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Current and Future Applications

當(dāng)前和未來的應(yīng)用

Machine learning is a key subset of artificial intelligence (AI), which originated with the idea that machines could be taught to learn in ways similar to how humans learn. While humans are just beginning to comprehend the dynamic capabilities of machine learning, the concept has been around for decades. The proliferation of data, primarily due to the rise of the Internet and advances in computer processing speed and data storage, has now made machine learning a significant component of modern life. Common examples of machine learning can be found in e-mail spam filters and credit monitoring software, as well as the news feed and targeted advertising functions of technology companies such as Facebook and Google.

機(jī)器學(xué)習(xí)是人工智能（AI）的一個(gè)重要分支，人工智能起源于這樣一個(gè)理念：機(jī)器可以通過類似于人類學(xué)習(xí)的方式來學(xué)習(xí)。當(dāng)人類剛剛開始理解機(jī)器學(xué)習(xí)的動(dòng)態(tài)能力時(shí)，這個(gè)概念已經(jīng)存在了幾十年。數(shù)據(jù)的激增，主要是由于互聯(lián)網(wǎng)的興起以及計(jì)算機(jī)處理速度和數(shù)據(jù)存儲(chǔ)的進(jìn)步，現(xiàn)在已經(jīng)使機(jī)器學(xué)習(xí)成為現(xiàn)代生活的一個(gè)重要組成部分。機(jī)器學(xué)習(xí)的常見例子可以在電子郵件垃圾郵件過濾器和信用監(jiān)控軟件中找到，也可以在Facebook和Google等科技公司的新聞提要（News Feed）和目標(biāo)廣告功能中找到。

Machine learning has the potential to disrupt nearly every industry during the next several years, and the auditing profession is no exception. Rather than relying primarily on representative sampling techniques, machine learning algorithms can provide firms with opportunities to review an entire population for anomalies. When audit teams can work on the entire data population, they can perform their tests in a more directed and intentional manner. In addition, machine learning algorithms can “l(fā)earn” from auditors’ conclusions on specific items and apply the same logic to other items with similar characteristics.

機(jī)器學(xué)習(xí)有可能在未來幾年內(nèi)擾亂幾乎所有行業(yè)，審計(jì)行業(yè)也不例外。機(jī)器學(xué)習(xí)算法不是主要依賴于典型的抽樣技術(shù)，而是可以為企業(yè)提供機(jī)會(huì)來審查整個(gè)群體的異常情況。當(dāng)審計(jì)團(tuán)隊(duì)可以處理整個(gè)數(shù)據(jù)總體時(shí)，他們可以以更直接、更有目的的方式執(zhí)行測(cè)試。此外，機(jī)器學(xué)習(xí)算法可以從審計(jì)人員對(duì)特定項(xiàng)目的結(jié)論中“學(xué)習(xí)”，并將相同的邏輯應(yīng)用到具有類似特征的其他項(xiàng)目中。

What is Machine Learning?

什么是機(jī)器學(xué)習(xí)？

Machine learning is a subset of artificial intelligence that automates analytical model building. Machine learning uses these models to perform data analysis in order to understand patterns and make predictions. The machines are programmed to use an iterative approach to learn from the analyzed data, making the learning automated and continuous; as the machine is exposed to increasing amounts of data, robust patterns are recognized, and the feedback is used to alter actions. Machine learning and traditional statistical analysis are similar in many regards, but different in execution. While statistical analysis is based on probability theory and probability distributions, machine learning is designed to find the combination of mathematical equations that best predict an outcome. Thus, machine learning is well suited for a broad range of problems that involve classification, linear regression, and cluster analysis.

