摘 要
随着信息技术的迅猛发展，大数据技术在企业管理中的应用日益广泛，尤其在会计应收账款管理领域展现出巨大潜力。本文旨在探讨大数据技术如何优化传统应收账款管理模式，提高企业资金使用效率与风险控制能力。通过文献分析与案例研究相结合的方法，选取多个行业典型企业作为研究对象，深入剖析大数据技术在信用评估、账期预测、逾期预警及催收策略制定等关键环节的应用路径。研究发现，基于大数据的智能分析系统能够显著提升应收账款预测的准确性，增强对客户信用风险的动态识别能力，从而有效降低坏账率并提升资金回笼效率。创新性地引入机器学习算法与非结构化数据处理技术，使应收账款管理从被动应对转向主动预测，构建了更具智能化和适应性的管理体系。本研究不仅拓展了大数据技术在财务领域的应用场景，还为企业实现数字化转型提供了理论支持与实践指导，具有较强的现实意义与推广价值。
关键词：大数据技术；应收账款管理；机器学习算法；信用风险控制；非结构化数据处理
Abstract
With the rapid development of information technology, big data has been increasingly applied in enterprise management, demonstrating significant potential particularly in the field of accounting accounts receivable management. This paper aims to explore how big data technologies can optimize traditional accounts receivable management models, thereby enhancing capital efficiency and risk control capabilities. By integrating literature analysis with case studies, this research selects multiple representative enterprises across different industries as study subjects, conducting an in-depth examination of the application pathways of big data in key processes such as credit evaluation, payment term forecasting, overdue warning, and collection strategy formulation. The findings reveal that intelligent analytics systems based on big data can substantially improve the accuracy of accounts receivable forecasting and strengthen dynamic identification of customer credit risks, thus effectively reducing bad debt ratios and improving cash recovery efficiency. Innovatively introducing machine learning algorithms and unstructured data processing techniques enables a shift from reactive responses to proactive predictions in accounts receivable management, establishing a more intelligent and adaptive management fr amework. This study not only expands the application scenarios of big data technology in the financial domain but also provides theoretical support and practical guidance for enterprises undergoing digital transformation, offering considerable practical significance and potential for broader implementation.
Keywords: Big Data Technology; Accounts Receivable Management; Machine Learning Algorithm; Credit Risk Control; Unstructured Data Processing
目 录
1绪论 1
1.1研究《大数据技术在会计应收账款管理中的应用探讨》的背景和意义 1
1.2《大数据技术在会计应收账款管理中的应用探讨》领域的研究现状 1
1.3本文研究《大数据技术在会计应收账款管理中的应用探讨》的方法 1
2大数据技术对会计应收账款管理的影响机制 2
2.1大数据技术的基本特征与功能分析 2
2.2应收账款管理的核心目标与传统挑战 2
2.3大数据技术如何重构应收账款管理模式 3
2.4技术融合下的管理效率提升路径 3
3大数据驱动下的应收账款风险识别与评估 4
3.1应收账款风险类型及其成因分析 4
3.2基于大数据的客户信用评估模型构建 4
3.3实时风险监测与预警系统设计 5
3.4案例分析：某企业应收账款风险控制实践 5
4大数据支持下的应收账款流程优化与决策支持 6
4.1应收账款全生命周期管理流程梳理 6
4.2大数据在应收流程自动化中的应用 6
4.3数据可视化与管理层决策支持系统 7
4.4提升资金周转效率的关键技术路径 7
结论 8
参考文献 9
致 谢 10
