Full population testing: Applying multidimensional audit data sampling (MADS) to general ledger data auditing

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.     

关键期间数据中心机房设备安全运行的保障措施

在我国金融行业日趋市场化、国际化的今天，商业银行向现代金融企业转型是必由之路，只有经营体制的转变才能从根本上提高企业的核心竞争力：继中国建设银行、中国银行股改上市以来，随着中国工商银行的股改上市步伐日渐临近，各项工作受到越来越广泛的关注：作为拥有资产规模最大、客户数量最多的商业银行，工商银行的稳定与发展同广大人民群众的利益息息相关.[第一段]