機(jī)器學(xué)習(xí)是人工智能的一個(gè)子集，它使分析模型的建立自動(dòng)化。機(jī)器學(xué)習(xí)使用這些模型來執(zhí)行數(shù)據(jù)分析，以便理解模式并作出預(yù)測(cè)。機(jī)器被編程為使用迭代方法從分析的數(shù)據(jù)中學(xué)習(xí)，實(shí)現(xiàn)學(xué)習(xí)的自動(dòng)化和連續(xù)性。當(dāng)機(jī)器暴露在越來越多的數(shù)據(jù)中時(shí)，強(qiáng)勁的模式被識(shí)別出來，“反饋”被用來改變動(dòng)作。機(jī)器學(xué)習(xí)和傳統(tǒng)的統(tǒng)計(jì)分析在許多方面相似，但在執(zhí)行上有所不同。雖然統(tǒng)計(jì)分析是基于概率論和概率分布，但機(jī)器學(xué)習(xí)的目的是找到#能預(yù)測(cè)結(jié)果的數(shù)學(xué)方程組合。因此，機(jī)器學(xué)習(xí)非常適合于涉及分類、線性回歸和聚類分析的廣泛?jiǎn)栴}。

Supervised learning is used in situations where historical data can be used to predict future outcomes, such as determining which customers are most likely to default on their debt. Unsupervised learning is used where there are no labels on the output variables; the system is not “told” what the assumed answer is, but instead figures out the data patterns on its own. Unsupervised learning contains different techniques that can be used on transactional data (e.g., cluster analysis) and may be beneficial if used as part of the risk assessment process to discover previously unforeseen risks. There is also semi-supervised learning, which contains a combination of labeled and unlabeled output data.

監(jiān)督學(xué)習(xí)被用于“使用歷史數(shù)據(jù)預(yù)測(cè)未來結(jié)果”的情況，例如確定哪些客戶#有可能拖欠債務(wù)。無監(jiān)督學(xué)習(xí)被用于輸出變量上沒有標(biāo)簽的地方；系統(tǒng)不會(huì)“被告知”假設(shè)答案是什么，而是自行計(jì)算出數(shù)據(jù)模式。無監(jiān)督學(xué)習(xí)包含可用于交易數(shù)據(jù)（例如，聚類分析）的各種不同技術(shù)；如果能作為風(fēng)險(xiǎn)評(píng)估過程的一部分來使用，以發(fā)現(xiàn)先前未預(yù)見到的風(fēng)險(xiǎn)，則這些技術(shù)可能是有益的。另外還有半監(jiān)督學(xué)習(xí)，它包含有標(biāo)記和未標(biāo)記的輸出數(shù)據(jù)的組合。

The predictive reliability of machine learning is dependent on the quality of the historical data that has been input. New and unforeseen events may create invalid results if left unidentified or inappropriately weighted. As a result, human biases can play an important role in the use of machine learning. Such biases can affect which data sets are chosen for training the AI, the methods chosen for the process, and the interpretation of the output. Finally, although machine learning has great potential, its models are still currently limited by many factors, including data storage and retrieval, processing power, algorithmic modeling assumptions, and human understanding and judgment.

機(jī)器學(xué)習(xí)的預(yù)測(cè)可靠性取決于輸入的歷史數(shù)據(jù)的質(zhì)量。如果未確定或權(quán)重不適當(dāng)，新的和不可預(yù)見的事件可能會(huì)產(chǎn)生無效的結(jié)果。因此，人的偏好在機(jī)器學(xué)習(xí)的使用中起著重要的作用。這種偏差可能會(huì)影響為訓(xùn)練人工智能而選擇的數(shù)據(jù)集、為過程選擇的方法以及對(duì)輸出的詮釋。#后，雖然機(jī)器學(xué)習(xí)有很大的潛力，但其模型目前仍然受到許多因素的限制，包括數(shù)據(jù)的存儲(chǔ)和檢索、處理能力、算法建模假設(shè)以及人類的理解和判斷。

Current and Potential Future Uses

當(dāng)前和潛在的未來用途

Although there are limitations to the current capabilities of machine learning, it excels at performing repetitive tasks. Because an audit requires a vast amount of data and has a significant number of task-related components, machine learning has the potential to increase both the speed and quality of audits. The machine-based performance of redundant tasks should allow auditors more time for review and analysis, which would give them a greater ability to focus on the areas of greatest risk, as well as a better understanding of the larger picture.

