Jul 2026· International Conference Computing Methodologies and Communication· pp. 1624-1631· 0 citations· 20 references
Abstract
Due to the rapid advancement of digital payment tools, the number of credit card fraud cases is increasing at a faster rate, posing a greater risk to users and financial institutions and highlighting the flaws of traditional rule-based preventive measures. The greatest challenge in this area is that transaction data is extremely unbalanced, with cases of fraud representing only a small portion of the total, and transaction fraudsters continuously develop new methods to overcome specific limits. This paper presents a comparison of popular supervised machine learning models used for detecting credit card fraud. A significant data set of transactions was treated with logistic regression (LR), support vector machines (SVM), random forests (RF), and artificial neural networks (ANN), with imbalance-holding methods, undersampling and oversampling to provide a fair judgment. These findings suggest that the ensemble models, in general, RF, were able to outperform individual classifiers in the sense of higher rates of detecting fraud, with low rates of false positives, resulting in high sensitivity and specificity even in highly biased statistics. Altogether, the discussion shows that ML-driven systems are much more versatile and reliable compared to conventional strategies and provide financial organisations with the means of minimising risks of fraud and protecting payment systems.
Now the days world become digital, credit card customers have become common; it makes the payment hassle-free. With the ease of use of credit cards; Fraudulent use of credit cards is growing as a significant affair for financial institutions and consumers on an international level. Traditional rule-based detection algorithms are ineffective in determining a transaction's fraudulent nature. First and foremost, it is imperative to comprehend the pattern of fraudulent activities. The current study explores various supervised machine-learning algorithms to analyze patterns and predict the fraudulent nature of transactions in a large dataset used for training the model. The effectiveness of different methods is assessed by comparing their accuracy, precision, F1score, and recall. In the present paper, we discuss the techniques named KNN, SVM(Support Vector Machine), Logistic Regression, Gradient Boosting, Neural Network, XG Boost, Naïve Bayes, Ada Boost, Decision Forest, and Random Forest.
S. Bansal, Reena Hooda, Rohit Yadav· Journal of Commerce, Economi...· 0 citations
The study shows that machine learning can be useful for fraud detection when it is combined with suitable preprocessing, class-imbalance techniques, and careful evaluation.
Jabulani Khumalo, Min Joon Kim· Global Knowledge Academy· 0 citations
With the rapid growth in the use of credit cards in electronic payments, financial institutions and financial service providers are becoming vulnerable to fraud, which leads to huge losses every year. The development and implementation of an effective credit card fraud detection system is essential to reduce such losses. The presented paper analyzes current scientific work in the field of developing methods and models of artificial intelligence to ensure the security of financial transactions. The purpose of this paper is to review and compare machine learning models and methods for conducting secure financial transactions using credit cards. The above publications in this area mainly use a data set on fraudulent credit card transactions collected from European cardholders. It also mentions publications that use both synthetic and other datasets. Among the machine learning algorithms used in these publications, the effectiveness of decision trees, random forests, SVM, logistic regression and other methods on anonymized credit card fraud data, as well as algorithms using neural networks, is investigated and tested. The researchers apply these methods to preprocessed data samples. To assess the quality of the machine learning model, various special metrics are considered in classification tasks, such as accuracy, completeness, F-measure, etc. А comparative analysis of these publications has revealed several of the most preferred and effective methods for processing financial transactions.
A. Kozlov, M. V. Smirnov· INFORMACIONNYE TEHNOLOGII· 0 citations
These findings validate that deep learning techniques can be used to detect fraudulent credit card transactions and deployed in real time systems of fraud detection.
Deepika Tiwari, Meenakshi Nawal, N. Neeraj et al.· Journal of Dynamics and Cont...· 1 citation
An AI-Based Credit Card Fraud Detection System using Machine Learning to identify suspicious transactions accurately and in the real time and improves prediction accuracy through sequential learning and optimized decision trees.
P. Ravikumar, Gowrav A. S., A. N et al.· International Journal of Inn...· 0 citations
The fact that banks may find fraud, minimize risks before they happen, and make their clients happy by combining ML and CRM standard data models together in an effective manner is demonstrated.
Satyendra Kumar Vanapalli· International Journal of Mac...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
