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...

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,...

Nowadays the customers prefer the most accepted payment mode via credit card for the convenient way of online shopping, paying bills in easiest way. At the same time the fraud transaction risks using credit card is a main problem which should be avoided. There are many data mining techniques available to avoid these risks effectively. In existing research they modelled the sequence of operation...

this paper proposes a HMM (Hidden Markov Model) based fraud detection system for credit card fraud detection. The method works on the statistical behavior of user’s transactions. Since the original transactions are not available due to privacy policies of bank we used here synthetically generated data for a credit card user, and then HMM model is trained using different size of sample of genera...

Credit card fraud detection along with its inherent property of class imbalance is one of the major challenges faced by the financial institutions. Many classifiers are used for the fraud detection of imbalanced data. Imbalanced data withhold the performance of classifiers by setting up the overall accuracy as a performance measure. This makes the decision to be biased towards the majority clas...

Financial fraud is increasing significantly with the development of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. The companies and financial institution loose huge amounts due to fraud and fraudsters continuously try to find new rules and tactics to commit illegal actions. Thus, fraud detection systems have be...

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-...

Due to the rise of technology, the possibility of fraud in different areas such as banking has been increased. Credit card fraud is a crucial problem in banking and its danger is over increasing. This paper proposes an advanced data mining method, considering both feature selection and decision cost for accuracy enhancement of credit card fraud detection. After selecting the best and most effec...

This paper presents a novel method of using Dynamic Soft Descriptor as a fraud prevention method for Merchant (as opposed to card Issuer) in a credit/debit card transaction under Card Not Present (CNP) environment, such as online transactions. A unique identifier is embedded into the transaction descriptor, which will instantly appear in the cardholder’s credit/debit card online statement. By c...

Many factors innuence a learning process and the performance of a learned classiier. In this paper we investigate the eeects of class distribution in the training set on performance. We also study diierent methods of measuring performance based on cost models and the performance eeects of training class distribution with respect to the diierent cost models. Observations from these eeects help u...