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42 results for “Fraud”
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Research ArticleOpen accessEvery word in the titleEnsemble Learning for Proactive Detection of Network-Intrusion-Based Insurance FraudBenjamin Asubam Weyori, Selorm Kofi Tagbo et al.We propose an ensemble learning pipeline that proactively integrates Stacking Feature Embedding (SFE) with Principal Component Analysis (PCA) and tree-based ensembles to proactively detect insurance fraud originating from network intrusions. The main contributions are: (1) the novel integration of SFE-PCA as a meta-feature construction step for tabular network flow data; (2) a sensitivity analysis that justifies PCA reduction ratios used … …Journal of Computer Science, Vol. 22, Issue 7 · 16 Jul 2026 · 935 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleComparative Analysis of Fraud Detection Methods in Banking Using Machine Learning TechniquesYoussef Tounsi, Ennouri TaziFraud detection in banking requires algorithms that balance classification performance, computational efficiency, and regulatory interpretability, criteria that are rarely benchmarked together. We present a comprehensive evaluation of nine machine learning approaches (Logistic Regression, Decision Tree, Random Forest, SVM, SGD, XGBoost, CatBoost, LightGBM, and MLP) across three datasets differing in size, imbalance severity, and feature type (synthetic, real-world PCA-anonymized, and large-scale … …Journal of Computer Science, Vol. 22, Issue 6 · 22 Jun 2026 · 1,341 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleOptimized XGBoost for Ethereum Fraud Detection: A Cost-Sensitive ApproachSupriya P., Rubah Sheriff, Shreya Padaki et al.In today’s technologically advancing world, many fields from finance to healthcare and education are shifting toward a digital and decentralized format. A significant transformation is underway with the currency of the masses. Blockchain-based cryptocurrencies like Bitcoin and Ethereum allow users to generate fungible tokens anonymously through smart contracts. However, these features also facilitate illicit transactions and cybercrimes like fraud, phishing, … …Journal of Computer Science, Vol. 21, Issue 12 · 10 Jan 2026 · 2,770 accessesView PDFCite
Review ArticleOpen accessEvery word in the titleMachine Learning-Based Detection of Credit Card Fraud: A Comparative StudyZainab Khamees Alkhateeb, Abeer Tariq MaoloodOne of the fastest-growing problems with a high impact on the financial sector is financial fraud. Recently, data mining has been identified as one of the effective ways of detecting fraudulent credit card transactions. As a data mining problem, the detection of fraudulent credit card transaction is a challenging task due to the following reasons: (i) The frequent changes in … …American Journal of Engineering and Applied Sciences, Vol. 12, Issue 4 · 25 Dec 2019 · 7,780 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleThe Diaspora Nigerians’ Image Problem of Drug and Fraud: A Case Study of the Malaysian-Indonesian Experience through Newspaper ReportsMusa Yusuf Owoyemi, Abdul Kadir Haji Din, Ahmad Zaharuddin Sani Ahmad SabriIt is no longer news that the economic and social problems in Nigeria have led to mass migration of Nigerians to other parts of the world for better job opportunities and/or education. South-East Asia has had its fair intake of some of these Nigerians especially in recent times. However, some unscrupulous Nigerians in their bid to make quick money have … …Journal of Social Sciences, Vol. 11, Issue 2 · 24 Jun 2015 · 7,761 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleA DATA WAREHOUSE DESIGN FOR THE DETECTION OF FRAUD IN THE SUPPLY CHAIN BY USING THE BENFORD’S LAWCornelia Kraus, Raul ValverdeLarge data volumes and the inability to analyse them enables fraudulent activities to go unnoticed in supply chain management processes such as procurement, warehouse management and inventory management. This fraud increases the cost of the supply chain management and a fraud detection mechanism is necessary to reduce the risk of fraud in this business area. This study was carried out … …American Journal of Applied Sciences, Vol. 11, Issue 9 · 7 Jul 2014 · 8,740 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleSmall Business Entrepreneurs: A Focus on Fraud Risk and PreventionDoug LauferProblem statement: Small businesses have unique problems in fighting fraud. Typically there are limited resources devoted to anti-fraud efforts by small businesses. Employee fraud typically results in monetary loss, often not recovered, investigation expenses and negative impact on employee productivity and morale. Conclusion/Recommendations: This study provides information to assist the small business entrepreneur in identifying potential fraud risks and to … …American Journal of Economics and Business Administration, Vol. 3, Issue 2 · 14 Jun 2011 · 8,620 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleIdentification of Fraud in Accidental and Healthcare Insurance Using Local Outlier Factor: A Machine Learning ApproachJyoti Lele, Vaidehi Deshmukh, Abhinav Chandra, Radhika DesaiAn unsupervised machine learning model that uses the mechanism of the Local Outlier Factor to flag and detect ambiguous as well as potentially fraudulent claims in Accidental and Healthcare insurance is proposed in this paper. It entirely automates the manual investigation of claims by claim appraisers in any organization. The ethos of this model is to comprehensively automate and expedite … …Journal of Computer Science, Vol. 21, Issue 12 · 22 Jan 2026 · 1,575 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleDetection of Commercial Fraud in Processed Meat Products Using Rapid TechniquesAli M. Ahmed, Takwa H. Ismail, Reham R. Abouelmaatti et al.Meat adulteration with different species has become a considerable problem worldwide. It means that meat products contain undeclared meat species; so, the meat ingredients are not consistent with the label. It has direct impact on public health, religious faith, fair-trades and food safety standers. This study focused on species adulteration detection in meat products by applying accurate, slandered and fast … …American Journal of Biochemistry and Biotechnology, Vol. 16, Issue 2 · 4 Jun 2020 · 7,817 accessesView PDFCite
Research ArticleOpen accessIn the keywordsProbabilistic Learning Models for Secure E-Commerce Transaction AnalysisAnanth Maria Pushpa, S. SubramaniaanThe tremendous expansion of e-commerce requires well-developed security mechanisms of transactions coupled with cryptography systems. This paper explores how probabilistic learning algorithms can be used to promote fraud and transaction validation in e-commerce systems that are cryptographically secured. A study compared different machine learning models to analyze retail data, including Naïve Bayes, Bayes Network, and Logistic learning. The results showed … …Journal of Computer Science, Vol. 22, Issue 11 · 20 Oct 2026 · 506 accessesView PDFCite