Generative Adversarial Networks-Based Novel Approach for Fraud Detection for the European Cardholders 2013 Dataset by Fahdah A. Almarshad, Ghada Abdalaziz Gashgari, Abdullah I. A. Alzahrani

Subjects: “…Credit card fraud detection…”
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A Multi-Modal Profiling Fraud-Detection System for Capturing Suspicious Airline Ticket Activities by Mehmed Taha Aras, Mehmet Amac Guvensan

Subjects: “…fraud detection…”
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Blockchain and Machine Learning for Fraud Detection: A Privacy-Preserving and Adaptive Incentive Based Approach by Tahmid Hasan Pranto, Kazi Tamzid Akhter Md. Hasib, Tahsinur Rahman, Akm Bahalul Haque, A. K. M. Najmul Islam, Rashedur M. Rahman

“…This study proposes a blockchain and smart contract-based approach to achieve robust Machine Learning (ML) algorithm for e-commerce fraud detection by facilitating inter-organizational collaboration. …”
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Auditors’ responsibility for fraud detection: Views of auditors, preparers, and users of financial statements in Saudi Arabia by Ahmed Abdullah Saad Al-Dhubaibi

“…The purpose of this study is to explore the views of independent external auditors, financial statements’ preparers and financial statements’ users regarding the general responsibilities of auditors with a focus on their responsibility for fraud detection. A structured questionnaire survey was used as an instrument to collect data for this purpose. …”
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Hyperparameter Optimization with Genetic Algorithms and XGBoost: A Step Forward in Smart Grid Fraud Detection by Adil Mehdary, Abdellah Chehri, Abdeslam Jakimi, Rachid Saadane

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An Adversary Model of Fraudsters’ Behavior to Improve Oversampling in Credit Card Fraud Detection by Daniele Lunghi, Gian Marco Paldino, Olivier Caelen, Gianluca Bontempi

Subjects: “…Fraud detection…”
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MEFUASN: A Helpful Method to Extract Features using Analyzing Social Network for Fraud Detection by Z. Karimi Zandian, M. R. Keyvanpour

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Use of optimized Fuzzy C-Means clustering and supervised classifiers for automobile insurance fraud detection by Sharmila Subudhi, Suvasini Panigrahi

Subjects: “…Fraud detection…”
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Fraud Detection Using the Fraud Triangle Theory and Data Mining Techniques: A Literature Review by Marco Sánchez-Aguayo, Luis Urquiza-Aguiar, José Estrada-Jiménez

“…This work aims to review current work related to fraud detection that uses the fraud triangle in addition to machine learning and deep learning techniques. …”
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Predictive Data Analytics for Electricity Fraud Detection Using Tuned CNN Ensembler in Smart Grid by Nasir Ayub, Usman Ali, Kainat Mustafa, Syed Muhammad Mohsin, Sheraz Aslam

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A Machine Learning Method with Hybrid Feature Selection for Improved Credit Card Fraud Detection by Ibomoiye Domor Mienye, Yanxia Sun

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Comparative Analysis of Data Mining Techniques for Fraud Detection (A Case Study of Branchless Banking) by Talha Umair, Syed Saif-ur-Rahman

“…In the end we have suggested the most suitable algorithms for fraud detection on branches bank. …”
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Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms by Fawaz Khaled Alarfaj, Iqra Malik, Hikmat Ullah Khan, Naif Almusallam, Muhammad Ramzan, Muzamil Ahmed

Subjects: “…Fraud detection…”
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An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine by Altyeb Altaher Taha, Sharaf Jameel Malebary

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Adaptive anomaly based fraud detection model for handling concept drift in short-term profile by Alabdeen, Aisha Abdallah Zain

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Classification of SIM box fraud detection using support vector machine and artificial neural network by Elmi, Abdikarim Hussein, Salleh @ Sallehuddin, Roselina, Ibrahim, Subariah, Mohd. Zain, Azlan

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Adoption of forensic accounting in fraud detection process by anti-corruption agency: a conceptual framework by Umar, Ibrahim, Samsudin, Rose Shamsiah, Mohamed, Mudzamir

“…Global incidences of fraudulent practices have led to the collapse of major businesses, public institution and governments.Fighting corruption requires the investigation, detection and prevention of frauds. Advancement in fraud detection process includes the use of forensic accounting and empirical studies reported its effectiveness in fraud detection process. …”
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Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers by César Pérez López, María Jesús Delgado Rodríguez, Sonia de Lucas Santos

“…The results showed that the selected model has an efficiency rate of 84.3%, implying an improvement in relation to other models utilized in tax fraud detection. The proposal can be generalized to quantify an individual’s propensity to commit fraud with regards to other kinds of taxes. …”
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Global calibration for non-targeted fraud detection in quinoa flour using portable hyperspectral imaging and chemometrics by Qianyi Wu, Magdi A.A. Mousa, Adel D. Al-Qurashi, Omer H.M. Ibrahim, Kamal A.M. Abo-Elyousr, Kent Rausch, Ahmed M.K. Abdel Aal, Mohammed Kamruzzaman

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Improving Tax Audit Efficiency Using Machine Learning: The Role of Taxpayer’s Network Data in Fraud Detection by Vardan Baghdasaryan, Hrant Davtyan, Arsine Sarikyan, Zaruhi Navasardyan

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