通过赠款总额近500,000美元Dyson Aiello-Lammens和Erika Crispo通过将数据科学纳入生物学和环境科学领域，帮助提升到新高度的节奏。
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.
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.
However, as Huang notes, this method has historically come with a risk, which is commonly known as—you guessed it—sampling risk.
“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.
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."