The Impact of AI in the accounting field. History of AI in Accounting

The Impact of AI in the Accounting Field
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The Impact of AI in the Accounting Field
Introduction
The success of companies is pegged in their book keeping abilities with accountants and auditors playing significant roles in this respect. Business accountancy is responsible for keeping accurate and updated business records. Additionally, business accounting is critical in the analysis of a company’s financial operations that is helps the management in making better business decisions more proficiently. The field of accounting continues to experience technological disruptions with companies adopting new and efficient accounting systems. The accounting field was first based on ledgers and paper journals until fast, and efficient computerized accounting systems were developed replacing the traditional accounting processes. Fast forward, organizations are now adopting artificial intelligence (AI) in their accounting processes which are not only fast and efficient but also accurate. AI has become instrumental in improving the quality of accounting processes as well as mitigating various challenges associated with traditional accounting systems.
History of AI in Accounting
AI technology has more than 20 years of applications in financial repowering and auditing with an increasing number of firms adopting the technology including accounting and auditing firms. A research that was conducted by the University of Oxford in 2015 indicated that the accounting field is among the fields expected to experience the greatest change in their jobs with accountants having a 95% chance of losing their jobs to AI technology (Griffin, 2016). The accounting field, however, used ledgers and journals to record financial transactions prior to computer technology. The ‘80s and ‘90s saw an increase in accounting computerization as a means of increasing speed and efficiency. Digitization of accounting processes simplified the distribution of financial data as well as save costs that went too hard copy printing (Boylan and Boylan, 2017).
Accounting embraced manual computer typing in the ’90s using computer software that eased writing and calculations which were more cumbersome and time-consuming (Frey & Osborne, 2017). The need for efficiency, accuracy, and speed necessitated the incorporation expert systems into accounting processes thus marking the debut of AI in accounting.
Some of the challenges that pushed for the adoption the AI technology in the early 1990s include:
* Accounting information unable to provide satisfactory information to decision makers:
Traditional accounting systems including computerized accounting database systems did not provide adequate accounting information with output such as productivity and reliability of data becoming biased due to data exaggeration O'Leary, 1991). Such information is unreliable in making management decisions.
* Complex computerized accounting database systems
The traditional computerized accounting database systems had complex output while churning out primary output models such as cash flow statements, balance sheet and income statements (O'leary, 1991). The summary output of these important primary models was complex straining decision-making abilities of decision makers and was usually led to suboptimal decisions being made about the performance of businesses.
* Primary focus on Syntactic Data
The traditional computerized accounting systems were hailed for their ability to perform calculations of numeric data. These systems had greater emphasis on the use of numeric data than interpretation and output of text data. As a result, the output lacked the most important component of data analysis which entails semantic data (text) and other symbolic information to accompany numeric data in analytical processes (O'leary, 1991). Use of AI technology came in handy to resolve some of these inefficiencies if the traditional accounting systems.
Application of AI in Accounting
The incorporation of AI technology in computerized accounting meant that symbolic and numeric data is generated to provide more substantial information for decision-makers. According to O'leary (1991), incorporation of AI in accounting processes would assist accountants in analyzing and interpreting large volumes of data more accurately. AI was adopted by various companies that viewed the technology as the future of accounting. In readiness for business effectiveness and efficiency, businesses adopted AI as a means to improve both accounting results as well as risk assessment (Baldwin, Brown, and Trinkle, 2006). Various techniques were used during the early adoption of AI technology in accounting. These include:
Genetic algorithms
Baldwin, Brown, and Trinkle (2006) indicate that genetic algorithms would be useful for “modeling auditor behavior in fraud decisions” (p. 81). Further, genetic algorithms are important in accounting in bankruptcy prediction using genetic programming application (Baldwin, Brown, and Trinkle, 2006).
To search for optimal solutions, the Genetic Algorithms first generate a set of random solutions for the problem at hand and then calculate the quality of each of these solutions using an evaluation function. Then, through the reproduction mechanism, these algorithms combine the best solutions, originating new ones that will possibly be more adequate than the first ones. The new set of solutions is then evaluated. The procedures of reproduction and evaluation are repeated until the solutions set cannot be improved any more.
This process is shown in the table below representing all the possible solutions for a problem in the horizontal axis, and the quality of each of these solutions in the vertical axis. A set of solutions is randomly generated, and it is expected that, though the ...