Fraudulent Behavior Research Articles

Internal Audit in the function of preventing corruptive activities in the public sector of the Federation of Bosnia and Herzegovina

Abstract The public sector of the Federation of Bosnia and Herzegovina (FBiH) from the aspect of complexity and comprehensiveness, includes all levels of government established within the state government, public companies, and the insurance system including: pension, health and social. The importance of performing duties and obligations in a transparent manner, and the possibility of detecting warning factors for corrupt activity are especially important in the public sector. Corrupt activities at all levels of the state structure of Bosnia and Herzegovina (BiH), as well as fraudulent behaviour and abuses within it, are precisely the consequences of the complexity of the state system, and the impossibility of efficient and rational use of public funds. Adequately organized, in accordance with the law, and accepted as such by employees within a certain public sector organization, internal auditing contributes to more efficient operations and prevention of corrupt practices. The aim of the subject research is to look at the process of functioning of the internal audit in the public sector of FBiH in the field of prevention of corrupt activities. The results of the research indicate the non-functioning and non-establishment of the internal audit in the manner specified by law, as well as the inadequate implementation of its recommendations, degrading and derogating its role, importance and contribution in the prevention of corrupt activities in the public sector of FBiH.

Advancing fraud detection in New Zealand Mānuka honey: Integrating hyperspectral imaging and GANomaly-based one-class classification

New Zealand Mānuka honey has become a prime target for adulteration due to its high commercial value. Given the diverse possibilities of fraudulent activities, training a supervised model that exhaustively covers all potential fraud scenarios is challenging. This study presents a new method for detecting fraudulent behavior in New Zealand Mānuka honey by combining hyperspectral imaging (HSI) with the GANomaly-based One-Class Classification method. We collected 18 different UMF-graded pure Mānuka honey samples from five New Zealand brands, which were used for training. The model was tested on fraudulent honey, including aged and syrup-adulterated honey, and compared with the traditional One-Class Classification methods. The results demonstrate that the HSI combined with the GANomaly method achieved 100% discrimination for all test samples, outperforming the standard rival techniques. In conclusion, this research developed a versatile model capable of detecting honey fraudulent behavior, showing significant practical implications for honey quality assessment.

Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention

The abstract is "The rapid evolution of cyber threats necessitates innovative defenses, particularly in the domains of risk assessment and fraud detection. This paper explores the integration of Artificial Intelligence (AI) and Adversarial Machine Learning (ML) techniques as a formidable strategy against increasingly sophisticated cyber-attacks. We present a comprehensive framework that leverages AI to dynamically assess cybersecurity risks and detect fraudulent activities with unprecedented accuracy and speed. Firstly, we delve into the foundational principles of adversarial machine learning, outlining how these techniques can be employed to simulate potential cyber threats, thereby enabling the development of more resilient AI-driven cybersecurity systems. We highlight the dual role of adversarial ML in both enhancing security defenses and potentially serving as a vector for sophisticated attacks, underscoring the importance of developing robust, adversarial-resistant models. Subsequently, we introduce a novel adaptive risk assessment methodology that incorporates real-time data analysis, machine learning algorithms, and predictive modeling to accurately identify and prioritize threats. This method adapts to the evolving digital landscape, ensuring that cybersecurity measures are always one step ahead of potential attackers. In the context of fraud detection, we explore -how AI algorithms can sift through vast datasets to detect anomalies and patterns indicative of fraudulent behavior. Through case studies and empirical analysis, we demonstrate the effectiveness of AI in identifying fraud across various sectors, from financial transactions to online identity verification processes. Our research contributes to the cybersecurity field by providing a detailed examination of how AI and adversarial ML can be harnessed to fortify digital defenses, improve risk assessment techniques, and enhance fraud detection capabilities. The insights garnered from this study not only advance theoretical understanding but also offer practical guidance for organizations seeking to implement AI-driven security solutions. As cyber threats continue to evolve, the integration of AI and adversarial ML in cybersecurity strategies will be paramount in safeguarding digital assets and maintaining the integrity of online systems."

