Probabilistic reasoning in intelligent systems: networks of plausible inference
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Knowledge discovery in databases: an overview
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Applied multivariate techniques
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Knowledge management and data mining for marketing
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Data Mining and Knowledge Discovery
Distributed Data Mining in Credit Card Fraud Detection
IEEE Intelligent Systems
Pattern Classification (2nd Edition)
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A Case Study of Applying Boosting Naive Bayes to Claim Fraud Diagnosis
IEEE Transactions on Knowledge and Data Engineering
Applications of Data Mining in Retail Business
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On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms
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Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
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Introduction to Data Mining, (First Edition)
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Data Mining techniques for the detection of fraudulent financial statements
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Credit Card Fraud Detection Using Hidden Markov Model
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Real-time credit card fraud detection using computational intelligence
Expert Systems with Applications: An International Journal
Association rules applied to credit card fraud detection
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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Auto claim fraud detection using Bayesian learning neural networks
Expert Systems with Applications: An International Journal
Discovering golden nuggets: data mining in financial application
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Neural fraud detection in credit card operations
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Decision Support Systems
Bagging k-dependence probabilistic networks: An alternative powerful fraud detection tool
Expert Systems with Applications: An International Journal
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Answering queries in hybrid Bayesian networks using importance sampling
Decision Support Systems
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Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
A semi-supervised graph-based algorithm for detecting outliers in online-social-networks
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A service oriented architecture to provide data mining services for non-expert data miners
Decision Support Systems
Assessing scorecard performance: A literature review and classification
Expert Systems with Applications: An International Journal
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More applicable environmental scanning systems leveraging "modern" information systems
Information Systems and e-Business Management
Topological pattern discovery and feature extraction for fraudulent financial reporting
Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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Decision Support Systems
BizPro: Extracting and categorizing business intelligence factors from textual news articles
International Journal of Information Management: The Journal for Information Professionals
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This paper presents a review of - and classification scheme for - the literature on the application of data mining techniques for the detection of financial fraud. Although financial fraud detection (FFD) is an emerging topic of great importance, a comprehensive literature review of the subject has yet to be carried out. This paper thus represents the first systematic, identifiable and comprehensive academic literature review of the data mining techniques that have been applied to FFD. 49 journal articles on the subject published between 1997 and 2008 was analyzed and classified into four categories of financial fraud (bank fraud, insurance fraud, securities and commodities fraud, and other related financial fraud) and six classes of data mining techniques (classification, regression, clustering, prediction, outlier detection, and visualization). The findings of this review clearly show that data mining techniques have been applied most extensively to the detection of insurance fraud, although corporate fraud and credit card fraud have also attracted a great deal of attention in recent years. In contrast, we find a distinct lack of research on mortgage fraud, money laundering, and securities and commodities fraud. The main data mining techniques used for FFD are logistic models, neural networks, the Bayesian belief network, and decision trees, all of which provide primary solutions to the problems inherent in the detection and classification of fraudulent data. This paper also addresses the gaps between FFD and the needs of the industry to encourage additional research on neglected topics, and concludes with several suggestions for further FFD research.