Before artificial intelligence, accounting had a difficulty with an enormous data explosion that is going to get worse in this world of data obesity. Because of this issue, there is a possibility of human error and fraud. Meanwhile, accounting is an important financial analysis task in human society. Therefore, every human error and fraud will have a significant effect on the company’s financial position. It can lead to financial losses, reputational damage, and even legal action. The application of artificial intelligence in financial accounting can make use of the huge advantages of science and technology to fill the shortage of artificial accounting. Artificial intelligence can be better applied to financial accounting and provide faster and smarter services for social development.
PwC has made a significant investment in pioneering artificial intelligence (AI) for financial accounting and audit. In collaboration with H2O.ai, a company in Silicon Valley, PwC has integrated AI technology into accounting practice through the creation of the GL.ai robot. Using artificial intelligence (AI) and machine learning, this revolutionary bot ‘x-rays’ a business, analyzing billions of data points in milliseconds, seeing what humans can’t, and applying judgment to detect anomalies in the general ledger.
GL.ai harnesses PwC’s global knowledge and experience, embedding it in algorithms trained to replicate the thinking and decision-making of expert auditors. It examines every uploaded transaction, every user, every amount and every account to find unusual transactions–indicating potential error or fraud–in the general ledger without bias or variability. The bot is built with the ability to look at different risks simultaneously. It looks beyond a single unusual activity or characteristic to identify a combination of different features.
With the features in GL.ai, the presentation of financial reports can be better. It’s because GL.ai can detects account activity that may be unusual due to a combination that rarely occurs or due to a combination that is not unusual but the scale of its usage in a period is unusual account activity, entries that are unusually late with regard to whether the period is a quarter-end close or not (unusually backdated), and multiple dimensions when the combination of different features helps identify entries that represent a particular risk of error or manipulation. This entity-specific analysis provides a breadth and depth of analysis that can only be delivered with advanced technology.
GL.ai also looks at the broad range of activity within the context of what is normal for the company and the individuals posting journals. It inspects the uploaded transactions, and corresponding amounts to detect potential errors and fraud in the general ledger. After inspecting these errors, the bot provides visualization and insights for each identified error. By that visualizations, it provides understanding and insight for each issue identified, ensuring that both auditor and client fully understand the issue and can resolve it efficiently
Because of the help of GL.ai, the company’s finances gain so many benefits. For example, the workload of accountants and auditors is reduced since there is no need to go-back-forth for asking questions to the client. By the use of AI, it reduces the costs involved in manual hours of research and analysis. As more data is processed, AI systems can enhance their accuracy in detecting anomalies by continuously learning from and adapting to datasets. As a result, the use of AI or machine learning increases the financial statement quality and reduces the risks from possibilities of fraud and human error. GL.ai also speeds up the audit process and generates insights that boost efficiency and provides comfort that attention is being focused on areas of true risk.
From the study that has been done by Kevin Deniswara, Michael Jonathan, Archie N. Mulyawan, and Irvan Santoso that aims to analyze the effectiveness of augmented artificial intelligence implementation, PwC’s GL.ai in preventing fraudulent financial statements by utilizing the Beneish M-Score model. This study concludes that the implementation of augmented artificial intelligence, namely PwC’s GL.ai is an effective treatment to prevent the probability of fraudulent financial statements from occurring. Now that you know all of GL.ai’s features and benefits, will AI for general ledger like gl.ai be implemented for every general ledger in the future? What do you think?
References
Banham, Russ (2019, September 3). Wrong Numbers: The Risks of Inaccurate Financial Statements. https://www.rmmagazine.com/articles/article/2019/09/03/-Wrong-Numbers-The-Risks-of-Inaccurate-Financial-Statements-
Berman, Jeff (2018, June 08). PwC Exec: AI Offers Significant Opportunity for Companies Across All Sectors. Retrieved from https://www.mesaonline.org/2018/06/08/pwc-exec-ai-offers-significant-opportunity-for-companies-across-all-sectors/
Dilmegani, Cem (2023, January 1). AI Audit in 2023: Guide to faster & more accurate audits. Retrieved from https://research.aimultiple.com/ai-audit/
GL.ai PwC’s anomaly detection for the general ledger. Retrieved from https://www.pwc.com/m1/en/events/socpa-2020/documents/gl-ai-brochure.pdf
Harnessing the power of AI to transform the detection of fraud and error. Retrieved from https://www.pwc.com/gx/en/about/stories-from-across-the-world/harnessing-the-power-of-ai-to-transform-the-detection-of-fraud-and-error.html
Kevin Deniswara, Michael Jonathan, Archie N. Mulyawan, and Irvan Santoso. 2023. Analysis on The Effectiveness of Augmented Artificial Intelligence Implementation in Preventing Fraudulent Financial Statement by Utilizing Beneish M-Score Model. In Proceedings of the 2022 6th International Conference on E-Business and Internet (ICEBI ’22). Association for Computing Machinery, New York, NY, USA, 319–326. https://doi.org/10.1145/3572647.3572695
Pahuja, Riya (2023, March 15). Artificial Intelligence at PWC. Retrieved from .https://accounting.binus.ac.id/2020/12/16/forensic-data-analytics-mengenal-forensic-accounting-berbasis-big-data/
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