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.Comment: 11 Pages. International Journal of Computer Applications February 201

The Impact of Insurance Fraud Detection Systems

The purpose of this paper is to characterize the impact of fraud detection systems on the auditing procedure and the equilibrium insurance contract, when a policyholder can report a loss that never occurred. Insurers can only detect fraudulent claims through a costly audit (costly state verification). With a fraud detection system insurers can condition their audits on the signal of the system and auditing becomes more effective. This paper presents conditions under which insurance fraud and the resulting welfare losses can be reduced by the implementation of a costly fraud detection system in a competitive insurance market that is supplied by an external third party.insurance fraud, auditing, detection systems

The Impact of Insurance Fraud Detection Systems

The purpose of this paper is to characterize the impact of fraud detection systems on the auditing procedure and the equilibrium insurance contract, when a policyholder can report a loss that never occurred. Insurers can only detect fraudulent claims through a costly audit (costly state verification). With a fraud detection system insurers can depend their audit on the signal of the system and auditing becomes more effective. This paper presents conditions under which insurance fraud and the resulting welfare losses can be reduced by the implementation of a costly fraud detection system that is supplied by an external third party.insurance fraud, auditing, detection systems, costly state verification

Electronic fraud detection in the U.S. Medicaid Healthcare Program: lessons learned from other industries

It is estimated that between $600 and$850 billion annually is lost to fraud, waste, and abuse in the US healthcare system,with $125 to$175 billion of this due to fraudulent activity (Kelley 2009). Medicaid, a state-run, federally-matchedgovernment program which accounts for roughly one-quarter of all healthcare expenses in the US, has been particularlysusceptible targets for fraud in recent years. With escalating overall healthcare costs, payers, especially government-runprograms, must seek savings throughout the system to maintain reasonable quality of care standards. As such, the need foreffective fraud detection and prevention is critical. Electronic fraud detection systems are widely used in the insurance,telecommunications, and financial sectors. What lessons can be learned from these efforts and applied to improve frauddetection in the Medicaid health care program? In this paper, we conduct a systematic literature study to analyze theapplicability of existing electronic fraud detection techniques in similar industries to the US Medicaid program

Does Canada Have a Problem with Occupational Fraud?

Small and medium-sized enterprises (SMEs) are an important collective force in the Canadian economy, however the visibility and economic power of small businesses suffer due to their size and frequent turnover. When it comes to the issue of businesses being subject to occupational fraud, the moderate visibility of SMEs only contributes to the challenge of assessing the real scope of the problem. This paper seeks to examine the prevalence and types of occupational fraud experienced by Canadian SMEs as well as gathers information on prevention and detection methods used to safeguard against occupational fraud. That is done based on data compiled from a survey of 802 SMEs across Canada. The analysis shows that a substantial proportion of SMEs experience incidents of occupational fraud; however, the majority of SMEs are not fully prepared to respond to fraud. Furthermore, SMEs’ experience with and attitudes toward fraud vary noticeably with company characteristics, although a large proportion of SMEs believe risk to occupational fraud is low.Occupational fraud, fraud prevention, fraud detection, types of occupational fraud, Canadian small and medium businesses, employee fraud, internal fraud

Search Rank Fraud De-Anonymization in Online Systems

We introduce the fraud de-anonymization problem, that goes beyond fraud detection, to unmask the human masterminds responsible for posting search rank fraud in online systems. We collect and study search rank fraud data from Upwork, and survey the capabilities and behaviors of 58 search rank fraudsters recruited from 6 crowdsourcing sites. We propose Dolos, a fraud de-anonymization system that leverages traits and behaviors extracted from these studies, to attribute detected fraud to crowdsourcing site fraudsters, thus to real identities and bank accounts. We introduce MCDense, a min-cut dense component detection algorithm to uncover groups of user accounts controlled by different fraudsters, and leverage stylometry and deep learning to attribute them to crowdsourcing site profiles. Dolos correctly identified the owners of 95% of fraudster-controlled communities, and uncovered fraudsters who promoted as many as 97.5% of fraud apps we collected from Google Play. When evaluated on 13,087 apps (820,760 reviews), which we monitored over more than 6 months, Dolos identified 1,056 apps with suspicious reviewer groups. We report orthogonal evidence of their fraud, including fraud duplicates and fraud re-posts.Comment: The 29Th ACM Conference on Hypertext and Social Media, July 201

HOW TO PREVENT FRAUD?

Fraud can range from minor employee theft and unproductive behavior tomisappropriation of assets and fraudulent financial reporting. The risk of fraud can be reduced through a combination of prevention and detection measures. Moreover, prevention and deterrence measures are much less costly than the time and expense required for fraud detection and investigation. The information presented in this document generally is applicable to entities of all sizes. However, the degree to which certain programs and controls are applied in smaller, less-complex entities and the formality of theirapplication are likely to differ from larger organizations.fraud proofing, fraud prevention, control, accounting analysis, job descriptions, supervision