雖然目前機(jī)器學(xué)習(xí)的能力有局限性，但它擅長(zhǎng)于執(zhí)行重復(fù)性任務(wù)。因?yàn)閷徲?jì)需要大量的數(shù)據(jù)，并且有大量與任務(wù)相關(guān)的組件，所以機(jī)器學(xué)習(xí)有可能提高審計(jì)的速度和質(zhì)量。以機(jī)器為基礎(chǔ)的冗余任務(wù)的執(zhí)行應(yīng)該讓審計(jì)人員有更多的時(shí)間進(jìn)行審查和分析，這將使他們更能專注于風(fēng)險(xiǎn)#大的領(lǐng)域，并更好地理解全局。

In the future, machine learning technology could allow CPA firms to detect patterns that currently might otherwise go unnoticed. For example, a restaurant might use historical financial data related to satellite imagery of parking lots, guest count information obtained from point of sale systems, and restaurant employee schedules to demonstrate a strong correlation between high revenues and the number of cars in parking lots during peak hours, high customer guest counts, and high employee wages. By recognizing these patterns, the system could identify locations with revenues inconsistent with vehicle counts, guest counts, or wages. This would allow the auditors to focus on restaurants with inconsistencies rather than selecting restaurants on a random basis.

在未來，機(jī)器學(xué)習(xí)技術(shù)可以讓會(huì)計(jì)師事務(wù)所檢測(cè)到目前可能會(huì)被忽視的模式。例如，餐廳可能會(huì)使用與停車場(chǎng)衛(wèi)星圖像，銷售點(diǎn)系統(tǒng)獲得的客人數(shù)量信息，以及餐廳員工時(shí)間表等相關(guān)的歷史財(cái)務(wù)數(shù)據(jù)，來證明高收益與高峰時(shí)段停車場(chǎng)的汽車數(shù)量、高客流量，以及高員工薪資之間的強(qiáng)相關(guān)性。通過識(shí)別這些模式，系統(tǒng)可以識(shí)別哪些地點(diǎn)的收益與車輛數(shù)量、客人數(shù)量或員工薪資不一致。

Challenges for Auditors

審計(jì)師面臨的挑戰(zhàn)

Audit firms and regulators must overcome several barriers in order for machine learning technologies to reach their full capabilities. Obtaining relevant and useful data (particularly nonfinancial data) from clients and external sources may be difficult. Due to statutory and regulatory limitations, auditors do not typically have access to vast amounts of information from data stores like Google or Facebook. Auditors are also bound by certain ethical and client confidentiality requirements, which may limit their ability to access the quality and quantity of data needed to build their training datasets.

審計(jì)公司和監(jiān)管者必須克服幾個(gè)障礙，以便機(jī)器學(xué)習(xí)技術(shù)發(fā)揮其全部能力。從客戶和外部來源獲取相關(guān)和有用的數(shù)據(jù)（特別是非財(cái)務(wù)數(shù)據(jù)）可能很困難。由于法律法規(guī)的限制，審計(jì)人員通常無法從谷歌或Facebook等數(shù)據(jù)商店獲取大量信息。審計(jì)人員還受到某些道德和客戶保密要求的約束，這可能會(huì)限制他們獲取構(gòu)建培訓(xùn)數(shù)據(jù)集所需數(shù)據(jù)質(zhì)量和數(shù)量的能力。

When relevant and useful data is available for use, auditors must understand and test the internal controls over data integrity and validate the completeness and accuracy of the input data in order to rely on the output. Data security and information integrity will be critically important in determining the reliability of the input data used in machine learning. Auditors will need to work with cybersecurity experts to determine that the client data is secure; otherwise, unauthorized access to financial and nonfinancial data may allow for inappropriate data manipulation that could skew the results.

當(dāng)相關(guān)和有用的數(shù)據(jù)可供使用時(shí)，審計(jì)師必須理解和測(cè)試數(shù)據(jù)完整性的內(nèi)部控制，并驗(yàn)證輸入數(shù)據(jù)的完整性和準(zhǔn)確性，以便信任輸出數(shù)據(jù)。數(shù)據(jù)安全和信息完整性對(duì)于確定機(jī)器學(xué)習(xí)中使用的輸入數(shù)據(jù)的可靠性至關(guān)重要。審計(jì)人員需要與網(wǎng)絡(luò)安全專家合作，以確定客戶數(shù)據(jù)是安全的。否則，未經(jīng)授權(quán)訪問財(cái)務(wù)和非財(cái)務(wù)數(shù)據(jù)可能會(huì)導(dǎo)致不適當(dāng)?shù)臄?shù)據(jù)操作，從而扭曲結(jié)果。

