Jul 2026· International Conference Computing Methodologies and Communication· pp. 1725-1731· 0 citations· 19 references
Abstract
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.
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
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
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
The rise of digital payments has magnified credit card fraud issues the complexity, scope and therefore the attack strategies have evolved to become an enormous obstacle for the traditional, static (rule based) and single- modal methods of ML in learning dynamic transactional patterns. In this paper we introduced a multimodal fraud detection approach combining the typical numerical features characterizing single transactional entities with their time- series transaction behaviour so that we could achieve higher accuracy in identifying frauds. The former represents properties for each transaction; temporal patterns in user spending are captured by a Gated Recurrent Unit (GRU), a network effectively modelling sequences of events (purchase history in our context) and then combined to finally perform binary classification in which either fraudulent or real transaction class is detected. We use the public domain 284,807 transaction records from credit card dataset; fraud transactions account for 0.172 of the samples. The experiment shows our novel framework (multimodal GRU) outperformed existing ML algorithms and single-mode techniques, having the recall reach 0.89, F1-score value 0.90 and AUC score 0.99. It’s highly scalable and efficient to use in real time monitoring applications. Our proposed method of combining two types of features: a set of single number characteristics and sequence-based information to successfully solve this challenging problem in modern finance.
J. J, S. S, Sabari Rr· 2026 International Conferenc...· 0 citations
Financial institutions are highly concerned about credit card fraud due to its significant impact on their financial losses. Detecting fraud is challenging as fraudsters are constantly adapting to new technology. To protect customers and businesses, it is crucial to have a robust fraud detection system. This study proposes a novel machine learning approach for detecting credit card fraud using Bayesian Gaussian Mixture Model (BGMM). The study contributes by processing the complex, multi-featured credit card dataset using an Autoencoder model to reduce its dimensionality. The reduced data is clustered in a low-dimensional space using the BGMM to separate fraudulent from normal transactions. The Gibbs sampler algorithm was used to estimate the parameters in the BGMM. The proposed model was evaluated and compared with baseline models, including the Gaussian Mixture Model (GMM) with EM algorithm, K-means, Autoencoder, and Isolation Forest, on two public datasets, Kaggle ULB and IEEE-CIS, using AUC-ROC scores. The results demonstrate that the proposed method achieves the best performance on the Kaggle ULB dataset and competitive, stable performance on the IEEE-CIS dataset, achieving an AUC-ROC score of 0.9672 on the Kaggle ULB dataset and 0.5422 on the IEEE-CIS dataset under a leakage-free unsupervised learning protocol. The adoption of the proposed approach will aid financial institutions and credit card companies in enhancing their fraud detection systems, resulting in greater accuracy and efficiency in detecting fraudulent transactions.
Suboh M. Alkhushayni, Du’a Al-zaleq, M. Alsmadi et al.· Discover Artificial Intellig...· 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
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