What is Machine Learning?
Artificial intelligence research focuses on designing computer systems to mimic human intelligence by having a computer program make decisions or take actions based on the information provided. Artificial intelligence includes expert systems and machine learning; machine learning includes a subfield known as deep learning, which uses multiple layers, as opposed to “shallow learning.”
Research in artificial intelligence goes as far back as the 1950s, but was limited by computing hardware. In 1997, IBM’s Deep Blue demonstrated the possibilities of machine learning by defeating the world chess champion (Bernard Marr, “A Short History of Machine Learning—Every Manager Should Read,” Forbes, 2016, https://bit.ly/3f6UKWk).
Although increased data storage capacity and computer processing power were helpful in advancing machine learning, two important developments in computers from the 1990s into the 2000s that accelerated the development of machine learning were the availability of large high-quality data and the development of parallel graphic processing units (GPU). Because machine learning requires large data sets in order to train the learning algorithms, the vast quantity of high-quality publicly available data allowed researchers to refine the machine learning algorithms. Parallel processing units were initially focused on meeting the demands of the graphic-intensive games, but the developments in parallel processing allowed significant enhancement of power for machine learning. Research and development in machine learning has rapidly accelerated in the past 15 years.
How Does Machine Learning Work?
Computer programs were initially created to give computers instructions to follow in solving a problem. This process, known as top-down programming, was used from the early days of computers until the 1990s when object-oriented programming (OOP) was created. OOP changed programming from isolated instructions to the computer to manipulate data, to treating the programs and the data that it manipulates into a defined object. This paradigm shift resulted in the rapid development of graphically based programs that were much easier to maintain because the programs were based on a set of self-contained objects that interacted with each other. OOP worked very well for typical programs such as word processing or spreadsheets.
The goal in machine learning is to write an algorithm that can be trained using test data to look for specific patterns.
The traditional methods of programming did not work very well for machine learning because they require a programmer to give the computer precise instructions for encountering both expected and unexpected possibilities. In machine learning, the expectation is that the algorithm will learn from the data provided, in a manner similar to how humans learn from data.
The goal in machine learning is to write an algorithm that can be trained using test data to look for specific patterns. For example, if a machine-learning algorithm that could look at pictures of animals and identify those that contain cats is desired, one starts by identifying the general characteristics of a cat (four-legged, furry animal with a tail) and providing it with a sample set of pictures of animals. Initially the algorithm would be able to identify animals that do not contain the common characteristics such as snakes (no fur, no legs), birds (no fur, not four-legged), and fish (no fur, no legs). But it would need to learn that there are other characteristics (distinctive sounds, claws, body shape) to differentiate it from other four-legged furry animals with tails.
There are several different mathematical solutions used for machine learning. One technique is called the K-nearest neighbor (K-NN) algorithm. This is one of the simplest classification algorithms used in machine learning. The basic idea is that the programmer determines the value for K, which would be the number of characteristics they would want the machine learning programming to use (Adi Bronshtein, “A Quick Introduction to K-Nearest Neighbors Algorithm,” Noteworthy–The Journal Blog, 2017, https://blog.usejournal.com/a-quick-introduction-to-k-nearest-neighbors-algorithm-62214cea29c7). Given the example of four-legged, furry, tail, and claws, this would mean four characteristics are used to determine whether a specific image identifies a cat; the more characteristics used, the better the program is able to eliminate animals that are not cats. A greater number of characteristics, however, could also mean that a cat may be misidentified. In machine learning, it is critical to use the optimal algorithm and then apply a large set of test data before training the algorithm to solve a problem or identify something based on characteristics.
Accounting processes such as expense reports, accounts payable, and risk assessment may be easily automated using machine learning.
Expected Innovations in Machine Learning
Although machine learning is more prevalent now than 10 years ago, it is expected that the impact it will have on everyday life and business operations will increase dramatically in the near future. Entefy, an artificial intelligence software company, identifies several industries that will be significantly impacted by machine learning. In the automotive industry, machine learning can be used to improve human driving skills to improve safety, while autonomous vehicles could eliminate accidents caused by carelessness or distractions. Manufacturing companies can use machine learning to reduce expenses, improve workflow, and increase productivity. In the consumer goods industry, machine learning helps analyze customers’ past purchase patterns, which can determine the development of new products, gpromotion strategies, and pricing to reduce expenses associated with unsold products. The hospitality industry has already started using machine learning to tailor the booking process based on the purchasing behavior of travelers (“The Machine Learning Revolution: ML Innovation in 8 Industries,” Entefy, 2018, https://bit.ly/2XOtshK).
