Accounting Fraud Detection Research Articles

Detection Techniques of Fraud in Accounting

The problem relating with the accounting fraud is that it has not stopped despite the numerous changes that have been made in legislation and strict controls by the internal and external auditors. Therefore, the aim of this paper is to asess strategies and techniques available to use for accounting fraud detection. Also, this paper aims to assess the impact of fraud, and the role of auditors and the media in combating this phenomenon. This research will be carried out using secondary data because it fits better the aim and purpose of the research. Data used derived from a range of sources that have a very high degree of credibility and reliability, and which have been carefully reviewed by other relevant bodies for accuracy and quality purposes. This includes databases and various publications. In addition, the data also stem from individual and group publications made by renowned scientific researchers who have solid expertise in their field of accounting. Narrative analysis is used to analyze the data in this paper. This research will conclude that techniques used for the detection of fraud in accounting and various strategies can positively influence the treatment of this phenomenon due to the alarm buzzer for possible fraud. However, vigilance is necessarily required to add all parties involved in the corporation through the use of different anti-fraud programs to stimulate the accountability of the management executives and employees through informants to fight this destructive phenomenon.

A Review of Financial Accounting Fraud Detection based on Data Mining Techniques

With an upsurge in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection (FAFD) has become an emerging topic of great importance for academic, research and industries. The failure of internal auditing system of the organization in identifying the accounting frauds has lead to use of specialized procedures to detect financial accounting fraud, collective known as forensic accounting. Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. This paper presents a comprehensive review of the literature on the application of data mining techniques for the detection of financial accounting fraud and proposes a framework for data mining techniques based accounting fraud detection. The systematic and comprehensive literature review of the data mining techniques applicable to financial accounting fraud detection may provide a foundation to future research in this field. The findings of this review show that data mining techniques like logistic models, neural networks, Bayesian belief network, and decision trees have been applied most extensively to provide primary solutions to the problems inherent in the detection and classification of fraudulent data.

Accounting Fraud Detection: Is it Possible to Quantify Undiscovered Cases?

Accounting fraud is defined as intentional misstatement of financial reports, in violation of generally accepted accounting principles, with the objective of making certain people act in detriment to their best interests. It is possible to identify the determinants of fraud using an econometric model, but the dependent variable (occurrence of fraud in a given company) is vulnerable to misclassification: Not every case of fraud will be detected, thus false negatives are possible. This paper estimates the percentage of undiscovered frauds and also estimates a probit model to detect fraud in US companies. The dependent variable was built using the instances of fraud discovered by the Securities and Exchange Commission (SEC). The model was estimated only with frauds that occurred between 1998 and 2002 (since many cases of fraud from the last years are still unknown). The independent variables were chosen using the concept of fraud triangle. The financial statement data were obtained using Compustat. The results show that the likelihood of fraud is negatively related to the current ratio, to the cash change (scaled by total assets) and to the fixed assets (also scaled by total assets). Companies that changed their auditors or receive a qualified auditing report are more susceptible to fraud. The probability that a case of fraud is not detected was estimated as 97.61%; this means just a small part of fraud cases are discovered by the SEC.