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.

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The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature

Identifying the signs of fraudulent accounts using data mining techniques

Due to a rapid advancement in the electronic commerce technology, the use of credit cards has dramatically increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In this paper, we model the sequence of operations in credit card transaction processing using a hidden Markov model (HMM) and show how it can be used for the detection of frauds. An HMM is initially trained with the normal behavior of a cardholder. If an incoming credit card transaction is not accepted by the trained HMM with sufficiently high probability, it is considered to be fraudulent. At the same time, we try to ensure that genuine transactions are not rejected. We present detailed experimental results to show the effectiveness of our approach and compare it with other techniques available in the literature.

Credit Card Fraud Detection Using Hidden Markov Model

Fraud detection system

Due to the dramatic increase of fraud which results in loss of billions of dollars worldwide each year, several modern techniques in detecting fraud are continually developed and applied to many business fields Fraud detection involves monitoring the behavior of populations of users in order to estimate, detect, or avoid undesirable behavior Undesirable behavior is a broad term including delinquency, fraud, intrusion, and account defaulting This paper presents a survey of current techniques used in credit card fraud detection, telecommunication fraud detection, and computer intrusion detection The goal of this paper is to provide a comprehensive review of different techniques to detect frauds

http://metalab.uniten.edu.my/~abdrahim/ntl/Survey%20of%20Fraud%20Detection%20Techniques.pdf

Survey of fraud detection techniques

A parallel granular neural network (GNN) is developed to speed up data mining and knowledge discovery process for credit card fraud detection. The entire system is parallelized on the Silicon Graphics Origin 2000, which is a shared memory multiprocessor system consisting of 24-CPU, 4G main memory, and 200 GB hard-drive. In simulations, the parallel fuzzy neural network running on a 24-processor system is trained in parallel using training data sets, and then the trained parallel fuzzy neural network discovers fuzzy rules for future prediction. A parallel learning algorithm is implemented in C. The data are extracted into a flat file from an SQL server database containing sample Visa Card transactions and then preprocessed for applying in fraud detection. The data are classified into three categories: first for training, second for prediction, and third for fraud detection. After learning from training data, the GNN is used to predict on a second set of data and later the third set of data is applied for fraud detection. GNN gives fewer average training errors with larger amount of past training data. The higher the fraud detection error is, the greater the possibility of that transaction being actually fraudulent.

M. Syeda

Papers

Parallel granular neural networks for fast credit card fraud detection