Aug 2026· FUDMA Journal of Sciences· 0 citations· 3 references
TL;DR
The study introduces a Stacked Logistic Regression ensemble to combine the predictive capacity of optimized Random Forest and XGBoost base classifiers and reveals that the proposed stacked model performance surpasses both individual base models.
Abstract
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.
This work provides a mathematically grounded benchmarking framework for integrating Explainable Artificial Intelligence (XAI) into fraud detection pipelines, aligning high-accuracy analytics with the transparency requirements expected in regulated financial environments.
Henrique Barros, F. Antunes, Maryam Abbasi· International Conference on...· 0 citations
Three deep tabular models, namely, an advanced multilayer perceptron (AdvancedMLP), an attention‐based residual network (AttentionFraudNet), and an advanced residual network (AdvancedResNet), are compared against three traditional machine learning baselines, including Random Forest, Gradient Boosting, and Logistic Regression.
Vahid Azarvand, Parvin Azhdari, A. Beitollahi· Engineering Reports· 0 citations
In this paper, a forensic end-to-end machine learning pipeline to detect financial fraud in PaySim synthetic mobile money dataset a corpus of 6,362,620 transactions with an extreme class imbalance ratio of 0.129% is presented. Proposed system is based on the distributed MLlib framework of Apache Spark in order to overcome the scalability issues associated with processing millions of financial records. Multicollinear raw ledger features are substituted with seven theoretically-based feature engineering transformations such as accounting discrepancy indices and account drain ratios as well as high-risk categorical flags. Ten classifiers such as Logistic Regression, Decision Tree variants, Random Forest ensembles, Gradient Boosted Trees, Linear SVC and a Factorization Machine are considered in a forensic model tournament. The imbalance between classes is alleviated through random undersampling to a balanced 50:50 training sample. The Matthews Correlation Coefficient (MCC) is the statistically rigorous evaluation index of the imbalanced binary classification that allows model selection. The winning ensemble model has a Recall of 99.39%, a Precision of 100.00%, an MCC of 0.9941 and a Financial Protection Index of over 96% which shows the operational reliability and good business value in preserving capital. Limitations regarding the synthetic nature of the data, the absence of sequence modeling, the schema dependence of the engineered features and the batch nature of the deployment are also discussed.
Anuran Baruah, Sahithya Sambhrama, K. S· 2026 4th International Confe...· 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
Financial fraud presents a major challenge to financial establishments, with Nigerian banks losing over ₦685 million to digital fraud in 2023. Traditional rule-based detection systems have high false-positive rates and limited adaptability; meanwhile, existing machine learning models are generally trained on non-localized datasets that ineffectively represent African fintech ecosystems. This study proposes a Hybrid Stacked Ensemble framework for fraud detection that improves detection accuracy, robustness, and explainability in localized financial environments. The proposed framework combines Random Forest, Gradient Boosting, and Extra Trees as base learners with XGBoost as the meta-classifier and integrates SHAP for model explainability. Performance was evaluated using the Kaggle credit card fraud dataset (284,807 transactions; 0.17% fraud) and a newly curated Nigerian synthetic dataset (150,000 transactions; 1.12% fraud) incorporating localized fraud patterns such as POS, USSD, mobile money, and rural–urban transaction disparities. Class imbalance was addressed using SMOTE oversampling, random undersampling, and cost-sensitive learning. On the Kaggle dataset, the Hybrid Ensemble achieved 99.96% accuracy, 97.14% precision, 79.70% recall, an F1-score of 0.880, and an AUC-ROC of 0.986, outperforming the best individual classifier in recall and AUC-ROC. On the Nigerian dataset, where individual classifiers achieved recall below 3.1%, the proposed framework attained 64.10% recall, an F1-score of 0.460, and an AUC-ROC of 0.866, representing improvements of 106.77% in recall and 488.57% in F1-score over XGBoost. Ablation studies and paired t-tests (p < 0.001) confirmed the effectiveness of the stacking strategy. The study contributes a localized Nigerian fraud dataset, a hybrid stacked ensemble architecture that exploits classifier diversity for improved fraud detection, and an explainable AI framework that enhances transparency, accountability, and regulatory compliance. Deployment as a containerized Streamlit application with JWT authentication demonstrates the framework's practicality as a scalable, explainable, and deployment-ready solution for fraud detection in Nigeria and similar African financial ecosystems.
Jumoke Soyemi, Jamiu R. Olasina· International Journal of Wir...· 0 citations
Digital payment services now handle millions of transactions each day, where even a tiny
fraction of fraud causes major financial losses and undermines customer trust. This paper
investigates how to accurately detect fraudulent transactions in a Kaggle financial payment
services dataset and to understand which transaction behaviors make payments appear risky.
The original dataset is pre-processed through outlier removal, label encoding, standardisation
and two stages balancing strategy combining random down sampling of the majority class with
SMOTE oversampling. Nine numeric and categorical features are then used as inputs to
classifiers including LR, KNN, DT, RF, SVM, GNB, AdaBoost, Bagging, Voting and Stacking
under four settings. They are no dimensionality reduction (NoDR), UMAP, NCA and PLS-DA
respectively. Hyperparameters are tuned with GridSearchCV using both 70/30 train–test split
and 10-fold cross-validation. Model performance is evaluated with accuracy, precision, recall,
F1-score, specificity, ROC-AUC, PR-AUC and training time. SHAP is applied to interpret
feature importance and local decisions. Without dimensionality reduction Bagging, Random
Forest and Stacking achieve 99.3 to 99.4% accuracy and F1, with ROC-AUC and PR-AUC close
to 99.95% on the test set and similarly strong cross-validation scores. UMAP and NCA preserve
high performance in lower dimensional spaces. While PLS-DA gives moderate but consistent
results. The study shows that ensemble models on the original feature space provide highly
accurate and stable fraud detection on this dataset. SHAP analysis reveals that source and
destination balances, transaction amount and type are the most influential features.
Merit Chinonso Opara· IIARD INTERNATIONAL JOURNAL...· 0 citations
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