Determinants of Academic Fraudulent Behavior Using the Fraud Diamond Theory Approach

Education aims to help humans to increase their potential through the field of study. Education is a process of positive character building so that a person becomes intelligent and has mental, emotional, and spiritual integrity. Education not only conveys knowledge and skills but also trains instincts, and builds habits, attitudes, and correct behavior in students. Various cases of academic fraud have made it difficult for educational institutions to achieve their goals. Fraud can occur in any field including in the academic field. This fraud is usually carried out by adults who consciously and deliberately commit fraud to obtain personal or group benefits. This study examines the effect of pressure, opportunity, rationalization, and capability on academic fraudulent behavior. The population in this study were students of the Accounting Bachelor Program at the Institute of Business and Informatics Kesatuan. The research sample was 110 respondents. This study uses primary data from a questionnaire designed using a 1-4 Likert scale. Data were analyzed using multiple linear regression. The results of the analysis show that pressure, opportunity, and rationalization have a positive effect on academic fraud behavior, while capability does not affect academic fraudulent behavior.

Analysis of Fraudulent Behavior using the Fraud Hexagon Approach at Jambi University

This research aims to determine and analyze fraudulent behavior using the fraud hexagon approach at Jambi University. The fraud hexagon used in this research consists of several factors, namely pressure, opportunity, rationalization, ability, arrogance, and collusion. This research uses quantitative and descriptive research methods. The type of data used is primary data collected using a questionnaire from 106 respondents who are leaders and employees whose scope of work is in the finance and procurement of goods/services at Jambi University. The results of this research show that if fraudulent behavior occurs at Jambi University, the most dominant factor in the fraud hexagon is the pressure factor

Does Financial Distress Contribute as an Intermediary Factor in Detecting Financial Statement Fraud ?

This study aims to investigate whether financial distress acts as an intermediary factor in detecting fraudulent financial statements in manufacturing companies listed on the Indonesia Stock Exchange. The purposive sampling method was used to select 105 manufacturing companies during the period 2017-2021. The analysis was conducted using the Outer Smart PLS model to evaluate the relationship between auditor turnover, board turnover, financial targets, ineffective supervision, capability, and financial distress on financial statement fraud. The results confirmed that there was no significant influence between capacity, board turnover, financial targets, ineffective supervision, and financial distress on financial statement fraud. In addition, financial distress also does not mediate the effect of other variables on financial statement fraud. This approach reflects the ethical philosophy in the study that emphasizes honesty, integrity, and transparency in corporate financial reporting. These results highlight the importance of effective supervisory strategies and strong internal controls in preventing and detecting financial statement fraud, regardless of the company's financial distress. This study contributes by exploring the role of financial distress in detecting financial statement fraud. For future research, it is important to investigate additional factors that influence fraud and develop more effective monitoring strategies. Contingency theory emphasizes the importance of contextual factors in organizational management, while Fraud theory pays attention to the motives and driving factors behind fraudulent behavior.

Prevention of food fraud and fraud emulation among companies in the supply chain based on a social Co-governance framework

This study develops a three-party evolutionary game model among upstream raw material producers, midstream food producers, and downstream distributors in the food supply chain, and investigates food fraud and fraud emulation among companies in the same group based on a food safety social co-governance framework. Moreover, the equilibrium points are divided into four scenarios according to the number of groups of companies committing fraud in the supply chain and whether companies in the same group emulate each other's fraudulent behavior. The stability conditions of these scenarios are also discussed and verified by numerical simulation in MATLAB. The results show that the behavioral strategy choices of different groups of food companies in the supply chain are closely related to the level of social co-governance involving the government, market, and consumers. Government regulation, supervision between companies, and consumer reporting can all change companies' behavioral strategies. Although the level of fraud emulation among companies in the same group does not change their behavioral strategy choice, it affects the time it takes for their behavioral strategy to evolve to a stable state. Moreover, the level of social co-governance directly affects companies' behavioral strategy choices at different emulation levels.

Motives For Fraudulent Behavior In Islamic Bank: Hexagon Model Of Perspective Fraud

This research explores the motives for fraudulent behaviour using the fraud hexagon model approach in Islamic Bank X, city Y. This research is qualitative research with primary data sources. Data collection was carried out through unstructured interviews and then deepened through observation. The informants for this research consisted of 7 people who had worked and were currently working in several parts of Islamic Bank X, city Y. This research used qualitative data analysis techniques with three stages in the form of data reduction, data presentation and conclusion. This research reveals the motives for fraudulent behaviour at Islamic Bank X in City Y from the perceptive fraud hexagon model. Fraudulent behaviour is based on the perpetrator's intention and is driven by motives in the form of pressure, rationalization, opportunity, ability, arrogance and collusion. Companies can use the results of this research to consider strategies and policies for controlling risks against fraud. The results of this research can also be helpful for Islamic Banks regarding the suitability of a person's position. Sharia banks must develop their human resources well so that there is a balance between employee rights and obligations.

