Purpose

Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

Design/methodology/approach

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

Findings

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Research limitations/implications

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Practical implications

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

Originality/value

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

中文翻译：

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人工智能财务会计预测——系统文献综述和未来研究议程

目的

会计信息系统主要是基于规则的，数据通常是可用的且结构良好。然而，许多会计系统尚未跟上当前的技术发展。因此，财务会计中的人工智能（AI）通常仅应用于试点项目。在会计中使用基于人工智能的预测可以实现主动管理和详细分析。然而，到目前为止，人们对哪些预测模型已经针对会计问题进行了评估还知之甚少。鉴于缺乏研究，我们的研究旨在总结有关人工智能如何用于财务会计预测目的的现有发现。因此，作者旨在为未来的研究人员提供全面的概述和议程，以获得更普遍的知识。

设计/方法论/途径

作者通过系统的文献综述，确定了财务会计中基于人工智能的预测的现有研究。为此，作者使用 Scopus 和 Web of Science 作为科学数据库。数据收集的最终样本量为 47 项研究。对这些研究的预测目的、样本量、周期和应用的机器学习算法进行了分析。

发现

作者确定了三个应用领域，并详细介绍了所使用的准确性和人工智能方法。我们的研究结果表明，社会技术和通用知识仍然缺失。因此，作者还制定了一个开放的研究议程，未来的研究人员可以解决这个问题，以便在财务会计中更频繁、更有效地使用基于人工智能的预测。

研究局限性/影响

由于人工智能算法的快速发展，我们的结果只能提供当前研究状况的概述。因此，很可能会应用新的人工智能算法，而现有研究尚未涵盖这些算法。然而，感兴趣的研究人员可以利用我们的发现和未来的研究议程进一步发展这一领域。

实际影响

鉴于人工智能在财务会计中的高度相关性，我们的结果对从业者有多种影响和潜在好处。首先，作者概述了不同会计用例中使用的人工智能算法。基于此概述，企业可以评估最适合其实际需求的人工智能算法。其次，从业者可以使用我们的结果作为可实现和应该努力达到的预测准确性的基准。最后，我们的研究发现了研究中的几个盲点，例如确保公司员工接受机器学习算法。然而，企业应该考虑这一点，以便在财务会计中成功实施人工智能。

原创性/价值

据我们所知，尚未开展任何研究来全面概述财务会计中基于人工智能的预测。鉴于人工智能在会计领域的巨大潜力，作者旨在弥合这一研究差距。此外，我们的跨应用视图提供了对特定算法的优越性的一般见解。