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This study aims to objectively synthesize the volume of accounting literature on financial statement fraud (FSF) using a systematic literature review research method (SLRRM). This paper analyzes the vast FSF literature based on inclusion and exclusion criteria. These criteria filter articles that are present in the accounting fraud domain and are published in peer-reviewed quality journals based on Australian Business Deans Council (ABDC) journal ranking. Lastly, a reverse search, analyzing the articles' abstracts, further narrows the search to 88 peer-reviewed articles. After examining these 88 articles, the results imply that the current literature is shifting from traditional statistical approaches towards computational methods, specifically machine learning (ML), for predicting and detecting FSF. This evolution of the literature is influenced by the impact of micro and macro variables on FSF and the inadequacy of audit procedures to detect red flags of fraud. The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper chronicles the cluster of narratives surrounding the inadequacy of current accounting and auditing practices in preventing and detecting Financial Statement Fraud. The primary objective of this study is to objectively synthesize the volume of accounting literature on financial statement fraud. More specifically, this study will conduct a systematic literature review (SLR) to examine the evolution of financial statement fraud research and the emergence of new computational techniques to detect fraud in the accounting and finance literature.

The storyline of this study illustrates how the literature has evolved from conventional fraud detection mechanisms to computational techniques such as artificial intelligence (AI) and machine learning (ML). The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper contributes to the literature by providing insights to researchers about why the evolution of accounting fraud literature from traditional statistical methods to machine learning algorithms in fraud detection and prediction.

This study aims to observe people’s decisions to commit fraud. This study is important in the current time because it provides insights into the development of fraudulent intentions within individuals.

The information used in this study is derived from semi-structured interviews, conducted with 16 high-ranking officials who are employed in Indonesian local government positions.

The study does not have strong evidence to support prior studies assuming that situational factors or social enablers have direct effects on fraud intentions. As suggested, individual factors which are related to moral reasoning (moral judgment and rationalisation) emerge as a consequence of social enablers. The significant role of that moral reasoning is to rationalise any fraud attempt as permissible conduct. As such, when an individual is capable of legitimising his/her fraud attempt into appropriate self-judgement, s/he is more likely to engage in fraudulent behaviours.

This study offers practical prescriptions in guiding the management to develop strategies to curb fraudulent behaviours. The study suggests that moral cognitive reasoning is found to be a parameter of whether fraud is an acceptable option or not. So, an understanding of observers’ moral reasoning is helpful in predicting the likelihood of fraud within an organisation or in detecting it.

This study provides a different perspective on the psychological pathway to fraud. It becomes a complement work for the fraud triangle to explain fraudulent behaviours. Specifically, it provides crucial insights into the underlying motivations that lead individuals to accept invitations to engage in fraudulent activities.