Jul 2026· Journal of Dynamics and Control· 1 citation
TL;DR
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.
Abstract
Detection of credit card frauds is a significant activity in the current financial systems with the rates of electronic transactions escalating and the fraudsters upgrading their fraud techniques as they continually develop. The fact that it is also extremely imbalanced with fraudulent transactions making only a small fraction of the overall data only exacerbates the problem. Hence, there is a need to have effective and smart mechanisms of detection that will guarantee the security of transactions and minimize losses incurred due to financial changes. The objective of the paper is to create and test a deep learning-based credit card fraud detector with the help of Artificial Neural Networks and convolutional neural networks architecture. To meet this goal, a real-life credit card transaction dataset was utilized and until a number of data pre-processing methods were employed that comprised; data normalization, dealing with data imbalances with Synthetic Minority Oversampling Technique, and dimensionality reduction with Principal Component Analysis. The practical section of the paper is based on the implementation and the comparison of three deep learning models, namely, Artificial Neural Network, VGG16, and VGG19. The models were trained and evaluated by normal performance measures which were accuracy, precision, recall and F1-score. It has been experimentally demonstrated that Artificial Neural Networks model depicted the greatest detection precision of 99%, more than both the VGG16 and VGG19 models. 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.
The combined oversampling of GAN and SMOTE with the CNN classifier model produces the highest evaluation score, and shows that augmented data quality does affect prediction performance, and Ensemble Oversampling technique could be considered to improve classifier performance in financial fraud data.
Moch Deny Pratama, A. Raharjo, Diana Purwitasari· IPTEK: The Journal for Techn...· 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
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
Experimental results demonstrate that TabNet outperforms traditional neural networks and popular machine learning algorithms by achieving high fraud detection accuracy with significantly reduced false alarms while maintaining excellent interpretability suitable for financial regulatory requirements.
G Srividhya, Dr. S Siva Sankara Rao· International Journal of Eng...· 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
Financial fraud is a growing concern for the global economy, with hundreds of billions of dollars lost every year, and the traditional rule-based fraud detection systems are no longer effective because they are unable to cope with the increasing complexity of fraud schemes. In this paper, we propose FraudShield-XAI, an ensemble learning framework to produce high fraud detection performance and transparent/interpretable decision making, which uses a stacking-based framework consisting of XGBoost, random forests, and an adaptive neural network meta-learner. We evaluate and train the framework using two widely used datasets, the IEEE-CIS Fraud Detection dataset and the PaySim simulated mobile money transactions dataset, and in order to address the class imbalance issue that is typical in fraud detection problems, we apply the Synthetic Minority Oversampling Technique with Edited Nearest Neighbours (SMOTE-ENN). Our experimental results show that FraudShield-XAI outperforms traditional single-model based approaches with an AUC-ROC of 98.72%, an MCC of 0.923, and an F1-score of 97.84%, and we use SHAP and LIME to explain the predictions of FraudShield-XAI, providing the most influential features for each prediction. Identified key factors include transaction velocity, merchant category, and geographical deviations, which can offer actionable insights to compliance teams and fraud analysts, therefore, FraudShield-XAI bridges the performance vs. interpretability gap, which is essential for regulatory approval and real-world deployment in fintech settings.
Soltand Albasha Albasha· Al-Noor Journal of Engineeri...· 0 citations
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