Fraud Detection Using the Fraud Triangle Theory and Data Mining Techniques: A Literature Review

Sánchez-Aguayo, Marco; Urquiza-Aguiar, Luis; Estrada-Jiménez, José

doi:10.3390/computers10100121

Cited by 36 publications

(25 citation statements)

References 63 publications

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“…As shown in Figure 2, the three-phase process used by Sánchez-Aguayo et al [9] was followed. Ali et al's [10] systematic strategy for literature review was utilized to gather and consolidate pertinent and current research tackling banking fraud detection.…”

Section: Methodsmentioning

confidence: 99%

Real-Time Online Banking Fraud Detection Model by Unsupervised Learning Fusion

Abbassi,

El Mendili,

Gahi

2024

HighTech. Innov. J.

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Digital trades and payments are becoming increasingly popular, as they typically entail monetary transactions. This not only makes electronic transactions more convenient for the end customer, but it also raises the likelihood of fraud. An adequate fraud detection system with a cutting-edge model is critical to minimizing fraud costs. Identifying fraud at the ideal time entails establishing and setting up ubiquitous systems to consume and analyze massive amounts of streaming data. Recent advances in data analytics methods and introducing open-source technology for big data storage and processing opened new options for detecting fraud. This study aims to tackle this critical issue by providing a newly real-time e-transaction fraud detection schema that consolidates the advantages of both unsupervised learners, including autoencoder and extended isolation forests, with cutting-edge big data gadgets such as Spark streaming and sparkling water. It addresses the shortage of non-fraudulent instances and handles the excessive dimension of the set of features. On two real-world transactional datasets, we assess our suggested technique. Compared with other current fraud identification systems, our methodology delivers an elevated accuracy yield of 99%. Furthermore, it outperforms state-of-the-art approaches in reliably identifying fraudulent samples. Doi: 10.28991/HIJ-2024-05-01-014 Full Text: PDF

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Section: Methodsmentioning

confidence: 99%

Real-Time Online Banking Fraud Detection Model by Unsupervised Learning Fusion

Abbassi,

El Mendili,

Gahi

2024

HighTech. Innov. J.

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“…It is based on his research into white-collar crimes and embezzlement by trusted individuals (Cheliatsidou et al, 2023;Tickner and Button, 2021;Wardani and Putri, 2023). The theory has been widely adopted and applied in various fields, including accounting, auditing and criminology, to understand and prevent fraud (Burke and Sanney, 2018;Free, 2015;Homer, 2020;Said et al, 2017;S anchez-Aguayo et al, 2021). The fraud triangle theory is a well-established and widely recognized framework for understanding and preventing fraud (Alzahrane, 2023).…”

Section: Literature Review Research Model and Hypotheses Developmentmentioning

confidence: 99%

“…In the academic setting, students may have the opportunity to cheat on exams, plagiarize work or falsify data. Previous researchers have demonstrated that when there is an opportunity to commit fraud with no possibility of detection or consequences, the likelihood of fraud increases among those who engage in such behavior (Cheliatsidou et al , 2023; Homer, 2020; Sánchez-Aguayo et al , 2021; Tickner and Button, 2021).…”

Section: Literature Review Research Model and Hypotheses Developmentmentioning

confidence: 99%

Factors affecting accounting students’ misuse of chatgpt: an application of the fraud triangle theory

Alshurafat,

Al Shbail,

Hamdan

et al. 2023

JFRA

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Purpose This study aims to explore the factors that contribute to student academic dishonesty through an examination of the misuse of AI language models. Using the fraud triangle theory, which posits that opportunity, rationalization and pressure are key factors for fraudulent behavior, this study investigates how these elements interact and contribute to academic dishonesty among students. Design/methodology/approach In this study, data on how accounting students used ChatGPT to cheat was acquired from 279 accounting students in Jordanian public universities over the course of two months, from January 2023 to March 2023, through previously tested and validated questionnaires. The main tool for gathering data was a questionnaire distributed online using Microsoft Forms. Findings The results show that all of the fraud triangle factors are significant determinants of student academic dishonesty and student misuse of ChatGPT. The findings of this research can be used to guide the development of technology-based preventative measures. Originality/value This study provides valuable insights into the motivations and factors that drive students to engage in academic dishonesty and sheds light on the broader issue of technology-assisted academic dishonesty and its impact on the educational system. This study’s contribution is significant, as it sheds light on a pressing issue in education and provides valuable information for educators and policymakers to address the problem and improve academic standards.

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“…A detailed study on fraud-related jobs was conducted in Sánchez-Aguayo, Urquiza-Aguiar & Estrada-Jiménez (2021) . A systematic literature review (SLR) proposes collecting and analyzing research that addresses this phenomenon, considering human behavior as the leading risk factor reviewed associated theories that study this phenomenon.…”

Section: Introductionmentioning

confidence: 99%

Improving fraud detection with semi-supervised topic modeling and keyword integration

Sánchez,

Urquiza

2024

PeerJ Computer Science

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Fraud detection through auditors’ manual review of accounting and financial records has traditionally relied on human experience and intuition. However, replicating this task using technological tools has represented a challenge for information security researchers. Natural language processing techniques, such as topic modeling, have been explored to extract information and categorize large sets of documents. Topic modeling, such as latent Dirichlet allocation (LDA) or non-negative matrix factorization (NMF), has recently gained popularity for discovering thematic structures in text collections. However, unsupervised topic modeling may not always produce the best results for specific tasks, such as fraud detection. Therefore, in the present work, we propose to use semi-supervised topic modeling, which allows the incorporation of specific knowledge of the study domain through the use of keywords to learn latent topics related to fraud. By leveraging relevant keywords, our proposed approach aims to identify patterns related to the vertices of the fraud triangle theory, providing more consistent and interpretable results for fraud detection. The model’s performance was evaluated by training with several datasets and testing it with another one that did not intervene in its training. The results showed efficient performance averages with a 7% increase in performance compared to a previous job. Overall, the study emphasizes the importance of deepening the analysis of fraud behaviors and proposing strategies to identify them proactively.

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Fraud Detection Using the Fraud Triangle Theory and Data Mining Techniques: A Literature Review

Cited by 36 publications

References 63 publications

Real-Time Online Banking Fraud Detection Model by Unsupervised Learning Fusion

Real-Time Online Banking Fraud Detection Model by Unsupervised Learning Fusion

Factors affecting accounting students’ misuse of chatgpt: an application of the fraud triangle theory

Improving fraud detection with semi-supervised topic modeling and keyword integration

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