Credit card Fraud Detection based on Machine Learning Algorithms
15 Mar 2019-International Journal of Computer Applications (Foundation of Computer Science (FCS), NY, USA)-Vol. 182, Iss: 44, pp 8-12
About: This article is published in International Journal of Computer Applications.The article was published on 2019-03-15 and is currently open access. It has received 17 citations till now. The article focuses on the topics: Credit card fraud.
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13 May 2020
TL;DR: The Random Forest and the Adaboost algorithms are compared and the algorithm that has the greatest accuracy, precision, recall, and F1-score is considered as the best algorithm that is used to detect the fraud.
Abstract: Credit card fraud detection is presently the most frequently occurring problem in the present world. This is due to the rise in both online transactions and e-commerce platforms. Credit card fraud generally happens when the card was stolen for any of the unauthorized purposes or even when the fraudster uses the credit card information for his use. In the present world, we are facing a lot of credit card problems. To detect the fraudulent activities the credit card fraud detection system was introduced. This project aims to focus mainly on machine learning algorithms. The algorithms used are random forest algorithm and the Adaboost algorithm. The results of the two algorithms are based on accuracy, precision, recall, and F1-score. The ROC curve is plotted based on the confusion matrix. The Random Forest and the Adaboost algorithms are compared and the algorithm that has the greatest accuracy, precision, recall, and F1-score is considered as the best algorithm that is used to detect the fraud.
29 citations
TL;DR: Intelligent machine learning based fraudulent transaction detection systems are very effective in real-world scenarios but need more research and development to be able to be truly effective in future.
Abstract: Credit card fraud is one of the biggest cybercrimes faced by users. Intelligent machine learning based fraudulent transaction detection systems are very effective in real-world scenarios. However, ...
28 citations
TL;DR: Experimental findings show that the suggested model can improve the precision of ranking against the danger of suspect operations and provide higher accuracy relative to traditional techniques.
Abstract: The widened uses of Internet credit cards in e-banking systems are currently prone to credit card fraud. Data imbalance also poses a significant difficulty in the method of fraud detection. The efficiency of the existing fraud detection systems is only in question because it detects fraudulent action after the suspect transaction has been completed. To address these difficulties, this article offers an improved two-level credit card fraud tracking model from imbalanced datasets based on the semantic fusion of k-means and the artificial bee colony (ABC) algorithm to improve identification precision and accelerate the convergence of detection. In the proposed model, ABC works as a kind of neighborhood search associated with a global search to be a second classification level to manage the failure of the k-means classifier to explore the actual clusters as it is sensitive to the initial condition. The proposed model filters the characteristics of the dataset using an integrated rule engine to evaluate whether the operation is real or false, depending on many parameters of client conduct (profile) such as geographical locations, usage frequency, and book balance. Experimental findings show that the suggested model can improve the precision of ranking against the danger of suspect operations and provide higher accuracy relative to traditional techniques.
17 citations
25 Mar 2022
TL;DR: Credit Card Fraud Detection that employ Machine Learning Techniques comes to the rescue, which is a data investigation procedure carried out by a Data Science team, with the model generated providing the greatest outcomes in stopping fraudulent transactions.
Abstract: In this new generation, each and everything is done online and most of the time the payment is performed via the internet using net banking or a credit card. Debit card and credit card plays a major part in day-to- day life. The total amount of money transfers through online has a great amount of growth. Fraudulent transactions have escalated as E-commerce continues to expand at a rapid pace. Therefore banks, financial institutions and many other companies offer credit card fraud detection applications with more demand, and it adds more value to the applications. To reduce the transactions that are fraud, Credit Card Fraud Detection that employ Machine Learning Techniques comes to the rescue, which is a data investigation procedure carried out by a Data Science team, with the model generated providing the greatest outcomes in stopping fraudulent transactions.
1 citations
TL;DR: The main aim of this research is to focus on machine learning methods and the algorithms used are unsupervised learning algorithms.
Abstract: Credit card fraud detection is one of the prominent problem in today's world. It is due to the extensive rise in both online and e-commerce transactions. The fraud happens when the users’ accessible card gets stolen from any unauthorized source or the use of credit card for fraudulent purposes. The present scenario is facing this kind of problem. So to detect the unethical activity, the credit card detection system was introduced. The main aim of this research is to focus on machine learning methods. So the algorithms used are unsupervised learning algorithms.
1 citations