Data mining process for fraud detection in mobile communication by Jelena Mamčenko, Regina Kulvietienė

“… Without dependence from a sort of activity (sale, rendering of services, etc.) the using of data mining methods can bring the certain advantage. Fraud detection methods of data mining can be applied to this problem quite readily. …”
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Credit card fraud detection through machine learning algorithm by Agyan Panda, Bharath Yadlapalli, Zhi Zhou

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Multi-view graph neural network for fraud detection algorithm by Zhuo CHEN, Miao ZHU, Junwei DU

Subjects: “…fraud detection…”
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Credit Card Fraud Detection through Parenclitic Network Analysis by Massimiliano Zanin, Miguel Romance, Santiago Moral, Regino Criado

“…We show how the inclusion of features extracted from the network data representation improves the score obtained by a standard, neural network-based classification algorithm and additionally how this combined approach can outperform a commercial fraud detection system in specific operation niches. Beyond these specific results, this contribution represents a new example on how complex networks and data mining can be integrated as complementary tools, with the former providing a view to data beyond the capabilities of the latter.…”
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A Comprehensive Investigation of Fraud Detection Behavior in Federated Learning by Sun Rui

“…This paper also highlights the challenges of FL in fraud detection, including data heterogeneity, communication costs, and security risks. …”
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Multi-Relational Graph Representation Learning for Financial Statement Fraud Detection by Chenxu Wang, Mengqin Wang, Xiaoguang Wang, Luyue Zhang, Yi Long

“…Traditional machine learning approaches focus on individual companies, overlooking the interactive relationships among companies that are crucial for identifying fraud patterns. Moreover, fraud detection is a typical imbalanced binary classification task with normal samples outnumbering fraud ones. …”
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Cloud-Based Transaction Fraud Detection: An In-depth Analysis of ML Algorithms by Ali Alhchaimi

Subjects: “…Cloud-Security, Fraud-Detection, Fraud-Transaction, ML, Evaluation, Performance.…”
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Harnessing artificial intelligence and machine learning for fraud detection and prevention in Nigeria by Oluwaseun Isaac Odufisan, Osekhonmen Victory Abhulimen, Erastus Olarenwaju Ogunti

“…The paper highlights the benefits of AI-powered fraud detection, including increased efficiency, improved accuracy, and proactive risk mitigation. …”
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Ad Click Fraud Detection Using Machine Learning and Deep Learning Algorithms by Reem A. Alzahrani, Malak Aljabri, Rami A. Mustafa Mohammad

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Optimization of the Naive Bayes Algorithm with SMOTETomek Combination for Imbalance Class Fraud Detection by Arief Tri Arsanto, Arif Faizin, Moch lutfi, Zulfatun Nikmatus Saadah

“…Dataset analysis needs to be carried out to analyze the history of transactions that have been carried out. In the fraud detection dataset, it can be seen that there are attributes that cause data imbalance. …”
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A Credit Card Fraud Detection Algorithm Based on SDT and Federated Learning by Yuxuan Tang, Zhanjun Liu

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A systematic review of AI-enhanced techniques in credit card fraud detection by Ibrahim Y. Hafez, Ahmed Y. Hafez, Ahmed Saleh, Amr A. Abd El-Mageed, Amr A. Abohany

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E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review by Abed Mutemi, Fernando Bacao

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Fraud Detection in Cryptocurrency Networks—An Exploration Using Anomaly Detection and Heterogeneous Graph Transformers by Víctor Pérez-Cano, Francisco Jurado

“…As a result, the article will provide reasonable conclusions to acknowledge that unsupervised approaches can be useful for fraud detection on blockchain networks. Furthermore, the effectiveness of supervised graph methods was revalidated, emphasizing the importance of data heterogeneity.…”
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A stacked ensemble approach with resampling techniques for highly effective fraud detection in imbalanced datasets by Idongesit E. Eteng, Udeze L. Chinedu, Ayei E. Ibor

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Efficient Handling of Data Imbalance in Health Insurance Fraud Detection Using Meta-Reinforcement Learning by Supriya Seshagiri, K. V. Prema

“…Data imbalance is one of the major challenges in health insurance fraud detection where the distribution of classes within the dataset is significantly skewed, leading statistical models to be biased toward the dominant class. …”
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Machine Learning Based Method for Insurance Fraud Detection on Class Imbalance Datasets With Missing Values by Ahmed A. Khalil, Zaiming Liu, Ahmed Fathalla, Ahmed Ali, Ahmad Salah

“…The obtained results outline that addressing the class imbalance in the insurance fraud detection dataset has a significant positive effect on the performance of the predictive model, while addressing the problem of missing values has a slight effect. …”
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FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining by K. R. Seeja, Masoumeh Zareapoor

“…This paper proposes an intelligent credit card fraud detection model for detecting fraud from highly imbalanced and anonymous credit card transaction datasets. …”
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Optimizing Multilayer Perceptron with Cost-Sensitive Learning for Addressing Class Imbalance in Credit Card Fraud Detection by Wowon Priatna, Hindriyanto Dwi Purnomo, Ade Iriani, Irwan Sembiring, Theophilus Wellem

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Optimizing Credit Card Fraud Detection: A Genetic Algorithm Approach with Multiple Feature Selection Methods by Sunil Kumar Patel, Devina Panday

“…To protect our financial security, it is crucial to develop robust and accurate fraud detection systems that stay one step ahead of the fraudsters. …”
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