8 as well as F1 score of 95.7, the IFSO-BPNN model outperformed four comparable models in terms of performance metrics.

Keywords: Risk Assessment, Digital finance, early warning, Artificial intelligence, IFSO-BPNN