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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 contribute to the discussion surrounding the definition of accounting proposed by Carnegie et al. (2021a, 2021b) and further elaborated by Carnegie et al. (2023) from/under an institutionalist political-economy (IPE) based foundation and to specifically extend this approach to the arena of social and environmental accounting (SEA).

By adopting an IPE approach to SEA, this study offers a critique of the use of the notion of capital to refer to nature and people in SEA frameworks and standards.

A SEA framework based on the capabilities approach is proposed based on the concepts of human capabilities and global commons for the purpose of preserving the commons and enabling the flourishing of present and future generations.

The proposed framework allows the engagement of accounting community, in particular SEA researchers, with and contribution to such well-established initiatives as the Planetary Boundaries framework and the human development reports initiative of the United Nations Development Programme.

Based on the capability approach, this study applies Carnegie et al.’s (2023) framework to SEA. This new approach more attuned to the pursuit of sustainable human development and the sustainable development goals, may contribute to turning accounting into a major positive force through its impacts on the world, expressly upon organisations, people and nature.

Mainland Tanzania has seen two decades of significant social policy reforms and transformations in its social and economic structures, whilst the country continues to grapple with persisting gender inequalities. This article examines Tanzania's social policy developments from a gender perspective. The authors analyse the level, reach and quality of social policy delivery to working-age women across the areas of health policy, social protection and employment policy during 2000–2021.

The article draws on qualitative research deploying the scoping review method. The data consist of diverse secondary materials, including academic publications, government policy documents, relevant statistics and other types of “grey” literature.

Tanzania has made significant advancements in the legal frameworks around welfare provision and has instituted increasingly gender-responsive government policy plans. The health and social protection sectors, in particular, have witnessed the introduction of large-scale measures expanding social policy implementation. However, social policy delivery remains two-tiered, with differences in provisions for women in the formal and informal sectors.

Social policy delivery and implementation have increased and diversified in Sub-Saharan Africa (SSA) during the new millennium, with a growing integration of gender-specific policy objectives. However, limited social policy scholarship has focused on the gendered effects of broader social policy models in SSA. The article remedies the concomitant knowledge gaps by examining various social policies and their impacts on working-age women in Mainland Tanzania. The authors also engage with the theoretical welfare regime literature and present an analytical framework for gender-sensitive assessment of emerging social policy models in the Global South.

The primary objective of this research is to chronicle how the Environmental Protection Agency (EPA) and other United States Federal Government Agencies (USFGA) agencies have played a role in shaping the trajectory of financial reporting for sustainability, with a particular emphasis on triple bottom line (TBL). This exploration extends to other indexes reporting sustainability data encompassed within financial, social and environmental reporting.

This study adopts an illustrative methodology, utilizing data sourced from governmental, business and international organizational documents.

Sustainability accounting predominantly finds its place within the framework of TBL. However, it is crucial to note that sustainability reporting remains voluntary rather than mandatory. Nevertheless, accounting firms and professional accounting societies have embraced it as a supplementary facet of financial accounting reporting.

The research highlights the historical evolution of sustainability within the USFGA and corporate entities. Corporations’ interest in accounting for sustainability performances has significantly contributed to the emergence of voluntary sustainability accounting rules, as embodied by the TBL.