Because of the inherent limitations of machine pattern-finding, auditors will continue to need an understanding of the individual business and its industry, as well as the external business environment and societal forces. For example, user accounts might be the best predictor of revenues for companies such as Facebook and therefore should be given the appropriate weighting in the internal algorithm. Without judgment as to what to specifically look for, the authenticity of accounts and the presence of “bots” may not be detectable by machines and could lead auditors to reach incorrect conclusions. Auditors will need to understand and validate the completeness and accuracy of the input data in order to reach an appropriate conclusion on the output. Furthermore, there will always be potential blind spots when evaluating empirical evidence; therefore, an auditor’s intuition will likely continue to be an important source of knowledge.

由于機(jī)器“模式發(fā)現(xiàn)”的固有局限性，審計(jì)師將繼續(xù)需要了解單個(gè)企業(yè)及其行業(yè)，以及外部商業(yè)環(huán)境和社會(huì)力量。例如，對(duì)于Facebook這樣的公司來說，用戶賬戶可能是收益的#佳預(yù)測(cè)者，因此應(yīng)該在內(nèi)部算法中給予適當(dāng)?shù)臋?quán)重。如果不判斷具體要找什么，賬戶的真實(shí)性和“機(jī)器人程序”的存在可能無法被機(jī)器檢測(cè)到，并可能導(dǎo)致審計(jì)人員得出錯(cuò)誤的結(jié)論。審核員需要理解和驗(yàn)證輸入數(shù)據(jù)的完整性和準(zhǔn)確性，以便對(duì)輸出得出適當(dāng)?shù)慕Y(jié)論。此外，在評(píng)估經(jīng)驗(yàn)證據(jù)時(shí)，總會(huì)有潛在的盲點(diǎn)；因此，審計(jì)師的直覺很可能繼續(xù)是一個(gè)重要的知識(shí)來源。

Future auditors will need to become more versatile and have a solid understanding of information systems, data science, and general business, in addition to an increasingly complex set of accounting and auditing rules and regulations. Whereas in the past audits have had a largely transactional focus, future audits will become increasingly interconnected. Audit firms need to be aware of changing auditor skillsets in order to help manage the disruption risks associated with machine learning technologies.

未來的審計(jì)師需要變得更加多才多藝，除了要了解日益復(fù)雜的一套會(huì)計(jì)和審計(jì)規(guī)則和條例外，還需要對(duì)信息系統(tǒng)、數(shù)據(jù)科學(xué)和一般業(yè)務(wù)有扎實(shí)的了解。過去的審計(jì)主要側(cè)重于交易，而未來的審計(jì)將日益側(cè)重相互關(guān)聯(lián)。審計(jì)公司需要意識(shí)到審計(jì)師技能的變化，以幫助管理與機(jī)器學(xué)習(xí)技術(shù)相關(guān)的中斷風(fēng)險(xiǎn)。

While machine learning technology affords auditors a greater ability to consider internal systematic relationships and external environmental forces, auditors must also exhibit a solid understanding of the input, processing, and output of data from a broader range of sources. In addition, while machine learning technology can provide significantly improved opportunities for auditors to explore their intuition, auditors must change their mode of thinking in order for these insights to be effective. Although it is impossible to foretell exactly how machine learning will ultimately change the audit process, now is the time to begin contemplating its current impact and future implications.

雖然機(jī)器學(xué)習(xí)技術(shù)為審計(jì)人員提供了更大的能力來考慮內(nèi)部系統(tǒng)關(guān)系和外部環(huán)境因素，但審計(jì)師還必須對(duì)來自更廣泛來源的數(shù)據(jù)的輸入、處理和輸出表現(xiàn)出扎實(shí)的理解。此外，雖然機(jī)器學(xué)習(xí)技術(shù)可以顯著改善審計(jì)師探索直覺的機(jī)會(huì)，但審計(jì)師必須改變思維模式，以使這些見解有效。雖然無法準(zhǔn)確預(yù)測(cè)機(jī)器學(xué)習(xí)#終將如何改變審計(jì)過程，但現(xiàn)在是開始考慮其當(dāng)前影響和未來影響的時(shí)候了。

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