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With online payment platforms being ubiquitous and important, fraud transaction detection has become the key for such platforms, to ensure user account safety and platform security. In this work, we present a novel method for detecting…

机器学习 · 计算机科学 2020-03-30 Longfei Li , Ziqi Liu , Chaochao Chen , Ya-Lin Zhang , Jun Zhou , Xiaolong Li

Money launderers exploit the weaknesses in detection systems by purposefully placing their ill-gotten money into multiple accounts, at different banks. That money is then layered and moved around among mule accounts to obscure the origin…

机器学习 · 计算机科学 2025-01-03 Haseeb Tariq , Marwan Hassani

Accounting fraud is a global concern representing a significant threat to the financial system stability due to the resulting diminishing of the market confidence and trust of regulatory authorities. Several tricks can be used to commit…

机器学习 · 统计学 2018-05-09 Maria Jofre , Richard Gerlach

Telecommunication fraud is an acute problem that leads to substantial material losses and compromises the reliability of telecom systems worldwide. Only effective and efficient detection mechanisms can help to deal with these threats,…

网络与互联网体系结构 · 计算机科学 2026-05-25 Praveen Hegde , Mishal Shah

With an increase in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection has become an emerging topics of great importance for academics, research and industries. Financial fraud is a…

其他计算机科学 · 计算机科学 2013-09-05 H. Lookman Sithic , T. Balasubramanian

Financial forensics has an important role in the field of finance to detect and investigate the occurrence of finance related crimes like money laundering. However, as with other forms of criminal activities, the forensics analysis of such…

密码学与安全 · 计算机科学 2023-03-10 Jonathan Pan

Spurious credit card transactions are a significant source of financial losses and urge the development of accurate fraud detection algorithms. In this paper, we use machine learning strategies for such an aim. First, we apply a mixed…

机器学习 · 计算机科学 2021-12-07 Daniel H. M. de Souza , Claudio J. Bordin

Social financial technology focuses on trust, sustainability, and social responsibility, which require advanced technologies to address complex financial tasks in the digital era. With the rapid growth in online transactions, automating…

Fraudulent transactions and how to detect them remain a significant problem for financial institutions around the world. The need for advanced fraud detection systems to safeguard assets and maintain customer trust is paramount for…

机器学习 · 计算机科学 2023-12-22 Tomisin Awosika , Raj Mani Shukla , Bernardi Pranggono

We present a prototype system developed in cooperation with a business organization that combines information visualization and pattern-matching techniques to detect fraudulent activity by employees. The system is built upon common fraud…

其他计算机科学 · 计算机科学 2013-12-02 Evmorfia N. Argyriou , Antonios Symvonis , Vassilis Vassiliou

With the increase of credit card usage, the volume of credit card misuse also has significantly increased. As a result, financial organizations are working hard on developing and deploying credit card fraud detection methods, in order to…

机器学习 · 计算机科学 2019-12-09 Niloofar Yousefi , Marie Alaghband , Ivan Garibay

Fraud detection is one of the most important challenges that financial systems must address. Detecting fraudulent transactions is critical for payment gateway companies like Flow Payment, which process millions of transactions monthly and…

神经与进化计算 · 计算机科学 2025-07-15 Kevin Reyes , Vasco Cortez

Card transaction fraud is a growing problem affecting card holders worldwide. Financial institutions increasingly rely upon data-driven methods for developing fraud detection systems, which are able to automatically detect and block…

应用统计 · 统计学 2020-05-07 Sebastiaan Höppner , Bart Baesens , Wouter Verbeke , Tim Verdonck

This study explores the application of anomaly detection (AD) methods in imbalanced learning tasks, focusing on fraud detection using real online credit card payment data. We assess the performance of several recent AD methods and compare…

机器学习 · 计算机科学 2023-12-22 Hugo Thimonier , Fabrice Popineau , Arpad Rimmel , Bich-Liên Doan , Fabrice Daniel

As online fraud becomes more sophisticated and pervasive, traditional fraud detection methods are struggling to keep pace with the evolving tactics employed by fraudsters. This paper explores the transformative role of machine learning in…

机器学习 · 计算机科学 2024-10-29 Md Kamrul Hasan Chy

The main objective of this article is to develop scalable dynamic anomaly detectors when high-fidelity simulators of power systems are at our disposal. On the one hand, mathematical models of these high-fidelity simulators are typically…

最优化与控制 · 数学 2020-10-07 Kaikai Pan , Peter Palensky , Peyman Mohajerin Esfahani

Credit card fraud causes significant financial losses and frequently occurs as fraud attack, defined as short-term sequence of fraudulent transactions associated with high transaction rates and amounts, business areas historically tied to…

最优化与控制 · 数学 2025-03-27 Alexander Stotsky

The article proposes an expert system for detection, and subsequent investigation, of groups of collaborating automobile insurance fraudsters. The system is described and examined in great detail, several technical difficulties in detecting…

人工智能 · 计算机科学 2011-04-21 Lovro Šubelj , Štefan Furlan , Marko Bajec

This study addresses the problem of dynamic anomaly detection in accounting transactions and proposes a real-time detection method based on a Transformer to tackle the challenges of hidden abnormal behaviors and high timeliness requirements…

机器学习 · 计算机科学 2025-11-18 Yi Wang , Ruoyi Fang , Anzhuo Xie , Hanrui Feng , Jianlin Lai

As the world is rapidly moving towards digitization and money transactions are becoming cashless, the use of credit cards has rapidly increased. The fraud activities associated with it have also been increasing which leads to a huge loss to…

人工智能 · 计算机科学 2021-08-24 Pooja Tiwari , Simran Mehta , Nishtha Sakhuja , Jitendra Kumar , Ashutosh Kumar Singh