The impact on businesses and the accounting profession will undoubtedly be quite dramatic in the near future. The major public accounting firms are focused on providing their customers with the expertise needed to deploy machine learning algorithms in businesses to speed up and improve business decisions while lowering costs. In May 2018, PricewaterhouseCoopers announced a joint venture with eBravia, a contract analytics software company, to develop machine learning algorithms for contract analysis (“PwC Announces Legal AI partnership with eBrevia for Doc Review,” Artificial Lawyer, 2018, https://bit.ly/2APAKZr). Those algorithms could be used to review documents related to lease accounting and revenue recognition standards as well as other business activities, such as mergers and acquisitions, financings, and divestitures. Deloitte has advised retailers on how to enhance customer experience by using machine learning to target product and services based on past buying patterns (“MapR and Deloitte Announce Strategic Alliance to Modernize Analytics and Speed AI Success,” BusinessWire, 2018, https://bwnews.pr/2zjSMmh). While the major public accounting firms may have the financial resources to invest in machine learning, small public accounting firms have the agility to use pre-built machine learning algorithms to develop expertise through implementations at a smaller scale.
Machine Learning’s Impact on Public Accounting
Many routine accounting processes will be handled by machine learning algorithms in the near future. Accounting processes such as expense reports, accounts payable, and risk assessment may be easily automated using machine learning. The jobs requiring the processing of documents have already started disappearing with the advent of document scanners, optical character recognition, and software to match source documents. As an example, machine learning algorithms can receive an invoice, match it to a purchase order, determine the expense account to charge, and place it in a pool of payments to release; a human worker can review the documents and release them for payment. While accounting jobs in businesses will change in the near future, the question of how the public accounting profession will evolve remains.
Given that companies will deploy machine learning in their operations to improve accuracy and reduce costs, the advisory services of public accounting firms could dramatically change. It is estimated that 80% of the time spent in advisories services is processing information about a company’s operations (Maria Jesus, “How Machine Learning is Disrupting the Accounting Industry,” BigML Blog, 2018, https://bit.ly/37hIvU5). Much of this information processing could be handled by machine learning algorithms, meaning that most of the time billed to clients would focus on valued added services that analyze the information produced by machine learning.
MACHINE LEARNING’S IMPACT ON PUBLIC ACCOUNTING
- Reduced audit staff
- Machine learning expertise
- Internal control emphasis
- Improved fraud detection
- Reduced time reviewing material
- Machine learning expertise
- New tax planning opportunities
- Improved value-added services
- Reduced time processing information
- Machine learning expertise
- Improved accuracy and reduced costs
- Emphasis on value-added service
INDUSTRIES AFFECTED BY MACHINE LEARNING
- Consumer Goods and Services
- Healthcare and Life Sciences
- Hospitality and Travel
- Media and Entertainment
The impact of machine learning will most likely be less pervasive in tax preparation services, due to the need for specialized advice and technical research in the context of complex corporate and individual planning issues. Global companies face large and increasingly complicated tax compliance requirements; allocating revenue and expenses to various taxing jurisdictions requires significant data processing and analysis. Machine learning can help tax professionals keep up with relevant tax law changes. Creating algorithms to extract relevant planning information from vast amounts of data is ideal for machine learning. It is hard to accomplish effective tax planning without the relevant and important facts; machine learning can make the fact gathering and analysis function much more efficient and effective. In addition, taxing authorities are exploring the use of machine learning to increase transparency and audit efficiency. The IRS has already begun to develop machine learning algorithms to identify patterns that are associated with tax evasion and fraud. Former IRS agent Michael Sullivan indicated that the public “should be aware that the IRS has begun using a new audit method, the ‘Machine Learning Tax Audit’ (“Beware of the New ‘Machine Learning’ IRS Tax Audit Advises Former IRS Manager,” PRWeb, 2019, https://bit.ly/2AQWeoO). As tax laws continue to grow more complex and the IRS’s processes for identifying a taxpayer for an audit become more sophisticated, machine learning may allow tax accountants to better predict deductions that will be disputed by the IRS and identify the regulations that allow for those deductions.
Auditing is an area that will significantly change in the future. Many have predicted that the automation of analyzing a company’s financial statements and source documents will result in smaller audit staffs. Auditing standards, however, require an auditor to understand the systems and processes related to the preparation of the financial statements—meaning that the technical expertise required of auditors to understand the machine learning algorithms used in a company’s financial systems will be very different from what it is today (Donny C. Shimamoto, “Why Accountants Must Embrace Machine Learning,” IFAC, 2018, https://bit.ly/2Ao7TeX). Auditors will need to understand the technologies involved and their interaction with internal controls to avoid material misstatements. Potential fraud in a company’s financial statements could become easier to identify by using a machine learning algorithm to identify transactions that have characteristics associated with fraudulent activities.
The evolution of machine learning is expected to have a dramatic impact on business. The public accounting profession will need to adapt—to understand the technology used by companies to better focus its efforts in auditing the financial statements, and be better positioned to provide the best tax and advisory services at a cost that creates value for their clients. As public accounting firms continue to harness the potential of machine learning, new and dynamic opportunities will be available for young professionals.