The FC-ANN (Artificial Neural Network) is used to speed up the technique. The anomaly Outlier detection is primary in various data-mining applications. Outlier detection methods have been suggested for number of application such as, fraud detection, voting irregularity analysis, data cleansing, clinical trials, network intrusion, severe weather prediction, geographic information system, credit ...

Nowadays, <span>credit card fraud has emerged as a major problem. People are becoming increasingly using credit cards to pay for their transactions, it become more popular and essential in our lives. Fraudsters developing new strategies techniques over time, is not easy humans manually check out all transactions. The cost of fraudulent transactions significant without prevention mechanism...

In two studies, time preferences for financial gains and losses at delays of up to 50 years were elicited using three different methods: matching, fixed-sequence choice titration, and a dynamic “staircase” choice method. Matching was found to create fewer demand characteristics and to produce better fits with the hyperbolic model of discounting. The choice-based measures better predicted real-w...

Financial innovations are a common explanation for the rise in credit card debt and bankruptcies. To evaluate this story, we develop a simple model that incorporates two key frictions: asymmetric information about borrowers’ risk of default and a fixed cost of developing each contract lenders offer. Innovations that ameliorate asymmetric information or reduce this fixed cost have large extensiv...

INTRODUCTION As a conseguence of expansion of modern technology , the number and scenario of fraud are increasing dramatically. Therefore, the reputation blemish and losses caused are primary motivations for technologies and methodologies for fraud detection that have been applied successfully in some economic activities. The detection involves monitoring the behavior of users based on huge dat...

Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. The implementation of data mining techniques for fraud detection follows the traditional information flow of data mining, which begins with feature selection followed by representation, da...

One of the issues facing credit card fraud detection systems is that a significant percentage of transactions labeled as fraudulent are in fact legitimate. These "false alarms" delay the detection of fraudulent transactions and can cause unnecessary concerns for customers. In this study, over 1 million unique credit card transactions from 11 months of data from a large Canadian bank w...

In India, the credit card usage as well as the penetration has been found to be very low compared to that of debit cards. Though banks offer the best of the services, still there are apprehensions about the risks involved in using a credit card in India. Hence, this study investigates the customer’s adoption behavior of credit cards and determines the factors which influence the acceptance amon...

We survey known security vulnerabilities in Massively Multiplayer Online Games (MMOGs), and describe how these are used to cheat. While such abuse often is aimed at gaining an edge in the game, there is a recent trend of financial fraud in MMOGs. We review common types of online fraud (such as phishing and click-fraud) that we believe increasingly will migrate into the MMOG sphere. We refer to ...

This paper addresses an effective learning method that enables us to directly optimize neural network classifier's discrimination performance at a desired local operating range by maximizing a partial area under a receiver operating characteristic (ROC) or domain-specific curve, which is difficult to achieve with classification accuracy or mean squared error (MSE)-based learning methods. The ef...