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Financial Statement Fraud Detection using Text Mining  [PDF]
Rajan Gupta,Nasib Singh Gill
International Journal of Advanced Computer Sciences and Applications , 2013,
Abstract: Data mining techniques have been used enormously by the researchers’ community in detecting financial statement fraud. Most of the research in this direction has used the numbers (quantitative information) i.e. financial ratios present in the financial statements for detecting fraud. There is very little or no research on the analysis of text such as auditor’s comments or notes present in published reports. In this study we propose a text mining approach for detecting financial statement fraud by analyzing the hidden clues in the qualitative information (text) present in financial statements.
Mining Financial Statement Fraud: An Analysis of Some Experimental Issues  [PDF]
J. West,Maumita Bhattacharya
Computer Science , 2015,
Abstract: Financial statement fraud detection is an important problem with a number of design aspects to consider. Issues such as (i) problem representation, (ii) feature selection, and (iii) choice of performance metrics all influence the perceived performance of detection algorithms. Efficient implementation of financial fraud detection methods relies on a clear understanding of these issues. In this paper we present an analysis of the three key experimental issues associated with financial statement fraud detection, critiquing the prevailing ideas and providing new understandings.
A Review of Financial Accounting Fraud Detection based on Data Mining Techniques  [PDF]
Anuj Sharma,Prabin Kumar Panigrahi
Computer Science , 2013, DOI: 10.5120/4787-7016
Abstract: 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.
Survey of Data-mining Techniques used in Fraud Detection and Prevention  [PDF]
Sheela Thiruvadi,Sandip C. Patel
Information Technology Journal , 2011,
Abstract: Data mining is a powerful tool widely used by organizations to enhance their businesses and gain a competitive advantage over their competitors. The data mining process helps in extracting and analyzing various data patterns, information or trends from large databases. Various data mining techniques are available to conduct the data mining process. Data mining techniques are used in a variety of applications, one of which is the detection and prevention of different types of frauds. Although there is existing research on data mining and various data mining techniques that can be used to detect and identify different types of frauds, there is little research that synthesizes various facets of fraud that uses the data mining techniques. In this survey study, we classify frauds into four categories with regards to the use of data mining as a tool in fraud detection and prevention. The four categories of fraud are management fraud, customer fraud, network fraud and computer-based fraud. We present the latest developments on the use of data mining as a tool for each of these categories.
Fraud and Financial Crimes Prevention and Control in Nigeria: A Sociological Analysis  [cached]
Abdul Raheem Abdul Rasheed,Isiaka Sulu Babaita,Muhammed Abubakar Yinusa
International Journal of Asian Social Science , 2012,
Abstract: Fraud constitutes one of the financial crimes in Nigeria. It is very widespread and it manifests itself in virtually all aspects of national life. The nation, organizations and individuals have lost huge funds to fraudulent practices. It is within this context that this paper sets to examine strategies for effective prevention and control of fraud and financial crimes in Nigeria. Idealistic theory, functionalist theory and Marxist approach were employed in explaining fraud and financial crimes in Nigeria. The Federal Government and the Central Bank of Nigeria have continued to adopt various measures in combating fraud. Some of these measures include the establishment of Independent Corrupt Practices and Other Related Offences Commission (ICPC) and Economic and Financial Crimes Commission (EFCC). It then concludes with the submission that the effective fraud and financial crimes prevention and control require the monitoring of financial intuitions by regulatory authorities and intelligence gathering.
Survey of Insurance Fraud Detection Using Data Mining Techniques  [PDF]
H.Lookman sithic,,T.Balasubramanian
International Journal of Innovative Technology and Exploring Engineering , 2013,
Abstract: With an increase in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection has become an emerging topics of great importance for academics, research and industries. Financial fraud is a deliberate act that is contrary to law, rule or policy with intent to obtain unauthorized financial benefit and intentional misstatements or omission of amounts by deceiving users of financial statements, especially investors and creditors. 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. Financial fraud can be classified into four: bank fraud, insurance fraud, securities and commodities fraud. Fraud is nothing but wrongful or criminal trick planned to result in financial or personal gains. This paper describes the more details on insurance sector related frauds and related solutions. In finance, insurance sector is doing important role and also it is unavoidable sector of every human being.
Survey of Insurance Fraud Detection Using Data Mining Techniques  [PDF]
H. Lookman Sithic,T. Balasubramanian
Computer Science , 2013,
Abstract: With an increase in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection has become an emerging topics of great importance for academics, research and industries. Financial fraud is a deliberate act that is contrary to law, rule or policy with intent to obtain unauthorized financial benefit and intentional misstatements or omission of amounts by deceiving users of financial statements, especially investors and creditors. 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. Financial fraud can be classified into four: bank fraud, insurance fraud, securities and commodities fraud. Fraud is nothing but wrongful or criminal trick planned to result in financial or personal gains. This paper describes the more details on insurance sector related frauds and related solutions. In finance, insurance sector is doing important role and also it is unavoidable sector of every human being.
