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Credit Card Fraud Detection System

1962 Words8 Pages

ABSTRACT One of the issues facing credit card fraud detection systems is that a significant percentage of the transactions labeled as fraudulent are in fact legitimate[1]. These “false alarms” delay the detection of fraudulent transactions. Analysis of 11 months of credit card transaction data from major banks was conducted to determine savings improvements that can be achieved by identifying truly fraudulent transactions. A meta-classifier model was used in this research. This model consists of 3 base classifiers constructed using the k-nearest neighbor, decision tree, and naïve Bayesian algorithms. The naive Bayesian algorithm was also used as the meta-level algorithm to combine the base classifier predictions to produce the final classifier[2]. …show more content…

Businesses are always susceptible to internal fraud or corruption from its management or employees. While external fraud is mainly about using the stolen, fake or counterfeit credit card to consume or obtain cash in disguised forms. This work is focused on the investigation of the external card fraud, which accounts for the majority of credit card frauds. Credit card fraud can be either an offline fraud or online fraud. Offline fraud is a stolen physical card at a storefront or call center. The institution issuing the card can lock the account before it is used in a fraudulent manner. Online fraud is committed via web, phone shopping or cardholder-not-present situations. The main objective in fraud detection is to identify fraud as quickly as possible once it is committed. The purpose of this work is to apply data mining strategies to a unique and to investigate whether a meta-learning strategy (a combination methodology) has the potential to save money and improve fraud detection[4]. This work primarily aims to improve current fraud detection processes by improving the prediction of fraudulent …show more content…

Clearly, global networking presents as many new opportunities for criminals as it does for businesses. While offering numerous advantages and opening up new channels for transaction business, the internet has also brought in increased probability of fraud in credit card transactions. The good news is that technology for preventing credit card frauds is also improving many folds with passage of time. Reducing cost of computing is helping in introducing complex systems, which can analyze a fraudulent transaction in a matter of fraction of a second. It is equally important to identify the right segment of transactions, which should be subject to review, as every transaction does not have the same amount of risk associated with it. Finding the optimally balanced ‘total cost of fraud’ and other measures outlined in this article can assist acquiring and issuing banks in combating frauds more efficiently. It has also been explained how the HMM can detect whether an incoming transaction is fraudulent or not . Experimental results show the performance and effectiveness of our system and demonstrate the usefulness of learning the spending profile of the cardholders. the ranges of transaction amount as the observation

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