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This study aims to trace the impact of corporate governance and its mechanisms in preventing companies from turning to fraudulent financial reporting.

For this purpose, using the systematic elimination pattern, the information of 187 listed companies on the Tehran Stock Exchange over six years from 2013 to 2019 were collected, and the hypotheses were examined using a linear regression model. To measure fraudulent financial reporting, the adjusted model of Beneish (1999) was used to evaluate corporate governance. Its mechanisms based on nine corporate governance mechanisms, including board independence, board remuneration, CEO financial expertise, expertise in CEO industry, board financial expertise, board industry expertise, board effort, CEO duality and managerial ownership, have been examined. These mechanisms are calculated as a combined index of corporate governance.

The findings indicate that robust corporate governance significantly reduces companies’ intention toward fraudulent financial reporting. In the same way, a negative and significant relationship was observed between each of the nine corporate governance mechanisms, except for board compensation and fraudulent financial reporting.

This study’s findings provide valuable insight into the importance of strengthening companies to prevent companies’ managers from engaging in fraudulent financial reporting activities. Hence, it is suggested that professional references bodies more seriously follow the rules to dictate to companies for using and empowering their corporate governance.

Purpose – This study investigates the nature, types, and methods of fraudulent financial reporting committed by Chinese listed companies with a view to understanding corporate behavior relating to management fraud in China. Such an understanding is important for preventing frauds and achieving better financial reporting compliance.

Design/Methodology/Approach – This study adopts a descriptive research approach using the data based on 182 punishment bulletins issued by the China Securities Regulatory Commission from 2002 to 2006. The study considers three categories of frauds (i.e., false income statements, false balance sheets, and insufficient or false disclosure) and uses these categories to describe and analyze the fraud cases.

Research findings/Insights – Based on the sample of 83 cases over the 5-year period from 2002 to 2006, this study finds that all the frauds in the sample involved the manipulation, alteration, and falsification of reported financial information. Fraud schemes often contained more than one technique to misstate financial statements, typically through overstating revenues and assets, and understating liabilities and expenses. Most of the sample companies committed several frauds simultaneously. This study also reveals that most of the frauds committed by Chinese listed companies lasted more than 2 years, with the longest being 9 years, and common intervals between the initial fraud year and the announcement year of punishment were more than 3 years, with the longest being 11 years.

Theoretical/Academic implications – This study provides an empirical analysis of fraudulent financial reporting cases committed by Chinese listed companies. These cases were rarely studied in the Western literature. This study contributes to the extant literature by providing an insight into management fraud in China. Research into fraudulent financial reporting in the largest developing economy is certainly of interest as prior research into this area is mostly based on developed economies.

Practitioner/Policy implications – The implications drawn from this study could be useful for a better understanding of the management behavior of companies in developing and transitional economies. This study has a potential to assist regulators and accounting professional bodies to set guidelines facilitating corporate compliance of regulated financial reporting.

This study aims to compare machine learning models, datasets and splitting training-testing using data mining methods to detect financial statement fraud.

This study uses a quantitative approach from secondary data on the financial reports of companies listed on the Indonesia Stock Exchange in the last ten years, from 2010 to 2019. Research variables use financial and non-financial variables. Indicators of financial statement fraud are determined based on notes or sanctions from regulators and financial statement restatements with special supervision.

The findings show that the Extremely Randomized Trees (ERT) model performs better than other machine learning models. The best original-sampling dataset compared to other dataset treatments. Training testing splitting 80:10 is the best compared to other training-testing splitting treatments. So the ERT model with an original-sampling dataset and 80:10 training-testing splitting are the most appropriate for detecting future financial statement fraud.

This study can be used by regulators, investors, stakeholders and financial crime experts to add insight into better methods of detecting financial statement fraud.

This study proposes a machine learning model that has not been discussed in previous studies and performs comparisons to obtain the best financial statement fraud detection results. Practitioners and academics can use findings for further research development.

The purpose of this paper is to provide a critical examination of the current law and the possible changes that are under consideration by the Law Commissions, after public consultation in relation to the continuing duty of good faith and post‐contractual duties owed by the insured towards the insurer.

The research is addressed using case law, statutes, current academic and Law Commissions publications in the UK.

First, the paper finds that the current state of the law allows for the insurer to claim damages from an insured when a fraudulent claim is made to recover the cost of any investigations. Second the insurer can refuse to meet a claim that is tainted by fraud. Third the insurer can have the right to avoid the policy obligations upon the discovery of a fraud, but subject to some limitations. Fourth there is a need for the insured to be protected against an insurer's unjustified allegations of fraud.

This work does not address detailed issues in relation to pre‐contractual issues of good faith. These have been discussed in a previous edition of this journal see Swaby. G. (2010) “Insurance law: fit for purpose in the twenty‐first century?” IJLMA, 52 (1), pp. 21‐39. ISSN 1754‐243X.

The Law Commission will be undertaking further consultations before reforming this area of law.

This paper will be of interest to legal practitioners and academics and those in the insurance industry.

Creditor reliance on accounting‐based numbers as a persistent and traditional standard to assess a firm's financial soundness and viability suggests that the integrity of financial statements is essential to credit decisions. The purpose of this paper is to provide an approach to explore fraudulent financial reporting (FFR) via growing hierarchical self‐organizing map (GHSOM), an unsupervised neural network tool, to help capital providers evaluate the integrity of financial statements, and to facilitate analysis further to reach prudent credit decisions.

