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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.

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.

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.

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.

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.

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.

The purpose of this study is to shed light on the tools, processes and negotiations involved in the formation of acceptable current values in the context of goodwill impairment testing. The study raises the questions of how a current value for goodwill becomes a faithful representation and how one expectation about the future becomes more convincing than other expectations.

Drawing on the study of associations, the analysis presents a case study of a large, internationally active organisation. By combining field notes, interview transcripts and a variety of documents, the qualitative analysis focusses on strategies and mechanisms of persuasion.

The findings reveal how epistemological objectivity of current values forms in three moments of relational becoming that codify, depersonalise and proceduralise the valuation task. Further, the study suggests that a convincing argument forms with the help of four enablers: a bricolage of inscriptions, methodological mystification, transformed professional identities and a practical need for closure.

The study contributes with an analysis and illustration of financial accounting as practice, elaborating on the meaning and construction of faithful representation in cases of measurement uncertainty.

Previous studies have postulated that an advance payment system (APS) positively impacts the contractor's working capital and is paramount to ensuring an efficient and effective project cash flow process. However, scant research has been undertaken to empirically establish the cash flow performance and domino effect of APS on project and organisational performance.

The epistemological design adopted a positivist philosophical stance augmented by deductive reasoning to explore the phenomena under investigation. Primary quantitative data were collected from 504 Construction Industry Development Board (CIDB) registered contractors (within the grade bandings 1–9) in South Africa. A five-point Likert scale was utilised, and subsequent data accrued were analysed using structural equation modelling (SEM).

Emergent findings reveal that the mandatory use of an APS does not guarantee a positive project cash flow, an improvement in organisational performance or an improvement in project performance.

The ensuing discussion reveals the contributory influence of APS on positive cash flow and organisational performance, although APS implementation alone will not achieve these objectives. Practically, the research accentuates the need for various measures to be concurrently adopted (including APS) towards ensuring a positive project cash flow and improved organisational and project performance.

There is limited empirical research on cash flow performance and the domino effect of APS on project and organisational performance in South Africa, nor indeed, the wider geographical location of Africa as a continent. This study addresses this gap in the prevailing body of knowledge.

Business rescue, as a mechanism to aid financially distressed companies in South Africa, has received considerable academic and practical recognition. However, the business rescue plan is an overlooked and, perhaps, underdeveloped aspect of the regime. For stakeholders, this is the ultimate decision-making document. Creditors are the most influential stakeholders in business rescue proceedings owing to their voting rights. For creditors to make informed decisions and exercise their votes meaningfully, the business rescue plan should be transparent and adequately disclose relevant and reliable information. This study aims to identify creditors’ primary information needs to enhance the sufficiency and decision-usefulness of business rescue plans, not only to entice the vote of creditors but to enforce accountability from practitioners.

Using a qualitative research design, semi-structured interviews were conducted with 14 executives from 10 South African financial institutions.

The findings reveal that comprehensive disclosure of financial, commercial and legal information in business rescue plans was a critical antecedent for stakeholder decision-making. Additionally, leadership and social impact information were influential determinants. This study advances academic knowledge and, for practitioners, adds value to the development of business rescue plans. This can enhance creditors' confidence in supporting the rescue effort and approving the plan.

This study advances academic knowledge and, for practitioners, adds value to the development of business rescue plans. This can enhance creditors' confidence in supporting the rescue effort and approving the plan.

The originality of this article lies in its investigation of how creditors assess the information in BR plans as a precursor to supporting the company’s reorganisation in a creditor-friendly business rescue system such as South Africa. This study provides novel insights into the decision-making process, particularly how creditors assess BR plans, address information asymmetry and vote on the plan.

The pandemic COVID-19 has affected every sector of an economy in every possible way. Banking sector of Bangladesh has been affected by it badly. The purpose of this paper is to find out the impact of COVID-19 on the liquidity and financial health of the listed banks in Bangladesh.

Liquidity ratios are calculated to measure the liquidity condition of the banks and revised Altman's Z-Score Model for non-manufacturing companies is used to measure the financial health. The ratios are compared before and during the COVID-19 periods to assess the impact.

The findings of this study indicate a deterioration of liquidity position and financial health of the listed banks after the emergence of this pandemic. Though the banks have poor liquidity ratios and financial health prior to the emergence of this pandemic, they have decreased more in the second quarter of 2020. Most of the banks have poor liquidity ratios and cash position. The listed Islamic Banks have poor financial health than the listed Commercial Banks and all the banks belong to the red zone in all the quarters.

The results of this study will have policy implications for companies and regulators of money market.

This paper is a pioneer initiative in assessing the impact of COVID-19 pandemic on liquidity and financial health based on empirical data.

The authors examine the role of analysts’ prior experience of forecasting for firms exposed to epidemics on analysts’ forecast accuracy during the COVID-19 pandemic.

The authors examine the impact of analysts’ prior epidemic experience on forecast accuracy by comparing the changes from the pre-COVID-19 period (calendar year 2019) to the post-COVID period extending up to March 2023 across HRE versus non-HRE analysts. The authors consider a full sample (194,980) and a sub-sample (136,836) approach to distinguish “Recent” forecasts from “All” forecasts (including revisions).

The study's findings reveal that forecast accuracy for HRE analysts is significantly higher than that for non-HRE analysts during COVID-19. Specifically, forecast errors significantly decrease by 0.6% and 0.15% for the “Recent” and “All” forecast samples, respectively. This finding suggests that analysts’ prior epidemic experience leads to an enhanced ability to assess the uncertainty around the epidemic, thereby translating to higher forecast accuracy.

The finding that the expertise developed through an experience of following high-risk firms in the past enhances analysts’ performance during the pandemic sheds light on a key differentiator that partially explains the systematic difference in performance across analysts. The authors also show that industry experience alone is not useful in improving forecast accuracy during a pandemic – prior experience of tracking firms during epidemics adds incremental accuracy to analysts’ forecasts during pandemics such as COVID-19.

The study findings should prompt macroeconomic policymakers at the national level, such as the central banks of countries, to include past epidemic experiences as a key determinant when forecasting the economic outlook and making policy-related decisions. Moreover, practitioners and advisory firms can improve the earning prediction models by placing more weight on pandemic-adjusted forecasts made by analysts with past epidemic experience.

The uncertainty induced by the COVID-19 pandemic increases uncertainty in global financial markets. Under such circumstances, the importance of analysts’ role as information intermediaries gains even more importance. This raises the question of what determines analysts’ forecast accuracy during the COVID-19 pandemic. Building upon prior literature on the role of analyst experience in shaping analysts’ forecasts, the authors examine whether experience in tracking firms exposed to prior epidemics allows analysts to forecast more accurately during COVID-19. The authors find that analysts who have experience in forecasting for firms with high exposure to epidemics (H1N1, Zika, Ebola, and SARS) exhibit higher accuracy than analysts who lack such experience. Further, this effect of experience on forecast accuracy is more pronounced while forecasting for firms with higher exposure to the risk of COVID-19 and for firms with a poor ex-ante informational environment.