Artificial Intelligence (AI) has become an integral part of modern business operations. Many organizations have begun transforming their work processes by integrating AI to reduce processing time and enhance overall efficiency. One area significantly impacted by this transformation is bookkeeping, particularly tasks that are repetitive and time-consuming. The use of AI can accelerate bookkeeping processes, improve accuracy, and reduce the required personnel and effort.
This article explores the potential applications of AI in bookkeeping and presents practical examples of its use.
AI-Assisted Account Code Selection
A critical process in bookkeeping is selecting the correct account code in accordance with accounting principles. Incorrect account coding can result in erroneous bookkeeping records and inaccurate financial statements, potentially affecting the decision-making of financial statement users.
AI’s ability to process large volumes of data is especially valuable for enhancing both the accuracy and efficiency of account code selection. For example, the study “Multilabel Classification of Account Code in Double-Entry Bookkeeping” (Kotepuchai & Limpiyakorn, 2024) proposed a method for applying AI to classify account codes using a multilabel classification technique. This approach is necessary because some transactions may correspond to multiple account codes.
The study used data from transaction descriptions and related attributes that accountants provide during the bookkeeping process, as well as information derived from the double-entry accounting system, to distinguish between debit and credit entries. These inputs were then used to train the model and develop accurate predictive capabilities.
In practice, an accountant simply inputs the transaction description into the model. For example, in the case of a credit sale transaction, the accountant specifies: “Sold goods to Company A on credit.” The model then processes the information and predicts that the transaction should be recorded using the Accounts Receivable code on the debit side and the Revenue code on the credit side.
Experimental results indicate that the model achieved an accuracy of 84.91% in classifying account codes, demonstrating AI’s potential to assist accountants in selecting codes efficiently and accurately. These results suggest that AI can be used as a reliable tool to support account code selection, reduce errors caused by manual data entry, and significantly accelerate the bookkeeping process.
Challenges and Opportunities for Accountants
Despite AI’s high potential in accounting, several challenges remain—particularly the need to adapt to rapidly changing technologies. Accountants in the digital age must develop skills in applying AI and understanding increasingly complex data systems. For example, account code classification using AI requires an understanding of advanced mathematical and statistical models.
In addition to classification, AI can also be applied to other accounting processes, such as lease categorization, receipt processing, and automated payment management. These developments can significantly reduce workloads and improve operational efficiency. At the same time, the opportunities arising from AI are substantial. When routine tasks are automated, accountants can allocate more time to value-added activities such as strategic analysis and financial advisory services.
AI also functions as an efficient tool for processing large amounts of data, enabling accountants to focus on developing their expertise and analytical skills in other important areas. As AI rapidly transforms workplace processes, organizations must adapt in order to remain competitive. Accounting is one such area undergoing change, and accountants should develop strong technological skills to enhance workflow efficiency and improve performance.
This will allow accountants to devote more time to in-depth data analysis and the organization’s financial strategy. In conclusion, the use of AI in accounting is a significant step toward increasing the efficiency of accountants. Therefore, developing knowledge and practical skills in AI is essential to maximize its benefits for the organization.
Source
- TFAC Newsletter No. (Issue 13, January 2025)
- “Multilabel Classification of Account Code in Double-Entry Bookkeeping” – Pakorn Kotepuchai & Yachai Limpiyakorn
Author
Mr. Pakorn Kotepuchai
Working Group on IT Promotion for Auditing,
under the Auditing Profession Committee, TFAC