Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 267-272· 0 citations· 16 references
Abstract
Financial Fraud Detection (FFD) have increase serious as fraudulent activities continue to change and pose significant risks to financial institutions and consumers alike. The increasing sophistication of fraudulent activities necessitates innovative approaches to identify and mitigate potential risks in financial transactions. The data preprocessing, containing data cleaning as well as handling of missing values, to get high-quality input for analysis. Feature engineering techniques are employed to create relevant attributes from raw data, improving the fraudulent patterns. A hybrid classification model utilizing Long Short-Term Memory and Deep Neural Network with Hyena Optimization Algorithm (LSTM-DNN with HOA) is implemented to seizure temporal dependencies in transaction data, improving detection accuracy. The dataset is split into training as well as testing sets to smooth verification of the model’s performance. Scaling normalization is applied to ensure all variables contribute equally to the analysis, whereas distance calculations help in assessing the similarity between transactions. After fraud prediction using the optimized LSTM-DNN model, Explainable Artificial Intelligence (XAI) is incorporated using Shapley Additive explanations (SHAP). SHAP is employed to take calculations by computing the support of each numerical feature toward fraud and non-fraud decisions, thereby improving transparency, trustworthiness, and decision interpretability. For FFD the Credit Card Fraud Detection dataset is implemented using python software the proposed LSTM-DNN with FOA have the accuracy of 99\% high compared to existing technique. Strength of the paper is the optimization process enhances model, convergence, and classification performance whereas reducing the chances of suboptimal parameter selection.
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
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 findings show that hybrid models are much more effective than standalone classifiers with respect to precision, recall, F 1 -score and area under the ROC curve (AUC) particularly in detecting rare and unseen cases of frauds.
M. Mohammed· International Journal of App...· 0 citations
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
Unified Payments Interface (UPI) has revolutionized digital payments in India, enabling seamless, real-time money transfers
between accounts. However, its growing popularity has made it increasingly susceptible to fraudulent activities such as phishing, account
takeovers, and transaction manipulation. This study introduces a hybrid fraud detection system integrating Convolutional Neural
Networks (CNN) with Autoencoder, Local Outlier Factor (LOF), and K-Means Clustering to detect anomalous UPI transactions
efficiently. The system utilizes anonymized UPI transaction data including transaction amount, time, device identifiers, and geolocation.
Preprocessing involved encoding categorical features, normalizing numerical variables, and addressing missing data. The proposed hybrid
approach was evaluated using accuracy, precision, recall, F1-score, and AUC-ROC metrics, achieving higher accuracy and fewer false
positives compared to traditional methods. The findings highlight that deep learning combined with unsupervised techniques offers a
robust solution for ensuring secure and reliable UPI payment operations.
Meghana L, Amutha S, Soumya Patil· International Journal for Re...· 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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