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Prevention and Detection of Financial Statement Fraud – An Implementation of Data Mining Framework  [PDF]
Rajan Gupta,Nasib Singh Gill
International Journal of Advanced Computer Sciences and Applications , 2012,
Abstract: Every day, news of financial statement fraud is adversely affecting the economy worldwide. Considering the influence of the loss incurred due to fraud, effective measures and methods should be employed for prevention and detection of financial statement fraud. Data mining methods could possibly assist auditors in prevention and detection of fraud because data mining can use past cases of fraud to build models to identify and detect the risk of fraud and can design new techniques for preventing fraudulent financial reporting. In this study we implement a data mining methodology for preventing fraudulent financial reporting at the first place and for detection if fraud has been perpetrated. The association rules generated in this study are going to be of great importance for both researchers and practitioners in preventing fraudulent financial reporting. Decision rules produced in this research complements the prevention mechanism by detecting financial statement fraud.
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.
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.
Detecting Auto Insurance Fraud by Data Mining Techniques  [cached]
Rekha Bhowmik
Journal of Emerging Trends in Computing and Information Sciences , 2011,
Abstract: The paper presents fraud detection method to predict and analyze fraud patterns from data. To generate classifiers, we apply the Na ve Bayesian Classification, and Decision Tree-Based algorithms. A brief description of the algorithm is provided along with its application in detecting fraud. The same data is used for both the techniques. We analyze and interpret the classifier predictions. The model prediction is supported by Bayesian Na ve Visualization, Decision Tree visualization, and Rule-Based Classification. We evaluate techniques to solve fraud detection in automobile insurance.
Exploration of Data mining techniques in Fraud Detection: Credit Card
Khyati Chaudhary,Bhawna Mallick
International Journal of Electronics and Computer Science Engineering , 2012,
Abstract: Data mining has been increasing as one of the chief key features of many security initiatives. Often, used as a means for detection of fraud, assessing risk as well. Data mining involves the use of data analysis tools to discover unknown, valid patterns as well as relationships in large data sets. Decades have seen a massive growth in the use of credit cards as a transactional medium. Data mining become even more common in both the private and public sectors. Data mining has been used widely in industries such as Banking, Insurance, Medicine and Retailing to reduce costs, enhance Research and increase Sales. Credit cards are much safer from theft than is cash and also a promising area for buying and sales. Credit Cards are growing as a popular medium of transaction. Therefore, Fraud Detection involves monitoring the behavior of users/customers in order to estimate, detect or avoid undesirable behavior in future. In this paper, we investigated the factors and various techniques involved in credit card fraud detection during/after transaction as well.
A Comprehensive Survey of Data Mining-based Fraud Detection Research  [PDF]
Clifton Phua,Vincent Lee,Kate Smith,Ross Gayler
Computer Science , 2010, DOI: 10.1016/j.chb.2012.01.002
Abstract: This survey paper categorises, compares, and summarises from almost all published technical and review articles in automated fraud detection within the last 10 years. It defines the professional fraudster, formalises the main types and subtypes of known fraud, and presents the nature of data evidence collected within affected industries. Within the business context of mining the data to achieve higher cost savings, this research presents methods and techniques together with their problems. Compared to all related reviews on fraud detection, this survey covers much more technical articles and is the only one, to the best of our knowledge, which proposes alternative data and solutions from related domains.
Research on outlier mining
离群点挖掘研究

XU Xiang,LIU Jian-wei,LUO Xiong-lin,
徐翔
,刘建伟,罗雄麟

计算机应用研究 , 2009,
Abstract: The problem of outlier mining attracts more and more interests in research when the research fields of fraud detection, intrusion detection, fault diagnosis and so on receive wide attentions. This paper presented a survey for the research results of outlier mining at home and abroad, and based on this survey, introduced the research process of outlier mining in the areas of database. It also presented a summary of the current state of the art of these techniques, a discussion on future research topics, and the challenges of the outlier mining.
A RECENT REVIEW ON ASSOCIATION RULE MINING  [PDF]
Maragatham G,Lakshmi M
Indian Journal of Computer Science and Engineering , 2011,
Abstract: Recently more encroachment has emerged in the field of data mining. One of the hottest topic in this area is mining for hidden patterns from the existing massive collection of databases. The knowledge obtained from these databases are used for different applications like super market sales-prediction, fraud detection etc. In thisarticle, the various advancements in data mining using the association rule mining is discussed. The role of Association rules in temporal mining, utility mining, statistical mining, privacy preservation mining, particle swarm optimizations etc., are reviewed. Therefore, this survey guides the researchers to know the progress ofpattern mining using association rules for the intended purposes.
No Evidence of the Effect of the Interventions to Combat Health Care Fraud and Abuse: A Systematic Review of Literature  [PDF]
Arash Rashidian, Hossein Joudaki, Taryn Vian
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0041988
Abstract: Background Despite the importance of health care fraud and the political, legislative and administrative attentions paid to it, combating fraud remains a challenge to the health systems. We aimed to identify, categorize and assess the effectiveness of the interventions to combat health care fraud and abuse. Methods The interventions to combat health care fraud can be categorized as the interventions for ‘prevention’ and ‘detection’ of fraud, and ‘response’ to fraud. We conducted sensitive search strategies on Embase, CINAHL, and PsycINFO from 1975 to 2008, and Medline from 1975–2010, and on relevant professional and organizational websites. Articles assessing the effectiveness of any intervention to combat health care fraud were eligible for inclusion in our review. We considered including the interventional studies with or without a concurrent control group. Two authors assessed the studies for inclusion, and appraised the quality of the included studies. As a limited number of studies were found, we analyzed the data using narrative synthesis. Findings The searches retrieved 2229 titles, of which 221 full-text studies were assessed. We found no studies using an RCT design. Only four original articles (from the US and Taiwan) were included: two studies within the detection category, one in the response category, one under the detection and response categories, and no studies under the prevention category. The findings suggest that data-mining may improve fraud detection, and legal interventions as well as investment in anti-fraud activities may reduce fraud. Discussion Our analysis shows a lack of evidence of effect of the interventions to combat health care fraud. Further studies using robust research methodologies are required in all aspects of dealing with health care fraud and abuse, assessing the effectiveness and cost-effectiveness of methods to prevent, detect, and respond to fraud in health care.
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