We embrace a fresh perspective to auditing by analyzing a large set of companies as complex financial networks rather than static aggregates of balance sheet data. Preliminary analyses show that network centrality measures within these networks could significantly enhance auditors' insights into financial structures. Utilizing data from over 300 diverse companies, we examine the structure of financial statement networks through bipartite graph analysis, exploring their scale-freeness by comparing degree distributions to power-law and exponential models. Our findings indicate heavy-tailed degree distribution for financial account nodes, networks that grow with the same diameter, and the presence of influential hubs. This study lays the groundwork for future auditing methodologies where baseline network statistics could serve as indicators for anomaly detection, marking a substantial advancement in audit research and network science.

翻译：我们采用一种全新的审计视角，将大量公司视为复杂的金融网络，而非资产负债表数据的静态聚合。初步分析表明，这些网络中的网络中心性度量能够显著增强审计人员对财务结构的洞察力。利用来自300多家不同公司的数据，我们通过二分图分析考察了财务报表网络的结构，并通过将度分布与幂律模型和指数模型进行比较，探索了其无标度特性。研究结果显示，金融账户节点的度分布呈重尾特征，网络的直径在增长过程中保持不变，且存在具有影响力的枢纽节点。本研究为未来的审计方法奠定了基础——网络基线统计指标可作为异常检测的指示器，标志着审计研究与网络科学领域的一次重大进展。