Search Results (5)

Financial statement fraud is a socio-technical risk that arises from coupled organizational, informational, and regulatory processes. To address the Identification Paradox in financial fraud detection, where existing models cannot simultaneously recognize both chronic manipulation and acute outbreaks in financial data, this study proposes the Causal–Temporal Asynchrony (CTA) theory as a process-oriented conceptual framework that guides feature construction and model design in a predictive setting. CTA defines fraud motive as a chronic, multi-period accumulation and fraud action as an acute, single-year event. To operationalize CTA within a predictive setting, we build a deployable Decoupling-Fusion System that encodes CTA as an Acute–Chronic Binary Feature Dimensions schema and performs detection via Decoupling-Fusion FraudNet. Within this system, parallel Long Short-Term Memory networks (LSTM) capture chronic motive signals from longitudinal sequences, while parallel Convolutional Neural Networks (CNN) and a Feed-forward Neural Network (FNN) identify acute action signals from multimodal snapshots; the resulting asynchronous probabilities are integrated via an adaptive decision-level fusion mechanism. Empirical tests on China’s A-share market (2001–2021) show the system (AUC = 0.967) outperforms baseline models. Furthermore, eXplainable AI analysis reveals patterns consistent with the classic fraud triangle (pressure, opportunity and rationalization). This study develops a theory-grounded decision-support system that unifies acute and chronic evidence streams and provides a deployable blueprint for continuous auditing and governance. Full article

We prove a fundamental attribution error connecting rule-breaking behavior to entrepreneurs. We do so in the research context of the US, where we recently sampled from medium-sized venture entrepreneurs and their corporate executive peers (as an applicable reference point). We chose the US not only for its high entrepreneurial activity, but also because of the not uncommon relationship between business leaders and religion. By including various measures of religiosity in the study, we could control for factors that would likely influence rule-breaking, which standard models like the fraud triangle do not explicitly consider. In fact, we add contingency theory ideas to the fraud triangle to determine whether it is the decision conditions that determine rule-breaking rather than the role of the person (i.e., as an entrepreneur). We find that once demographic, religious, firm and industry contingencies are controlled for, any statistically significant influence of being an entrepreneur (relative to being a corporate executive with similar opportunity, motivation, capability and rationalization) disappears when it comes to self-admitted value-bending behaviors at work. Our contribution consists of a novel analysis, results and discussion of the ‘bent’repreneur—adding to conversations on the under-researched nexus of entrepreneurship with religiosity and ethical decision-making. Full article

Fraud is increasingly common, and so are the losses caused by this phenomenon. There is, thus, an essential economic incentive to study this problem, particularly fraud prevention. One barrier complicating the research in this direction is the lack of public data sets that embed fraudulent activities. In addition, although efforts have been made to detect fraud using machine learning, such actions have not considered the component of human behavior when detecting fraud. We propose a mechanism to detect potential fraud by analyzing human behavior within a data set in this work. This approach combines a predefined topic model and a supervised classifier to generate an alert from the possible fraud-related text. Potential fraud would be detected based on a model built from such a classifier. As a result of this work, a synthetic fraud-related data set is made. Four topics associated with the vertices of the fraud triangle theory are unveiled when assessing different topic modeling techniques. After benchmarking topic modeling techniques and supervised and deep learning classifiers, we find that LDA, random forest, and CNN have the best performance in this scenario. The results of our work suggest that our approach is feasible in practice since several such models obtain an average AUC higher than 0.8. Namely, the fraud triangle theory combined with topic modeling and linear classifiers could provide a promising framework for predictive fraud analysis. Full article

Fraud entails deception in order to obtain illegal gains; thus, it is mainly evidenced within financial institutions and is a matter of general interest. The problem is particularly complex, since perpetrators of fraud could belong to any position, from top managers to payroll employees. Fraud detection has traditionally been performed by auditors, who mainly employ manual techniques. These could take too long to process fraud-related evidence. Data mining, machine learning, and, as of recently, deep learning strategies are being used to automate this type of processing. Many related techniques have been developed to analyze, detect, and prevent fraud-related behavior, with the fraud triangle associated with the classic auditing model being one of the most important of these. 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. We used the Kitchenham methodology to analyze the research works related to fraud detection from the last decade. This review provides evidence that fraud is an area of active investigation. Several works related to fraud detection using machine learning techniques were identified without the evidence that they incorporated the fraud triangle as a method for more efficient analysis. Full article

China has suffered frequent water pollution incidents in recent years, and information disclosure on relevant hazards is often delayed and insufficient. The purpose of this paper is to unearth the latent resistance, and analyze the institutional arrangements and countermeasures. After reviewing representative journal literature about environmental information disclosure, this paper provides a theoretical review based on a comparison of the ontological differences between stakeholder theory and fraud triangle theory. A tentative application of fraud triangle theory as a means of explaining the phenomenon is proposed. Empirical analysis is undertaken to verify the tentative theoretical explanation. Based on news reports from Chinese official news websites, content analysis on longitudinal case evidence of representative water pollution incidents is applied, to contribute to unearthing the mechanism of the latent resistance towards information disclosure. The results show that local government agencies have a dominant position vis a vis information disclosure, but that some important actors rarely participate in information disclosure, which provides a chance for local government agencies’ information disclosure to commit fraud. The phenomenon, its essence, and proposed countermeasures are discussed and explained by referring to recent governmental environmental practices in China. Promising research topics are illuminated, providing enlightenment for future study. Full article