The EMD issue has raised many concerns in previous research with variation noted in solutions and limitations. Recently, a new approach in EMD such as supervised machine learning has been introduced. The tool has shown its capability in improving the efficiency of systems and the designs of machines especially in the classification process.
In this study, the supervised machine learning technique was used as a classification tool to evaluate and compare the advanced mathe- matical model, “the Beneish model” with the “Typical Manual Auditors’ Methods”. This study was conducted using audited financial data over four consecutive years from 2006 to 2009. Unlike prior studies, the BNC was introduced for the purpose of evaluating both the Beneish model and the auditors’ manual methods. Previous research has shown that the BNC is an optimal classifier and it best fits the EMD classification problem highlighted. The results drawn from this study reveal that the M-Score generated through the Beneish model produced better sensitivity as compared to the Manual Auditors’ Methods in assessing financial data for the purpose of detecting earnings manipulation. The BNC had correctly classified 86.84 per cent of the manipulated companies under the M-Score assessment while under the manual auditors’ methods it had accurately categorised only 60.53 per cent.
Since prior studies have identified manipulating companies from the list of enforcement actions for fraud and restatement, the identi- fication of fraud cases by authorities and governments appear to have been less effective. Furthermore, the Tax Compliance Departments or
the Certified Auditing Agencies may have selection biases in some fraud cases thus, detection is low. Moreover, the criteria used to assess and evaluate EMD are diverse, making the process less efficient. Thus, using the Beneish model would help to overcome this issue.
The aim of this study is to enhance the processes of earnings manipulation detection by creating a preliminary tool which, in this case, is based on machine learning, to assess any typical manual audit behaviour against any advanced mathematical, statistical, or scientific model. The study also aims to demonstrate to what extent the BNC can be applied on advanced mathematical model such as the Beneish model to reshape modern auditing framework.
From the study conducted, it can be concluded that traditional audit methods, which are based on simple methods of basic ratio analysis, sampling, vouching of every transaction, manual verification of accounts, and non-technological incorporation, need to be integrated with technological advancements so as to satisfy the needs of the new economy. The identification of material misstatements of financial statements is a critical step in the audit field today because fraud identification and prevention is important. However, fraud identification and prevention is impeded by the complexity of the transactions involved and the growth of global economies in the new era of technology. Therefore, modern audit methods complemented with supervised classification and machine learning can assist the auditing analytical procedures within the EMD context. Incorporating advanced mathematical models such as the Beneish model can provide better results. The traditional method is disadvantaged by the limited analytical capabilities of auditors in analysing big financial data and their limited testing abilities for all business cases. Nonetheless, the mathematical procedure using the Beneish model lacks the qualitative featured data of manual auditing. The Beneish model can build an advanced model through expectation maximisation or any algorithm. This will definitely strengthen the Beneish model through machine learning which can help to detect earnings manipulations, regardless of the qualitative audit factors. At this level, incorporating both the Beneish model and machine learning technique is a step forward for the auditing field.
This study contributes valuable knowledge for potential users of financial statements such as shareholders, financial analysts, auditors, accountants, government tax controllers, financial forensic investigators and academic researchers. It constitutes a preliminary study on machine- learning approach for auditing. It broadens the scope of using the
Beneish model for identifying earnings manipulators and in assisting the decision-making process. It can indicate manipulations under the application of machine learning tools. Furthermore, the comparison noted in the power of classification in EMD using a machine learning technique such as the BNC will encourage auditors or other related investigative parties to search for tools and techniques which can be embedded into their continuous auditing procedure. This can certainly enable them to have a better understanding of the attributes of earnings manipulation and machine learning in detecting fraud.
Despite the contributions of this study, the research is limited by the single industry analysis, single machine learning technique, single mathematical model, and the small sample size. These limitations may restrict the generalisation of the results to other industries. Therefore, for future contributions, an expansion of data in terms of other industries
should be examined with the application of other manipulation detec-
tion models and machine learning techniques to test for the classification of earnings manipulation.
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