Credit Card Fraud Detection System

Globally, credit card fraud is a serious threat to people, businesses, and financial institutions. With the rise of online transactions, fraudsters have developed clever ways to take advantage of loopholes in payment systems. Traditional fraud detection methods based on manual inspections and rules-based systems are unable to counteract this new and evolving risk. As a result, the use of data analytics and machine learning has become a viable option for real-time detection and prevention of credit card fraud. The paper looks at using machine learning algorithms such as logistic regression, decision trees, random forests, neural networks, etc. to detect fraudulent transactions We go over the importance of data sources and components, analytical metrics, and how fraud detection on the effectiveness of examples. In addition, we list the current challenges and directions in which credit card fraud detection is likely to continue, including the use of blockchain technology and sophisticated AI techniques. Overall, this study highlights the importance of credit card theft detection and the promise of machine learning in mitigating this ubiquitous problem financial institutions use advanced machine learning algorithms and analytics function to detect fraudulent behaviour, protect customer interests, and maintain payment environment integrity to improve their capabilities.

Research on Financial Fraud in the Supply Chain of NianFu Company Under Performance Commitment - From the Perspective of Business Ethics and Accounting Professional Ethics

With the vigorous development of China's economy, business ethics in the field of market economy has become the focus of social discussion. In recent years, cases of financial fraud to realize their interests are common, disrupting the healthy development of the capital market and damaging the vital interests of small and medium-sized investors. This paper takes the typical illegal case of the securities regulatory inspection in 2021 - the financial fraud case of the Nianfu supply chain as an example. First, the basic situation of the case is introduced; Secondly, it exposes the means of financial fraud and analyzes the motivation of fraud based on the triangle theory of financial fraud, and finds that enterprises commit financial fraud out of financing needs and under the pressure of performance commitment. Finally, it analyzes the behavior of financial fraud in violation of business ethics and accounting professional ethics, and puts forward corresponding countermeasures and suggestions.

Health insurance fraud detection based on multi-channel heterogeneous graph structure learning

Health insurance fraud is becoming more common and impacting the fairness and sustainability of the health insurance system. Traditional health insurance fraud detection primarily relies on recognizing established data patterns. However, with the ever-expanding and complex nature of health insurance data, it is difficult for these traditional methods to effectively capture evolving fraudulent activity and tactics and keep pace with the constant improvements and innovations of fraudsters. As a result, there is an urgent need for more accurate and flexible analytics to detect potential fraud. To address this, the Multi-channel Heterogeneous Graph Structured Learning-based health insurance fraud detection method (MHGSL) was proposed. MHGSL constructs a graph of health insurance data from various entities, such as patients, departments, and medicines, and employs graph structure learning to extract topological structure, features, and semantic information to construct multiple graphs that reflect the diversity and complexity of the data. We utilize deep learning methods such as heterogeneous graph neural networks and graph convolutional neural networks to combine multi-channel information transfer and feature fusion to detect anomalies in health insurance data. The results of extensive experiments on real health insurance data demonstrate that MHGSL achieves a high level of accuracy in detecting potential fraud, which is better than existing methods, and is able to quickly and accurately identify patients with fraudulent behaviors to avoid loss of health insurance funds. Experiments have shown that multi-channel heterogeneous graph structure learning in MHGSL can be very helpful for health insurance fraud detection. It provides a promising solution for detecting health insurance fraud and improving the fairness and sustainability of the health insurance system. Subsequent research on fraud detection methods can consider semantic information between patients and different types of entities.

Uncovering Sophisticated Discrimination with the Help of Credence Goods Markups: Evidence from a Natural Field Experiment

Credence goods, such as repair and healthcare services, are characterized by profound information asymmetries between less-informed customers and better-informed expert sellers. These information asymmetries open the door for fraudulent behavior on the seller side. In a preregistered natural field experiment, we vary in one dimension the seller’s perception of whether the service is an ordinary or a credence good service and in the second dimension whether the customer is a member of a minority or a member of the majority. This allows us to measure the size of the induced credence goods markup and to address the question whether it interacts systematically with discrimination. We document the existence of a large credence goods markup, on average. Moreover, we find that members of the minority pay a sizeable discriminatory markup if the good is perceived as a credence good but not if it is perceived as an ordinary good. Our results show that sellers engage in sophisticated discrimination where informational asymmetries are used to hide discriminatory (fraudulent) behavior. With the help of an ex post survey, we derive a possible explanation for our results. This paper was accepted by Maria Claire Villeval, behavioral economics and decision analysis. Funding: Financial support was provided by the Austrian Science Fund [Special Research Area Grant SFB F63 and Grants P26901 and P27912]. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.02666 .

