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This study explores the question of whether investors can successfully detect management fraud using a firm's financial statements. Using financial ratios obtained from fraudulent companies’ financial statements, we examine the effectiveness of both logit and discriminant analyses in predicting the likelihood of fraud. Sixty-eight fraudulent companies used in the study are identified from the SEC's Accounting and Auditing Enforcement Releases. Our research design has addressed certain weaknesses present in prior fraud-detection studies. The empirical results suggest that ratio analysis is grossly ineffective in detecting financial statement fraud. We also discuss the implications of our findings on future research.

Fraudulent financial reporting is a matter of grave social and economic concern. The Treadway Commission recommended that the Auditing Standards Board require the use of analytical procedures to improve the detection of fraudulent financial reporting. This is an exploratory study to determine if financial ratios of fraudulent companies differ from those of nonfraudulent companies. Fraudulent firms were identified by examining the SEC's Accounting and Auditing Enforcement Releases issued between 1982 and 1999. The fraudulent firms (n=79) were then matched with nonfraudulent firms on the basis of firm size, time period, and industry. Using this matched‐pairs design, ratio analysis for a seven‐year period (i.e. the fraud year −/+ 3 years) was conducted on 21 ratios. Overall, 16 ratios were found to be significant. Of these, only three ratios were significant for three time periods. Of the 16 statistically significant ratios, only five were significant during the period prior to the fraud year. Using discriminant analysis, misclassifications for fraud firms ranged from 58 percent to 98 percent. These results provide empirical evidence of the limited ability of financial ratios to detect and/or predict fraudulent 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 present study is to assess the impact of intellectual capital (IC) on fraud in listed firms' financial statements on the Tehran Stock Exchange (TSE). In other words, this paper seeks to figure out whether IC and its components, namely, the efficiency of human capital (HC), structural capital (SC), relational capital (RC) and customer capital (CC).

The logistic regression model is used for analyzing the material of this study. Research hypotheses are also examined using a sample of 187 listed firms on the TSE during 2011–2018 by employing the logistic regression pattern based on synthetic data technique. Moreover, some robustness checks are also used to ensure the correctness of the obtained results.

The findings show a negative and significant relationship between IC and its components, including the efficiency of HC, SC, RC and CC, and fraud in financial statements. This means that by investing in the IC and its components, the amount of fraud in business firms' financial statements decreases.

Since few studies are carried out by existing literature, this paper is among the pioneer efforts assessing IC's potential impact on fraud commitment. The findings apply to policymakers to improve the clarity of the business atmosphere of Iran.

According to the Association of Certified Fraud Examiners, financial statement fraud represents the smallest amount of fraud cases but results in the greatest monetary loss. The researcher previously investigated the characteristics of financial statement fraud and determined the presence of 16 fraud indicators. The purpose of this study is to establish whether investors and other stakeholders can detect and identify financial statement fraud using these characteristics in an analysis of a company’s annual report.

This study analyses a financial statement fraud case, using the same techniques that were previously applied, including horizontal, vertical and ratio analysis. These are preferred because stakeholders have relatively easy access to them.

The findings show several fraud characteristics, with a few additional ones not previously found prevalent. Financial statement fraud thus tends to differ between cases. It is also easier to detect and identify fraud indicators ex post facto.

This study is a practical case showing that financial statement fraud can be detected and identified in the financial statements of companies that commit fraud.

The purpose of this paper is to develop a model for assessing the audit evidence of the going-concern (GC) assumptions underlying the preparation of financial statements.

This research analyses 678 audit opinions of Italian listed firms from 2007 to 2016 and uses a multiple linear discriminant analysis to create a GC score, which includes variables suggested by the international standards on auditing (ISA) 570 and by literature on GC.

The model provides three cut-off scores which can orient auditors towards issuing the most appropriate GC audit opinions (unmodified opinion, unmodified opinion, which includes emphases of matter, qualified opinion or disclaimer of opinion).

The development of the model is mainly based on public data and does not assess confidential information that is not disclosed in audit opinions.

This model can enable auditors to identify the most appropriate GC opinion and align auditor’s opinions in similar circumstances, thereby reducing their reliance on discretion and increasing the reliability of their judgement with a higher degree of accuracy. Moreover, this research lists additional events or conditions that may individually or collectively cast significant doubt on GC assumptions.

This study goes beyond the traditional decision-making process, apparently binary in nature, between “continuity” and “failure” or between “unmodified” and “modified” opinions. It is conceived to detect the different degrees of uncertainty that affect GC evaluations to orient auditors’ professional judgements.

This paper aims to explore the effectiveness of fraud prevention and detection techniques, including data analytics, machine learning and data mining, and to understand how widespread the use of data analytics is across different sectors and to identify and understand the potential barriers to implementing these techniques to detect and prevent fraud.

A survey was administered to 73 Irish businesses to determine to what extent traditional approach, data mining or text mining are being used to prevent or detect fraudulent financial reporting, and to determine the perception level of their effectiveness.

The study suggests that whilst data analytics is widely used by businesses in Ireland there is an under-utilisation of data analytics as an effective tool in the fight against fraud. The study suggests there are barriers that may be preventing companies from implementing advanced data analytics to detect financial statement fraud and identifies how those barriers may be overcome.

In contrast to the majority of literature on big data analytics and auditing, which lacks empirical insight into the diffusion, effectiveness and obstacles of data analytics, this explanatory study contributes by providing useful insights from the field on big data analytics. While the extant auditing literature generally addresses the avenues of big data utilisation in auditing domain, our study explores particularly the use big data analytics as a fraud prevention and detection techniques.

Proposes a refined conceptual framework for understanding the holistic process of service failure and service recovery for managers from a customer's point of view. The framework focuses on three main dimensions that are of particular relevance for service recovery research. The main dimensions are: awareness, process quality, and intent. The framework provides a holistic understanding of the antecedents and consequences of customer (dis) satisfaction in service failure and presents implications for management. It also presents an agenda for future research in this area.