Blind Alleys of Financial Fraud

Raval, Vasant

doi:10.1080/07366981.2020.1791390

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…In addition, there is still a risk of fraudulent activities, as bookings can be modified or eliminated retrospectively (Faccia and Mos ¸teanu, 2019). Recent fraud scandals such as Wirecard, Luckin Coffee or Steinhoff show that in some cases the accounting information presented does not correspond to the actual financial situation and can contain false statements (Raval, 2020). This leads to misallocations in the capital market, as investors make wrong investments based on false information (Giroux, 2008).…”

Section: Financial Accountingmentioning

confidence: 99%

Forecasting in financial accounting with artificial intelligence – A systematic literature review and future research agenda

Kureljusic

Karger

2023

JAAR

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PurposeAccounting 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/approachThe 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.FindingsThe 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/implicationsOwing 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 implicationsGiven 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/valueTo 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.

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Section: Financial Accountingmentioning

confidence: 99%