Changes to the General Ledger (GL) represent a link between transactional business events from Journal Entries and prepared financial statements. Errors in these very large datasets can result in material misstatements or account misbalance. Unfortunately, a plethora of conditions renders traditional statistical and non-statistical sampling less effective. As a full-population examination procedure, Multidimensional Audit Data Sampling (MADS) mitigates these issues. In conjunction with top practitioners, we utilize a design science approach in applying the full-population MADS methodology to a real dataset of GL account balance changes. Issues such as the effectiveness of internal controls, detection of low-frequency high-risk errors, and earnings management concerns are addressed. This paper demonstrates how vital insights can be gained using MADS. More importantly, this approach also highlights the exact portion of the population that is error-free with respect to the auditors' tests.

对总帐 (GL) 的更改代表了日记账分录中的交易业务事件与准备好的财务报表之间的联系。这些非常大的数据集中的错误可能导致重大错报或账户失衡。不幸的是，过多的条件使传统的统计和非统计抽样效率降低。作为一种全面检查程序，多维审计数据抽样 (MADS) 可以缓解这些问题。与顶级从业者一起，我们利用设计科学方法将全种群 MADS 方法应用于 GL 账户余额变化的真实数据集。解决了诸如内部控制的有效性、低频高风险错误的检测以及盈余管理问题等问题。本文展示了如何使用 MADS 获得重要的洞察力。更重要的是，这种方法还突出了在审计员的测试方面没有错误的人口的确切部分。