This paper develops a two‐stage approach: a classification stage that well trains the GHSOM to cluster the sample into subgroups with hierarchical relationship, and a pattern‐disclosure stage that uncovers patterns of the common FFR techniques and relevant risk indicators of each subgroup.

An application is conducted and its results show that the proposed two‐stage approach can help capital providers evaluate the reliability of financial statements and accounting numbers‐based decisions.

Following the SOM theories, it seems that common FFR techniques and relevant risk indicators extracted from the GHSOM clustering result are applicable to all samples clustered in the same leaf node (subgroup). This principle and any pre‐warning signal derived from the identified indicators can be applied to assessing the reliability of financial statements and forming a basis for further analysis in order to reach prudent decisions. The limitation of this paper is the subjective parameter setting of GHSOM.

This is the first application of GHSOM to financial data and demonstrates an alternative way to help capital providers such as lenders to evaluate the integrity of financial statements, a basis for further analysis to reach prudent decisions. The proposed approach could be applied to other scenarios that rely on accounting numbers as a basis for decisions.

This paper aims to analyze the relationship between the following auditor's characteristics with detecting frauds in the listed companies on the Tehran Stock Exchange.

A multiple regression model is used to test the research hypothesis. The hypothesis was further tested with a sample of 187 companies listed on the Tehran Stock Exchange (1,309 observations) from 2012 to 2018 and by using multiple regression models based on panel data and the random-effects model.

The results suggest a positive and significant relationship between audit firms' size, auditor rotation, specialization in the industry, the audit market's focus, auditor's independence, audit report lag and renewal of financial statements with fraud detection. The results revealed a significant relationship between the period of auditor tenure, auditor's narcissism, audit fees and the type of auditors' opinion (un-qualified opinion) with fraud detection.

As the present study is a pioneer in examining this issue in the emerging markets, it provides users, analysts and legal entities with useful information about auditor characteristics that significantly affect the fraud detection of financial statements. The results mitigate the literature gap and improve knowledge in this area.

This study examines the effect that client size has on the relation between industry‐specialist auditors and fraudulent financial reporting. Most of the major accounting firms have organized their audit practices along industry lines, reflecting a belief that industry specialization leads to higher quality audits. Furthermore, regulatory bodies and extant research suggests that larger clients have greater bargaining power and are more likely to be able to convince the auditor to acquiesce to aggressive accounting. Also, it may be more difficult for an auditor to possess industry expertise for larger clients who are likely to be more complex and operate in more than one industry. Consistent with previous research, we generally find a significant negative relation between auditor industry specialization and client financial fraud. Also, as expected, the negative relation between auditor industry specialization and financial fraud is weaker for larger clients. This study provides evidence that the positive benefits of auditor industry specialization in deterring financial fraud is affected by client size.

The increasing incidence of fraudulent financial reporting by firms in recent years raises concerns about investors' confidence in capital markets. Academicians and industry practitioners adopt diverse risk management techniques to detect fraudulent reporting of financial statements. This paper aims to determine the effectiveness of the Beneish M-score and Altman Z-score models for the early detection of material misstatements at Comscore, Inc., a media analytics firm in the United States of America.

The financial statements of Comscore Inc. from 2012 to 2018 were analyzed with the primary objective of early fraud detection by employing the Beneish M-score and the Altman Z-score.

The study’s outcomes indicate that the Beneish M-score is less predictable in fraud detection compared to the Altman Z-score. The study results did not confirm the efficacy of the Beneish model in predicting fraudulent financial statements. The study concludes that the choice of forensic tool greatly influences fraud detection outcomes.

The research findings can guide the policy decision-making of investors, financial auditors, and forensic auditors as this study provides some evidence of the effectiveness of forensic tools in the detection of financial statement fraud in corporate entities.

This is the first study to apply these two widely used tools to the most recent big corporate scandal: Comscore, Inc.

This paper examines published data to develop a model for detecting factors associated with false financial statements (FFS). Most false financial statements in Greece can be identified on the basis of the quantity and content of the qualifications in the reports filed by the auditors on the accounts. A sample of a total of 76 firms includes 38 with FFS and 38 non‐FFS. Ten financial variables are selected for examination as potential predictors of FFS. Univariate and multivariate statistical techniques such as logistic regression are used to develop a model to identify factors associated with FFS. The model is accurate in classifying the total sample correctly with accuracy rates exceeding 84 per cent. The results therefore demonstrate that the models function effectively in detecting FFS and could be of assistance to auditors, both internal and external, to taxation and other state authorities and to the banking system.

This study aims to provide theoretical and empirical insights concerning how macro-level characteristics influence micro-psychological characteristics, in perpetrating fraudulent behaviours. This is because many fraud studies have mainly been focussed on the solo psychological aspects of the offender, rather than the social environments. This study also makes clear that fraudulent behaviours are different from delinquencies.

This research is focussed on the big bureaucratic scandals, occurring in Indonesia. The authors chose Indonesia because it places one of the corrupt countries in The Association of Southeast Asian Nations countries. To achieve the goals, the authors used the ethnographic approach by conducting an exclusive interview with 30 elite executives from the Government of Indonesia.

This study finds a deeper understanding of the root causes of fraud committed by individuals and co-offenders, in which micro-psychological, situational, sociological and criminological aspects are linked together.

This study contains provocative findings that can stimulate a critical understanding of the psychological aetiology of an individual’s intention to perpetrate partial fraud or to co-offend.