摘 要
随着经济环境的复杂化和企业信用风险管理需求的提升，应收账款坏账准备计提方法的科学性与合理性日益受到关注。本研究以优化现行坏账准备计提方法为核心目标，旨在通过引入更精确的计量模型和评估体系，提高企业财务信息的质量与决策支持能力。研究基于国内外相关理论与实践，选取了具有代表性的行业样本数据，运用统计分析、机器学习算法及情景模拟等多元方法，对传统计提方法的局限性进行了深入剖析，并提出了一种结合动态风险因子的综合计提模型。结果表明，该模型能够显著提升坏账准备计提的准确性与时效性，有效应对不同经济周期下的信用风险变化。此外，研究还验证了模型在跨行业应用中的适应性与稳健性，为企业的个性化计提策略提供了理论依据。本研究的主要创新点在于将大数据技术与传统会计方法相融合，突破了静态计提模式的束缚，同时强调了前瞻性信息的重要性。这一成果不仅为企业信用管理提供了新工具，也为监管机构完善相关政策框架奠定了基础，从而推动会计准则与风险管理实践的协同发展。
关键词：坏账准备计提；动态风险因子；综合计提模型；机器学习算法；信用风险管理
ABSTRACT
With the increasing complexity of the economic environment and the rising demand for corporate credit risk management, the scientificity and rationality of allowance for doubtful accounts estimation methods have garnered significant attention. This study focuses on optimizing current estimation methods by introducing more precise measurement models and evaluation systems to enhance the quality of corporate financial information and its decision-support capabilities. Grounded in both domestic and international theories and practices, representative industry sample data were selected, and diverse methodologies including statistical analysis, machine learning algorithms, and scenario simulations were employed to thoroughly examine the limitations of traditional estimation approaches. A comprehensive provisioning model incorporating dynamic risk factors was proposed. The findings indicate that this model significantly improves the accuracy and timeliness of bad debt provisioning, effectively addressing credit risk fluctuations across different economic cycles. Furthermore, the study validates the model's adaptability and robustness in cross-industry applications, providing a theoretical basis for personalized provisioning strategies in enterprises. The primary innovation of this research lies in integrating big data technology with traditional accounting methods, breaking through the constraints of static provisioning models, and emphasizing the importance of forward-looking information. This achievement not only offers new tools for corporate credit management but also lays a foundation for regulatory authorities to refine relevant policy fr ameworks, thereby promoting the coordinated development of accounting standards and risk management practices.
KEY WORDS： Bad Debt Provision;Dynamic Risk Factor;Comprehensive Provision Model;Machine Learning Algorithm;Credit Risk Management
目 录
摘 要 IABSTRACT II
1 绪论 1
1.1 应收账款坏账准备计提的研究背景 1
1.2 应收账款坏账准备计提的现实意义 1
1.3 国内外研究现状与不足分析 1
1.4 本文研究方法与技术路线 2
2 应收账款坏账准备计提的理论基础 2
2.1 应收账款管理的基本概念 2
2.2 坏账准备计提的会计准则解读 3
2.3 现有计提方法的分类与特点 4
2.4 提取方法优化的理论支撑 4
3 当前坏账准备计提方法的问题分析 5
3.1 现行计提方法的主要缺陷 5
3.2 数据依赖性与信息不对称问题 5
3.3 行业差异对计提方法的影响 6
3.4 风险评估在计提中的局限性 7
4 应收账款坏账准备计提方法的优化策略 7
4.1 基于大数据的动态计提模型构建 7
4.2 考虑行业特性的差异化计提方案 8
4.3 引入机器学习的风险预测机制 8
4.4 优化方法的实际应用案例分析 9
结论 11
致 谢 12
参考文献 13
