2026· International journal of research and scientific innovation· Vol 13, pp. 2087-2108· 0 citations
TL;DR
This study proposes an Explainable Artificial Intelligence (XAI)-based fraud detection framework for FinTech transactions using the Kaggle Credit Card Fraud Detection dataset containing 284,807 transactions, including 492 fraudulent cases.
Abstract
The rapid growth of digital financial services has significantly increased the volume of online transactions, making fraud detection a critical challenge for financial institutions. Traditional machine learning models often provide strong predictive performance but lack interpretability, limiting trust and practical adoption in financial decision-making. This study proposes an Explainable Artificial Intelligence (XAI)-based fraud detection framework for FinTech transactions using the Kaggle Credit Card Fraud Detection dataset containing 284,807 transactions, including 492 fraudulent cases. To address severe class imbalance, Synthetic Minority Oversampling Technique (SMOTE) was applied, increasing the dataset to 568,630 balanced instances. Data preprocessing involved feature scaling and train–test splitting prior to model training. Three machine learning algorithms—Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGBoost) were developed and evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics.
The experimental results demonstrate strong predictive performance across all models. Logistic Regression achieved 94.50% accuracy, 97.32% precision, 91.51% recall, 94.33% F1-score, and a ROC-AUC of 94.50%. Random Forest produced the highest overall performance with 99.99% accuracy, 99.98% precision, 100.00% recall, 99.99% F1-score, and 99.99% ROC-AUC. XGBoost also achieved excellent results with 99.97% accuracy, 99.94% precision, 100.00% recall, 99.97% F1-score, and 99.97% ROC-AUC. To improve model transparency, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) were integrated with the XGBoost model to provide both global and local interpretability. SHAP analysis identified transaction amount and several transformed principal component features as the most influential predictors of fraudulent behavior, while LIME provided instance-level explanations for individual fraud predictions. Feature importance analysis from Random Forest and XGBoost further validated the consistency of the most influential variables.
The findings demonstrate that combining high-performing machine learning models with explainable AI techniques can significantly enhance fraud detection accuracy while maintaining transparency and interpretability. The proposed framework offers a reliable and practical approach for intelligent fraud prevention in financial technology systems and supports trustworthy decision-making in real-world financial environments.
Credit card fraud is a menace to financial institutions, but detection is compromised by highly imbalanced transaction datasets. This study proposes an advanced machine learning framework optimized for fraud detection. To address the issue of data imbalance, SMOTE-Tomek Links is applied to synthetically generate minority fraud cases while removing noisy, overlapping majority-class instances. Recursive Feature Elimination (RFE) is deployed to identify the optimal features, and RandomizedSearchCV automates hyperparameter optimization. The study introduces a Stacked Logistic Regression ensemble to combine the predictive capacity of optimized Random Forest and XGBoost base classifiers. The model’s effectiveness is assessed using seven evaluation methods: accuracy, recall, precision, confusion matrix, F1-score, Receiver Operating Characteristic Area Under the Curve (ROC-AUC) score and the Area Under the Precision-Recall Curve (AUC-PR) score. Findings reveal that the proposed stacked model performance surpasses both individual base models. While achieving deceptively high baseline accuracy across all models, the stacked ensemble delivers a superior AUC-PR score of 0.8207 and an F1-score of 0.93. This minimizes the confusion matrix misclassifications to just 20 False Negatives and 5 False Positives. The framework provides a cost-optimized operational engine that aggressively mitigates bank fraud losses while successfully shielding legitimate cardholders from accidental checkout declines.
