学院and Staff

Crunching the Numbers

经过
Lance Pauker.
发布
2021年11月16日
piggy bank atop a bed of $100 bills

The days of accountants relying on sprawling legal notepads, bulky calculators, and illegible chicken scratch are long gone. Accounting in 2021, like many industries, has been revolutionized by an onslaught of new technologies—ranging from complex Microsoft Excel spreadsheets and formulas to ever-sophisticated software.

Pace Accounting Professor Freddy Huang is well aware of these changes, and is helping to usher in a new era in accounting. Through the Multi-Dimensional Audit Data Selection (MADS) framework—which Huang and his colleagues helped develop in conjunction with the American Institute of Certified Public Accountants (AICPA), the Chartered Professional Accountants (CPA) Canada, and audit experts from the Big Four accounting firms—Huang is helping auditors to, quite literally, crunch the numbers more effectively.

一大组成部分,会计审计;哪一个refers to independent examination of financial information of a company, often conducted by external accounting firm. For large companies, auditing theoretically involves looking at potentially millions of business transactions in a given year.

“传统上,审计是相当劳动密集型的,”黄说。

Because of this reality, auditors have long engaged in a process known as sampling—which involves taking a smaller portion of transactions and analyzing that portion to make determinations about the full quantity of transactions.

“与审计数据分析有关,对于学生发展心态很重要。”

“他们没有看看所有商业交易。他们会选择,让我们说,作为人口的代表性样本100次记录,“黄说。“那么,随着这些记录,他们将应用一些审计程序,并在整个人口上得出结论。”

However, as Huang notes, this method has historically come with a risk, which is commonly known as—you guessed it—sampling risk.

“如果只有审计100条记录,您的结论可能与您解释审核全部人口(数千人或数百万记录)不同。”

输入,数据分析。凭借当今数据分析工具的优势,它已经变得越来越容易且具有成本效益,以衡量具有相对低成本的大量交易,从而理论上解决了抽样风险的问题。然而,出现了一个新问题。即,如果审计员有几周待审计公司,那么分解这种洪水的最有效方法是什么?甚至试图通过审计师的最佳利益筛选数据山脉?

“One reason why accounting firms are hesitant to apply full population tests are the large number of outliers,” notes Huang.

Outliers, are in essence, the red flags—transactions in an audit that based on the audit criteria, raise possible suspicion and merit a further look. Because a full-scale audit can produce thousands of outliers, analyzing all of the outliers in a large audit is often impractical, and possibly conducive the human error.

The key is then, as Huang explains, to apply frameworks to further reduce the number of outliers down a number that is manageable for an auditor, while also ensuring those outliers are the most important transactions for the auditor to look at. This is exactly the problem that Huang’s Multi-Dimensional Audit Data Selection (MADS) Framework is dedicated to solving, and does so by breaking down outliers into three different outputs. In other words, it uses algorithms and equations to even further reduce the number of outliers in an audit to the ones that raise the greatest suspicion.

“我们首先从全面的人口分析开始,”黄。“基于您需要申请的任何测试或过滤器,您将首先获得一大一组异常值。这是一个输出阶段,但不是分析的最终输出。然后,我们对我们的第两个输出进行额外的测试,这可能是先进的机器学习算法,或者基于阶段的输出,审计员可以使用自己的判断开发额外的过滤器或测试。“

最后,在三阶段的输出中,黄和他的同事创造了一个“怀疑评分”,它发展了一种基于特征和/或违规行为的权重交易的方程式,给定的交易与交易的美元金额一起使用。如果两笔交易具有相同数量和违规的类型,但一笔交易以美元金额明显更大,例如,该交易很可能是令人关切的原因。

最终,通过Mads框架 - 以及通过观察如何最好地将机器人流程自动化(RPA)纳入审计(旨在释放会计师,以便专注于需要更高专业的任务判决) - 皇家理解,会计的未来是跨越有强烈的数据分析的趋势。谈到培训下一代会计师的速度时,黄相信,确保学生对当今技术工具的强大指挥方面的复杂任务是成功至关重要的。

“In relation to audit data analytics, it’s important for students to develop a mindset. In the future, when they start to work for different accounting firms and with different clients, they will certainly be dealing with different types of data sets. But with this mindset, they'll always know where to start and what to follow."

速度的更多研究:

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