Methodologies in Financial Fraud Detection and Prevention
Fraud is increasing dramatically with the expansion of modern technology and global communication, resulting in substantial losses to the business. It has escalated to become a global concern and significantly threatens organization. The true extent of fraudulent instances is unknown. However, the reported incidents show an ever-increasing trend in the number and size of frauds. Prior research on the occurrence of fraud has addressed the effectiveness of monitoring mechanism such as good governance, sound internal control procedures and fraud prevention programs. However, research results do not help to fully understand how these factors may mitigate incidences of fraud in developing countries. In addition, incentives to reduce or prevent fraud may not be applicable across various industries.
Fraudulent reporting has been a serious problem that may lead to not direct financial losses and jeopardize the reputation of an organization and its relationships with external stakeholders, such as customers, suppliers, financiers and business partners. According to the agency theory, a conflict of interests between managers and stakeholders has potential agency costs such as management decisions that do not maximize stakeholders’ benefits. Managers may manage reported earnings to justify their actions. Misreporting financial data may lead to an agency cost whereby the public is driven to make non-optimal investment decisions from reported earnings. For instance, in a situation where a company has a high free cash flow, the manager may be engaged in misreporting financial data to show better performance of the company.
Tax fraud is another type of fraud that can erodes the revenue of the nation. Aggressive measures need to be exercised to combat tax fraudsters and rescue the severe financial loss to the country. The income erosion under the current tax system proves to be substantial that compromises the fundamental revenue, efficiency and equity goals of the nation (Murray, 1997). The government’s effort in implementing the goods and services tax (GST) to increase the generation of income for the country will not be successful unless proper monitoring, regulations and proper preventive procedures are in place to curb tax fraud. Although the income tax and GST systems can generate additional revenue for the country, inadequacy in the enforcement and implementation of tax system may result in serious tax evasion cases. As such, it is crucial to develop an effective and efficient mechanism of Malaysian system for income tax and GST.
The most effective remedy to combat fraud is prevention (Button, 2011). Although research on anti-fraud software analysis (Morley, Ball & Ormerod, 2006) and anti-fraud resources (Palasinski, 2013) exist, it is noted that research on the counter fraud strategies used in the public and private sectors are relatively rare (Button & Brooks, 2009). Reports and researches have documented huge losses resulting from fraud, such as non-financial damage to reputation that may have long term negative repercussion. The situation is further exacerbated by a lack of manpower trained in fraud investigation and detection, and identifying red flags. To facilitate fraud investigation and detection, an effective system is critically warranted to ensure organizational sustainability. The system is seen as one of the important tool in forensic investigation activities to improve the current dire situation so as to enhance and improve damage control efforts. Besides, the system would help to overcome the current problem of inadequate monitoring of fraud red flags, notably because red flags are effective indicators of what future actions need to be taken.
Financial Fraud and Corporate Governance for Financial Reporting Quality, Integrity and Transparency
For achieving this goal, this study is going to be a mixed method research. At the first stage, in order to recognize the potential aspects of misreporting reporting, several depth interviews will be conducted with the expert of custom officers and also internal auditors. Then, secondary data analysis will be conducted based on the reported financial statements by Malaysian listed companies for year of 2015 and 2016.
Income Tax System in Elevating the Nation Income: Mitigation of Tax Evasion and Enforcement of GST
This study reviews the literature to understand GST implementation experience of other countries. This study uses two research methods. First, the study conducts focus group discussions (FGD) representing a qualitative method. FGD involves semi structured interviews of staffs of IRB and RMCD on knowledge and expertise, technology-based collection system, monitoring mechanism and regulatory enforcement. Data from the interviews are recorded and transcribed into a text program and transferred into software for analysis. Second, the study conducts survey and experiments involving tax parties. The sample consists of companies from selected industries. Questionnaires are developed from focus group discussion and documented in previous publications. The study conducts contents validation and reliability tests by cross reference to documented scientific evidence and discussions with experts.
Fraud Detection and Prevention Systems to Mitigate Fraud Risk for sustainability of Organizational Performance
This study will focus on public listed companies in Malaysia. Exploratory sequential mixed method research design which combines qualitative and quantitative data will be employed in this research (Cresswell, 2012). Prior to the actual research fieldwork, a preliminary study will be conducted in order to address ethical issue and to validate data collection procedures. The data collection process consists of interviews with participants on their experiences along with experts’ opinions on the issues and literature reviews. At the initial stage, the researchers will review all the relevant literatures on fraud prevention programs. The use of Bloomberg Database will facilitate to capture necessary data to predict the occurrence of financial fraud. Example of Financial fraud prediction technique is Artificial Neural Network (ANN).