Uduh Israel Akakoh, G. N. Edegbe· FUDMA Journal of Sciences· 0 citations
Financial fraud has become a major problem in modern digital transactions. Traditional fraud detection systems mainly focus on prediction accuracy, but they do not clearly explain why a transaction is marked as fraudulent. This creates trust and transparency issues in banking and financial applications. To solve this problem, this research paper proposes FraudXAI-Net, an explainability-driven intelligent fraud detection framework that combines machine learning and Explainable Artificial Intelligence (XAI). The proposed framework performs data preprocessing, feature scaling, class imbalance handling, model training, fraud prediction, and explainability analysis. Multiple machine learning models such as Logistic Regression, Random Forest, and XGBoost are used for fraud classification. SHAP (Shapley Additive Explanations) is applied to identify the contribution of each feature in prediction results. The framework improves both fraud detection performance and model interpretability. Experimental results show that the XGBoost model achieved the best performance with high accuracy, precision, recall, and F1-score. The SHAP-based explainability method successfully highlighted the important features responsible for fraudulent transactions. The proposed FraudXAI-Net framework can help financial institutions build more reliable, transparent, and intelligent fraud detection systems.
Jakkula Jayanthi· International Journal of Res...· 0 citations
Financial fraud detection presents a significant challenge due to the extreme class imbalance inherent in transaction datasets. This paper presents a comprehensive evaluation of deep learning architectures for fraud detection in structured transaction data, with a focus on model performance, interpretability, and operational relevance. Specifically, we compare three deep tabular models, namely, an advanced multilayer perceptron (AdvancedMLP), an attention‐based residual network (AttentionFraudNet), and an advanced residual network (AdvancedResNet), against three traditional machine learning baselines, including Random Forest, Gradient Boosting, and Logistic Regression. The experiments are conducted on the BankSim Simulator dataset, comprising 594,643 transactions with a fraud rate of approximately 1.21%. We evaluate models using multiple metrics, including PR‐AUC, ROC‐AUC, recall at low false positive rates, and precision at various operating thresholds. Our results show that AdvancedMLP achieves the highest PR‐AUC (0.8921 ± 0.0020), while Gradient Boosting attains the best ROC‐AUC (0.9987 ± 0.0001) and the highest recall at 1% FPR (0.9498 ± 0.0087). AttentionFraudNet provides interpretable attention weights that highlight transaction‐level features contributing to fraud predictions, enabling fraud analysts to understand and validate model decisions. Business impact analysis reveals that the proposed models can reduce fraud losses while maintaining false positive rates below 1%, translating to estimated annual savings of approximately 49.2 M$ for a mid‐sized financial institution. All models achieve inference times below 1 ms per transaction on an NVIDIA RTX 3090 GPU, demonstrating computational feasibility for near‐real‐time screening. Our findings highlight the effectiveness of deep tabular models for fraud detection and provide practical guidance for selecting models based on operational requirements and performance trade‐offs.
Vahid Azarvand, Parvin Azhdari, A. Beitollahi· Engineering Reports· 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
Credit card fraud remains a major challenge for financial institutions, both financially and operationally, as digital transactions continue to grow and fraud datasets remain highly imbalanced. This study compares the performance of several supervised machine learning models for fraud detection, using a unified data preprocessing pipeline. The approach includes removing duplicates, applying RobustScaler normalization, engineering features and using the Synthetic Minority Oversampling Technique (SMOTE) to balance classes before training. Four models were developed and tested Logistic Regression, Decision Tree, Random Forest and Artificial Neural Network (ANN) using the publicly available Kaggle Credit Card Fraud Detection dataset. Their performance was measured with Accuracy, Precision, Recall, F1-score and ROC-AUC metrics. Results showed that thorough preprocessing combined with SMOTE significantly improved the models ability to detect fraudulent transactions. Among them, the Random Forest model delivered the strongest overall performance, proving especially effective at handling highly imbalanced financial data. The comparative analysis also highlighted that ensemble learning methods generally outperform single classifiers in both accuracy and minority-class recognition. These findings emphasize the importance of pairing robust preprocessing strategies with machine learning techniques to boost fraud detection in real-world financial systems. The proposed system offers institutions a scalable and practical solution for building intelligent fraud detection systems, while laying the groundwork for future integration of Explainable AI (XAI) and real-time detection tools.
Nafiu Yahuza, Ahmad Baita Garko, Abubakar Atiku Muslim et al.· Lead Sci Journal of Manageme...· 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
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