Credit card fraud is an inclusive term for fraud committed using a payment card, such as a credit card or debit card. The purpose may be to obtain goods or services or to make payment to another account that is controlled by a criminal.
Credit card fraud can be authorized, where the genuine customer themselves processes payment to another account which is controlled by a criminal, or unauthorized, where the account holder does not provide authorization for the payment to proceed and the transaction is carried out by a third party.
Credit cards are more secure than ever, with regulators, card providers and banks taking considerable time and effort to collaborate with investigators worldwide to ensure fraudsters aren’t successful.
Causes of Credit Card Fraud
Credit card fraud is usually cause either by card owner’s negligence with his data or by a breach in a website’s security. Here are some examples:
A consumer reveals his credit card number to unfamiliar individuals.
A card is lost or stolen and someone else uses it.
Mail is stolen from the intended recipient and used by criminals.
Business employees copy cards or card numbers of its owner.
Card information is stored in a number of formats. Card numbers – formally the Primary Account Number (PAN) – are often emboss or visible on the card, and a magnetic stripe on the back contains the data in machine-readable format.
Fields can vary, but the most common include: Name of card holder; Card number; Expiration date; and Verification CVV code.
However, a PIN isn’t require for online transactions. In some European countries, if you don’t have a card with a chip, you may be asked for photo-ID at the point of sale.
However, a stolen credit or debit card could be use for a number of smaller transaction prior to fraudulent activity being flag.
Card issuers maintain several countermeasures, including software that can estimate the probability of fraud. For example, a large transaction occurring a great distance from the cardholder’s home might seem suspicious.
Credit Card Fraud Detection Systems
Credit Card Fraud Detection using Machine Learning Algorithms
CCF (Credit Card Fraud) are easy and friendly targets. E-commerce and many other online sites have increased the online payment modes, increasing the risk for online frauds.
Increase in fraud rates, researchers started using different machine learning methods to detect and analyse frauds in online transactions.
Where cardholders are cluster into different groups based on their transaction amount.
Then using sliding window strategy , to aggregate the transaction made by the cardholders from different groups so that the behavioral pattern of the groups can be extracted respectively. Later different classifiers ,,, are trained over the groups separately.
And then the classifier with better rating score is choose to be one of the best methods to predict frauds. Thus, followed by a feedback mechanism to solve the problem of concept drift. In this paper, we worked with European credit card fraud dateset.
Credit Card Fraud Detection: Based on Bagging Ensemble Classifier
Credit card fraud costs consumers and the financial company billions of dollars annually, and fraudsters continuously try to find new rules and tactics to commit illegal actions.
Thus, fraud detection systems have become essential for banks and financial institutions, to minimize their losses. However, there is a lack of published literature on credit card fraud detection techniques, due to the unavailable credit card transactions dataset for researchers.
Additionally, the most commonly techniques used fraud detection methods are Naïve Bayes (NB), Support Vector Machines (SVM), K-Nearest Neighbor algorithms (KNN). These techniques can be use alone or in collaboration using ensemble or meta-learning techniques to build classifiers.
After several trial and comparisons; we introduced the bagging classifier based on decision three, as the best classifier to construct the fraud detection model.
The performance evaluation is perform on real life credit card transactions dataset to demonstrate the benefit of the bagging ensemble algorithm.
Credit Card Fraud Detection System Using Adaptive Data Mining And Intelligent Agents
The growth in electronic transactions has resulted in a greater demand for fast and accurate user identification and authentication. Conventional method of identification based on possession of pin and password are not all together reliable.
As a result, credit card payment systems must be in support by efficient fraud detection capability for minimizing unwanted activities by fraudster’s. Most of the well-known algorithms for fraud detection are based on supervised training.
Every cardholder has a certain shopping behavior, which establishes an activity profile for him. Existing fraud detection systems try to capture behavioral patterns as rules which are static.
This becomes ineffective when cardholder develops new patterns.
Credit Card Fraud Detection System in Commercial Sites
In modern retail market, electronic commerce has rapidly gained a lot of attention and also provides instantaneous transactions.
The objective of the paper is to develop a credit card fraud detection system in commercial sites.
It is design as a web based application in which transition state model was adopted for the research process. PHP (Hypertext Pre-Processor) is use for application development and MySQL to generate databases.
The result shows that the system performance is performing to its task. Therefore recommended to electronic commerce owners to ensure data integrity and security of their customers.
Fraud is a major problem for the whole credit card industry that grows bigger with the increasing popularity of electronic money transfers. Credit card issuers should consider the implementation of advanced Credit Card Fraud Prevention and Detection methods.
Additionally, machine Learning-based methods can continuously improve the accuracy of fraud prevention solutions according to information about each cardholder’s behavior. Was this article useful? If Yes! Please leave your comment. For more related articles, subscribe to our web page.