Due to the rapid advancement of electronic commerce technology, there is a great and dramatic increase in credit card transactions. 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; to detect credit card frauds in electronic transactions becomes the focus of risk of control of banks. The propos...

Anomaly detection is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or finding errors in text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions. Many tec...

With the help of a commercial bank in China, we studied consumer credit card debt behavior in correlation with demographics, attitude, personality, and credit card features factors. The study was conducted by using mail-in questionnaires, which were sent to credit card holders who was using or had used either revolving credit or petty installment plans. According to regression functions, we fou...

It is the time of technology advancement. Due to internet everything available at touch a finger. There benefit online shopping: first it saves lots and second does not demand go market buy anything. exists various mode payments credit card payment one them. Today, there good number users in world. Every day so many cards transactions are taken place. Some these fraudulent. such fraudulent bank...

Credit Card Fraud is one of the biggest threats to business establishments today. This paper presents a cascade artificial neural network for the recognition of credit card fraud detection. This system aims at attaining a very high recognition rate and a very high reliability, In other words, excellent recognition performance of credit card fraud detection was obtained. Then, One solution was p...

Credit card frauds are increasing day by day regardless of the various techniques developed for its detection. Fraudsters are so expert that they engender new ways for committing fraudulent transactions each day which demands constant innovation for its detection techniques as well. Many techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Machine learning, Sequence Alignment,...

Fraud is increasing dramatically with the expansion of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. Although prevention technologies are the best way of reducing fraud, fraudsters are adaptive and, given time, will usually find ways to circumvent such measures. Methodologies for the detection of fraud are esse...

Debit cards have become an indispensable part of the U.S. payments system, accounting for more than a third of consumer payments at point of sale. With this development has come controversy: Card networks charge merchants fees that merchants believe are too high. And most of the fees are ad valorem—that is, based on transaction value—rather than fixed fees per transaction. Merchants are critica...

Many factors innuence the performance of a learned classiier. In this paper we study diier-ent methods of measuring performance based on a uniied set of cost models and the eeects of training class distribution with respect to these models. Observations from these eeects help us devise a distributed multi-classiier meta-learning approach to learn in domains with skewed class distributions, non-...

Bank transaction fraud results in over $13B annual losses for banks, merchants, and card holders worldwide. Much of this fraud starts with a Point-of-Compromise (a data breach or a ”skimming” operation) where credit and debit card digital information is stolen, resold, and later used to perform fraud. We introduce this problem and present an automatic Pointsof-Compromise (POC) detection procedu...