Credit Card Fraud Detection Using AdaBoost and Majority Voting by Kuldeep Randhawa, Chu Kiong Loo, Manjeevan Seera, Chee Peng Lim, Asoke K. Nandi

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A Machine Learning and Blockchain Based Efficient Fraud Detection Mechanism by Tehreem Ashfaq, Rabiya Khalid, Adamu Sani Yahaya, Sheraz Aslam, Ahmad Taher Azar, Safa Alsafari, Ibrahim A. Hameed

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Artificial intelligence and algorithmic decisions in fraud detection: An interpretive structural model by Evrim Tan, Maxime Petit Jean, Anthony Simonofski, Thomas Tombal, Bjorn Kleizen, Mathias Sabbe, Lucas Bechoux, Pauline Willem

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Forecasting the Financial Statements Fraud Detection of Companies Listed on the Stock Exchange by Aso Bahrami, Iraj Noravesh, abbas Raad, ata mohamadi molqarani

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Impact of reviewer social interaction on online consumer review fraud detection by Kunal Goswami, Younghee Park, Chungsik Song

“…The effectiveness of the feature set is in rivaling the other approaches to fraud detection. Conclusions A user’s social interaction on a digital platform such as Yelp is equally important in evaluating the user as social interaction is in real life. …”
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Research on Fraud Detection Method Based on Heterogeneous Graph Representation Learning by Xuxu Zheng, Chen Feng, Zhiyi Yin, Jinli Zhang, Huawei Shen

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The effect of internal audit effectiveness, auditor responsibility and training in fraud detection by George Drogalas, Michail Pazarskis, Evgenia Anagnostopoulou, Angeliki Papachristou

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The effect of feature extraction and data sampling on credit card fraud detection by Zahra Salekshahrezaee, Joffrey L. Leevy, Taghi M. Khoshgoftaar

“…Our results show that the implementation of the RUS method followed by the CAE method leads to the best performance for credit card fraud detection.…”
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Fraud detection models and payment transactions analysis using machine learning by Shpyrko Viktor, Koval Bohdan

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Fraud Detection and Identification in Credit Card Based on Machine Learning Techniques by Omega John Unogwu, Youssef Filali

“…However, implementing fraud detection models presents challenges such as limited data availability, data sensitivity, and imbalanced class distributions. …”
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Research on Financial Fraud Detection Models Integrating Multiple Relational Graphs by Jianfeng Li, Dexiang Yang

Subjects: “…fraud detection…”
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The Analysis of Benford's Law Ability to Identify and Predict Financial Fraud Detection by Seyed Abbas Hashemi, Amirsina Hariri

“…The results of the third hypothesis indicates that deviation from Benford's Law in fraud detection year is reduced compared to previous years. …”
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Classification of Credit Card Frauds Detection using machine learning techniques by Rasha Rokan Ismail, Farah Hatem Khorsheed

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Big Data fraud detection using multiple medicare data sources by Matthew Herland, Taghi M. Khoshgoftaar, Richard A. Bauder

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Survey of Credit Card Anomaly and Fraud Detection Using Sampling Techniques by Maram Alamri, Mourad Ykhlef

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Continuous Fraud Detection in Enterprise Systems through Audit Trail Analysis by Peter J. Best, Pall Rikhardsson, Mark Toleman

“…This includes developing methods and tools for continuous assurance and fraud detection. In this paper we propose a methodology for continuous fraud detection that exploits security audit logs, changes in master records and accounting audit trails in enterprise systems. …”
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Application of Machine Learning and Resampling Techniques to Credit Card Fraud Detection by Chinedu L. Udeze, Idongesit E. Eteng, Ayei E. Ibor

Subjects: “…Machine learning, Fraud detection, Random forest, Resampling techniques, XGBoost, TensorFlow, Deep neural network…”
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Improving fraud detection with semi-supervised topic modeling and keyword integration by Marco Sánchez, Luis Urquiza

“…Fraud detection through auditors’ manual review of accounting and financial records has traditionally relied on human experience and intuition. …”
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Integrating Data Mining Techniques for Fraud Detection in Financial Control Processes by Viktor M. Sushkov, Pavel Y. Leonov, Olga S. Nadezhina, Irina Y. Blagova

“…In conclusion, this paper underscores the comprehensive understanding that can be achieved through the integration of Benford's Law and other data mining techniques in fraud detection, emphasizing their potential to automate and scale fraud detection efforts in financial control processes.…”
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Enhancing fraud detection in banking by integration of graph databases with machine learning by Ayushi Patil, Shreya Mahajan, Jinal Menpara, Shivali Wagle, Preksha Pareek, Ketan Kotecha

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