Comprehensive Survey of Data Mining-based Fraud Detection Research
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.
Data mining is about finding insights which are statistically reliable, unknown previously, and actionable from data (Elkan, 2001). This data must be available, relevant, adequate, and clean. Also, the data mining problem must be well-defined, cannot be solved by query and reporting tools, and guided by a data mining process model (Lavrac et al, 2004). The term fraud here refers to the abuse of a profit organisation’s system without necessarily leading to direct legal consequences. In a competitive environment, fraud can become a business critical problem if it is very prevalent and if the prevention procedures are not fail-safe. Fraud detection, being part of the overall fraud control, automates and helps reduce the manual parts of a screening/checking process. This area has become one of the most established industry/government data mining applications. It is impossible to be absolutely certain about the legitimacy of and intention behind an application or transaction. Given the reality, the best cost effective option is to tease out possible evidences of fraud from the available data using mathematical algorithms. Evolved from numerous research communities, especially those from developed countries, the analytical engine within these solutions and software are driven by artificial immune systems, artificial intelligence, auditing, database, distributed and parallel computing, econometrics, expert systems, fuzzy logic, genetic algorithms, machine learning, neural networks, pattern recognition, statistics, visualisation and others. There are plenty of specialised fraud detection solutions and software which protect businesses such as credit card, e-commerce, insurance, retail, telecommunications industries. There are often two main criticisms of data mining-based fraud detection research: the dearth of publicly available real data to perform experiments on; and the lack of published wellresearched methods and techniques. To counter both of them, this paper garners all related literature for categorisation and comparison, selects some innovative methods and techniques for discussion; and points toward other data sources as possible alternatives. The primary objective of this paper is to define existing challenges in this domain for the different types of large data sets and streams. It categorises, compares, and summarises relevant data mining-based fraud detection methods and techniques in published academic and industrial research. The second objective is to highlight promising new directions from related adversarial data mining fields/applications such as epidemic/outbreak detection, insider trading, intrusion detection, money laundering, spam detection, and terrorist detection. Knowledge and experience from these adversarial domains can be interchangeable and will help prevent repetitions of common mistakes
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