The prevention of credit card fraud is an important application for prediction techniques. One major obstacle for using neural network training techniques is the high necessary diagnostic quality: Since only one financial transaction of a thousand is invalid no prediction success less than 99.9% is acceptable. Due to these credit card transaction proportions complete new concepts had to be deve...

Online shopping, already on a steady rise, was propelled even further with the advent of COVID-19 pandemic. Of course, credit cards are dominant way doing business online. The card fraud detection problem has become relevant more than ever as losses due to accumulate. Most research this topic takes an isolated, focused view problem, typically concentrating tuning data mining models. We noticed ...

Credit card fraud detection has been one of the major necessities of the current e-commerce based world. The ease of use provided by e-commerce transactions is hindered by the threat caused by fraudsters. Several models have been proposed for identifying fraudulent transaction in a credit card system. However, the threats still do tend to exist. This paper discusses and analyzes the major reaso...

Credit card is one of the convenient way of payment in online shopping. In this on line shopping, payment is made by giving information like card no, security code , expiration date of the Credit card etc . To rectify the risk factors of using the credit card, every card holder’s spending method is modeled by using HMM. By using this authenticated security check the information of transaction i...

In recent years, with the rapid development of Internet technology, number credit card users has increased significantly. Subsequently, fraud caused a large amount economic losses to individual and related financial enterprises. At present, traditional machine learning methods (such as SVM, random forest, Markov model, etc.) have been widely studied in detection, but these are often difficulty ...

Association rules are considered to be the best studied models for data mining. In this article, we propose their use in order to extract knowledge so that normal behavior patterns may be obtained in unlawful transactions from transactional credit card databases in order to detect and prevent fraud. The proposed methodology has been applied on data about credit card fraud in some of the most im...

This paper introduces a new credit card payment scheme called No Number Credit Card that can significantly reduce the possibility of credit card fraud. The proposed payment system is loosely based on Kerberos, a cryptographic framework that has stood the test of time. In No Number Credit Card, instead of card numbers, only payment tokens are exchanged between the customers and merchants. The to...

Outlier is defined as an observation that deviates too much from other observations. The identification of outliers can lead to the discovery of useful and meaningful knowledge. Outlier detection has been extensively studied in the past decades. However, most existing research focuses on the algorithm based on special background, compared with outlier detection approach is still rare. Most soph...

Worldwide, billions of euros are lost every year due to credit card fraud. Increasingly, fraud has diversified to different digital channels, including mobile and online payments, creating new challenges as innovative new fraud patterns emerge. Hence, it remains challenging to find effective methods of mitigating fraud. Existing solutions include simple if-then rules and classical machine learn...

In this study we develop a method which improves a credit card fraud detection solution currently being used in a bank. With this solution each transaction is scored and based on these scores the transactions are classified as fraudulent or legitimate. In fraud detection solutions the typical objective is to minimize the wrongly classified number of transactions. However, in reality, wrong clas...