The ubiquitous cases of abnormal transactions with intent to defraud is a global phenomenon. An architecture that enhances fraud detection using a radial basis function network was designed using a supervised data mining technique― radial basis function (RBF) network, interpolation approximation method. Several base models were thus created, and in turn used in aggregation to select the optimum...

Credit card plays a very important rule in today's economy. It becomes an unavoidable part of household, business and global activities. Although using credit cards provides enormous benefits when used carefully and responsibly,significant credit and financial damagesmay be causedby fraudulent activities. Many techniques have been proposed to confront thegrowthin credit card fraud. However, all...

Detecting financial fraud is an important issue and ignoring this issue may cause financial and non-financial losses to individuals and organizations. The aim of this study is to test the ability of Beneish M-Score Model for detecting financial fraud among companies listed on Tehran stock exchange. The research sample consists of 137 companies listed on Tehran Stock Exchange for a period of 11 ...

Specialty literature and solutions in the market have been focusing last decade on collecting aggregating significant amounts of data about transactions (and user behavior) refining algorithms used to identify fraud. At same time, legislation European Union has adopted direction (e.g., PSD2) order impose obligations stakeholders However, one hand, provides a high-level description this legal ob...

Outlier detection is useful for credit card fraud detection. Due to drastic increase in digital frauds, there is a lot of financial losses and therefore various techniques are developed for fraud detection and applied to diverse business fields. In high-dimensional data, outlier detection presents some challenges because of increment of dimensionality. In this paper, the proposed model aims to ...