基于BP神经网络的财务风险识别及监测模型构建

Construction of a Financial Risk Identification and Monitoring Model Based on BP Neural Network

摘要: 在财务信息日益复杂、舞弊手段不断演化的背景下，财务风险的有效识别成为保障资本市场透明度的重要课题。传统方法多依赖人工判断与静态指标，难以应对风险的非线性、隐蔽性与时变性。本文引入BP神经网络技术，构建适用于财务领域的智能监测模型。模型设计从行业特征出发，融合多源数据，强化其动态适应能力与稳健性，并提出涵盖风险特征提取、数据处理、模型训练与监测生成的系统流程。研究致力于提供一套理论与实践相结合的技术方案，为财务风险的前置干预和数字化监管提供可行思路。

Abstract: Against the backdrop of increasingly complex financial information and evolving fraud techniques, the effective identification of financial risks has become a critical issue for ensuring capital market transparency. Traditional methods, which largely rely on manual judgment and static indicators, struggle to cope with the nonlinearity, concealment, and time-varying nature of risks. This paper introduces BP neural network technology to construct an intelligent monitoring model tailored to the financial domain. Grounded in industry characteristics and integrating multi-source data, the model design enhances dynamic adaptability and robustness, while proposing a systematic process that encompasses risk feature extraction, data processing, model training, and monitoring generation. The study aims to provide a technical solution that integrates theory with practice, offering feasible insights for the proactive intervention of financial risks and digital supervision.