Jul 2026· International Journal of Innovative Science and Research Technology· 0 citations· 10 references
TL;DR
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.
Abstract
The rapid growth of digital banking, online transactions, and the electronic payment of the systems has been
significantly increased the use of credit cards, results in the parallel rise in fraudulent activities. Traditional methods of
detecting the fraud are often unable to handle large-scale transaction data and evolving fraud patterns efficiently. This paper
presents an AI-Based Credit Card Fraud Detection System using Machine Learning to identify suspicious transactions
accurately and in the real time. To identify patterns and detect potential fraud, the proposed system analyzes past
transaction data containing various transaction-related attributes amount, merchant details, category, time, and customer
information. Data preprocessing techniques including K-Nearest Neighbor (KNN) imputation are applied to handle missing
values, followed by label encoding and Z-score normalization to improve data quality and consistency. Feature engineering
is performed to extract meaningful transaction patterns. The processed data is then classified using the XGBoost algorithm,
which improves prediction accuracy through sequential learning and optimized decision trees. Experimental evaluation is
carried out using a credit card transaction dataset containing legitimate and fraudulent records.
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
This project presents an ML-Based Real-Time UPI Fraud Detection System that uses machine learning algorithms to identify fraudulent transactions efficiently and shows that the Random Forest algorithm achieves the highest accuracy, making it the most effective model for fraud detection.
Yekkirala Suvarcha, K. M., G. Prasad· International Scientific Jou...· 0 citations
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 rapid evolution of digital banking and e-commerce puts online payment fraud problems at another level. Because of the large class imbalance present, coupled with the ever-changing nature of fraud, in the instance of credit card fraud, it becomes doubly challenging to achieve real-time detection. Fraudsters continuously evolve and modify their strategies to capture the weaknesses in the digital banking system. To contribute to this problem, this paper provides an approach to detect credit card fraud in the context of machine learning, with a comprehensive set of data preparation methods, the Synthetic Minority Over-sampling Technique (SMOTE) for class imbalance, normalization, feature selection by the information gain, and a hybrid approach using two different supervised learning techniques. The study is based on a readily accessible dataset of European credit card transactions from September 2013, which is composed of 284,807 transactions, of which only 492 (0.172%) are considered to belong to the fraudulent class. In this approach, a Support Vector Machine (SVM) is used to define the critical decision boundaries, and probabilistic outputs from the SVM are used as input to train an artificial neural network (ANN). This hybrid SVM-ANN approach leverages the strengths of both models and improves the classification performance using margin-based learning and nonlinear representation technologies. The study tackles some of the major issues of fraud detection, such as the use of feature sets transformed by PCA for confidentiality, the evaluation of metrics for models that capture rare events, and the generalization of models to be used in imbalanced situations. The proposed approach performs most other traditional classifiers, as evidenced by the experiments and the stated evaluation metrics, which include accuracy, precision, F-measure, and recall. The results show how important it is to use hybrid modeling and strong preprocessing techniques to build scalable and reliable systems for credit card fraud detection.
Firdos R. Kachur, Shivangi Agarwal, Gargi Phadke· International Conference Com...· 0 citations
A UPI Fraud Detection System using the Random Forest machine learning algorithm that helps banks and digital payment platforms identify suspicious transactions quickly, reducing financial losses and improving user security.
Avu Siva Dinesh Kumar, G. Kumar· International Journal for Re...· 0 citations
Credit card fraud detection is widely recognized as a critical issue in digital financial systems due to the rapid growth of online transactions. However, existing approaches often fail to provide a comprehensive comparative framework and show limited effectiveness under highly imbalanced conditions. In this context, this study proposes a systematic machine learning pipeline to analyze fraudulent transactions using the IEEE-CIS dataset (590,000 records, 3–4% fraud). The approach integrates data preprocessing, feature encoding, and stratified sampling, followed by the evaluation of multiple supervised models including Logistic Regression, Decision Tree, Random Forest, SVM, Naive Bayes, and XGBoost. Performance is assessed using Accuracy, Precision, Recall, F1-score, and ROC-AUC. The results indicate that ensemble-based methods outperform traditional classifiers. In particular, XGBoost achieves the highest performance (accuracy 98.22%, ROC-AUC 0.9545, F1-score 0.6759), suggesting a strong ability to handle class imbalance. Nevertheless, no model fully optimizes both precision and recall, highlighting a key limitation in current fraud detection systems. Taken together, these findings suggest that ensemble learning provides a robust baseline, while further improvements through hybrid and cost-sensitive approaches are required for effective real-world deployment.
