Every year billions of Euros are lost worldwide due to credit card fraud. Thus, forcing financial institutions to continuously improve their fraud detection systems. In recent years, several studies have proposed the use of machine learning and data mining techniques to address this problem. However, most studies used some sort of misclassification measure to evaluate the different solutions, a...

a r t i c l e i n f o Keywords: Data mining Financial fraud detection Feature selection t-statistic Neural networks SVM GP Recently, high profile cases of financial statement fraud have been dominating the news. This paper uses data mining techniques such as Multilayer to identify companies that resort to financial statement fraud. Each of these techniques is tested on a dataset involving 202 C...

Fraud detection is of great importance to financial institutions. This paper is concerned with the problem of finding outliers in time series financial data using Peer Group Analysis (PGA), which is an unsupervised technique for fraud detection. The objective of PGA is to characterize the expected pattern of behavior around the target sequence in terms of the behavior of similar objects, and th...

One of the recent strategies for increasing the customer’s loyalty in banking industry is the use of customers’ club system. In this system, customers receive scores on the basis of financial and club activities they are performing, and due to the achieved points, they get credits from the bank. In addition, by the advent of new technologies, fraud is growing in banking domain as well. Therefor...

a r t i c l e i n f o Occupational fraud is a $652 billion problem to which disgruntled employees are a major contributor. Much security research addresses reducing fraud opportunity and increasing fraud detection, but detecting motivational factors like employee disgruntlement is less studied. The Sarbanes–Oxley Act requires that companies archive email, creating an untapped resource for deter...

Loan fraud is a critical factor in the insolvency of financial institutions, so companies make an effort to reduce the loss from fraud by building a model for proactive fraud prediction. However, there are still two critical problems to be resolved for the fraud detection: (1) the lack of cost sensitivity between type I error and type II error in most prediction models, and (2) highly skewed di...

Managing fraud is important for business, retail and financial alike. One method to manage fraud is by detection, where transactions, etc. are monitored and suspicious behaviour is flagged for further investigation. There is currently a lack of public research in this area. The main reason is the sensitive nature of the data. Publishing real financial transaction data would seriously compromise...

We estimate what percentage of firms engage in fraud and the economic cost of fraud. Our estimates are based on detected frauds, and frauds that we infer are started but are not caught. To identify the ‘iceberg’ of undetected fraud we take advantage of an exogenous shock to the incentives for fraud detection: Arthur Andersen’s demise, which forces companies to change auditors. By assuming that ...

-------------------------------------------------------------------ABSTRACT---------------------------------------------------------------------Financial losses due to financial statement frauds (FSF) are increasing day by day in the world. The industry recognizes the problem and is just now starting to act. Although prevention is the best way to reduce frauds, fraudsters are adaptive and will ...