Document Fraud Detection with the help of Data Mining and Secure Substitution Method with Frequency Analysis
Ms.Namrata Shukla,Ms. Shweta Pandey
International Journal of Advanced Computer Research , 2012,
Abstract: Prevention of fraud and abuse has become a majorconcern of many organizations. The industryrecognizes the problem and is just now starting toact. Although prevention is the best way to reducefrauds, fraudsters are adaptive and will usually findways to circumvent such measures. Detecting fraudis essential once prevention mechanism has failed.Several data mining algorithms have beendeveloped that allow one to extract relevantknowledge from a large amount of data likefraudulent financial statements to detect. In thispaper we present an efficient approach for frauddetection. In our approach we first maintain a logfile for data which contain the content separated byspace, position and also the frequency. Then weencrypt the data by substitution method and send tothe receiver end. We also send the log file to thereceiver end before proceed to the encryption whichis also in the form of secret message. So the receivercan match the data according to the content,position and frequency, if there is any mismatchoccurs, we can detect the fraud and does not acceptthe file.
RELEVANCE OF RED FLAGS IN THE EVALUATINH THE RISK OF FINANCIAL STATEMENT FRAUD: PERCEPTIONS OF BRAZILIAN INDEPENDENT AUDITORS RELEV NCIA DOS RED FLAGS NA AVALIA O DO RISCO DE FRAUDES NAS DEMONSTRA ES CONTáBEIS: A PERCEP O DE AUDITORES INDEPENDENTES BRASILEIROS
Fernando Dal-Ri Murcia,José Alonso Borba,Eduardo Schiehll
Revista Universo Contábil , 2008,
Abstract: The objective of this paper was to identify perceptions of Brazilian auditors regarding the relevance of red flags in evaluating the risk of fraudulent financial reporting. To accomplish this, a questionnaire was designed using six papers as references: American Institute of Certified Public Accountants (2002), Conselho Federal de Contabilidade (1999), Albrecht and Romney (1986), Eining, Jones and Loebbecke, (1997), Bell and Carcacello (2000) and Wells (2005). Together, these works presented a total of 266 red flags. After a thorough comparative analysis, 45 red flags were selected and classified into 6 clusters: organizational structure and environment, sector/industry, managers, economic-financial position, financial reports and independent auditing. The sample is composed of auditors from the Instituto dos Auditores Independentes do Brasil (IBRACON). A total of 33 auditors participated in this research. Findings evidence that 95.56% of red flags presented an “intermediate risk” or “high risk” in the process of detecting fraudulent financial reporting. All 6 clusters also presented an average fraud risk of 3.35 or higher on a 1-5 scale. Keywords: Red flags. Financial statement. Fraud risk. Perception. Independent auditors. Este estudo objetivou analisar a percep o de auditores independentes brasileiros sobre a relevancia dos red flags na avalia o do risco de fraudes nas demonstra es contábeis. Para examinar esta quest o, elaborou-se um questionário de pesquisa baseado, principalmente, em 6 estudos publicados sobre o assunto e que identificam um total de 267 red flags. Por meio de uma análise comparativa, 45 red flags foram selecionados e posteriormente divididos em 6 grandes grupos: estrutura e ambiente, setor/indústria, gestores, situa o econ mico-financeira, relatórios contábeis e auditoria externa. A amostra é intencional foi composta por auditores cadastrados no Instituto dos Auditores Independentes do Brasil (IBRACON). Um total de 33 auditores, responderam à pesquisa. A análise das respostas compreende estatística descritiva (média, mediana, moda e desvio padr o) e uma Análise Hierárquica de Clusters. Os resultados sugerem que cerca de 95,56% dos red flags apresentam um “risco médio” ou “risco alto” no processo de avalia o de fraudes nas demonstra es contábeis. Do mesmo modo,todos os 6 grupos de red flags também apresentaram individualmente uma nota média igual ou superior a 3,35 em rela o ao nível de importancia, em uma escala de 1 a 5. Por outro lado, a Análise Hierárquica de Cluster permitiu formar 3 Clusters, mas apenas 1 deles era com
Intelligent Financial Fraud Detection Practices: An Investigation  [PDF]
J. West,Maumita Bhattacharya,R. Islam
Computer Science , 2015,
Abstract: Financial fraud is an issue with far reaching consequences in the finance industry, government, corporate sectors, and for ordinary consumers. Increasing dependence on new technologies such as cloud and mobile computing in recent years has compounded the problem. Traditional methods of detection involve extensive use of auditing, where a trained individual manually observes reports or transactions in an attempt to discover fraudulent behaviour. This method is not only time consuming, expensive and inaccurate, but in the age of big data it is also impractical. Not surprisingly, financial institutions have turned to automated processes using statistical and computational methods. This paper presents a comprehensive investigation on financial fraud detection practices using such data mining methods, with a particular focus on computational intelligence-based techniques. Classification of the practices based on key aspects such as detection algorithm used, fraud type investigated, and success rate have been covered. Issues and challenges associated with the current practices and potential future direction of research have also been identified.
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