SIX CULTURE HOFSTEDE AND STUDENT ACADEMIC FRAUD AT PRIVATE UNIVERSITIES IN BALI

Academic fraudulent behavior is a dishonest act that violates ethics in the academic sphere at both the student, lecturer and administration levels which harms other parties. This research wants to test the influence of Hofstede's cultural dimensions on student academic fraud. This research was conducted at Warmadewa University in Bali. The population in this study were 2020 Even semester students of the Accounting Department, Faculty of Economics and Business, Warmadewa University in Bali, totaling 2456 students. To determine the minimum sample size, the researcher used the Slovin formula, namely 100 students as a sample. This research uses an interval scale with 5 Likert scales. Data analysis in research uses Partial Least Square (PLS) with the SmartPLS program. The results show that the cultural dimension of power distance has no effect on academic fraud, the cultural dimension of uncertainty avoidance has a negative and significant effect on academic fraud, the cultural dimension of individualism has a negative and significant effect on academic fraud, the cultural dimension of masculinity has a positive and significant effect on academic fraud, the cultural dimension of orientation long term has a negative and significant effect on academic fraud, and the cultural dimension of indulgence has a negative and significant effect on academic fraud

Attitudes toward fraudulent behavior and the decision to file first‐party and third‐party bodily injury auto insurance claims

AbstractThis research explores the presence of ex post moral hazard in auto insurance claim filing decisions by determining the relationship between attitudes toward fraudulent behavior and the decisions by those injured in auto accidents to file bodily injury auto insurance claims. Of particular interest is the effect of these attitudes on the propensity to file first‐party bodily injury auto insurance claims, which has not been specifically identified in earlier studies. A multinomial logistic regression model is used to take advantage of survey data that directly capture the first‐ and third‐party auto insurance claim filing decisions for bodily injuries suffered in auto accidents across the United States between 1995 and 1998. The results indicate that, in addition to third‐party claims, ex post moral hazard is present in first‐party bodily injury auto insurance claim filing decisions as well despite possible differences in the incentives to commit first‐party and third‐party insurance fraud.

Abnormal-trajectory detection method based on sub-trajectory classification and outlier-factor acquisition

Abnormal-trajectory detection can be used to detect fraudulent behavior of taxi drivers transporting passengers. Aiming at the problem that existing methods do not fully consider abnormal fragments of trajectories, this paper proposes an abnormal-trajectory detection method based on sub-trajectory classification and outlier-factor acquisition, which effectively detects abnormal sub-trajectories and further detects abnormal trajectories. First, each trajectory is reconstructed using the turning angles and is divided into multiple sub-trajectories according to the turning angle threshold and trajectory point original acceleration. The sub-trajectories are then classified according to the extracted directional features. Finally, the multivariate distances between angular adjacent segments are calculated to obtain the outlier factor, and abnormal sub-trajectories are detected. The sum of the lengths of the abnormal sub-trajectories is used to calculate the outlier score and identify abnormal trajectories. Based on experimental results using real trajectory datasets, it has been found that the proposed method performs better at detecting abnormal trajectories than other similar methods.

Trustworthy semi‐supervised anomaly detection for online‐to‐offline logistics business in merchant identification

AbstractThe rise of online‐to‐offline (O2O) e‐commerce business has brought tremendous opportunities to the logistics industry. In the online‐to‐offline logistics business, it is essential to detect anomaly merchants with fraudulent shipping behaviours, such as sending other merchants' packages for profit with their low discounts. This can help reduce the financial losses of platforms and ensure a healthy environment. Existing anomaly detection studies have mainly focused on online fraud behaviour detection, such as fraudulent purchase and comment behaviours in e‐commerce. However, these methods are not suitable for anomaly merchant detection in logistics due to the more complex online and offline operation of package‐sending behaviours and the interpretable requirements of offline deployment in logistics. MultiDet, a semi‐supervised multi‐view fusion‐based Anomaly Detection framework in online‐to‐offline logistics is proposed, which consists of a basic version SemiDet and an attention‐enhanced multi‐view fusion model. In SemiDet, pair‐wise data augmentation is first conducted to promote model robustness and address the challenge of limited labelled anomaly instances. Then, SemiDet calculates the anomaly scoring of each merchant with an auto‐encoder framework. Considering the multi‐relationships among logistics merchants, a multi‐view attention fusion‐based anomaly detection network is further designed to capture merchants' mutual influences and improve the anomaly merchant detection performance. A post‐hoc perturbation‐based interpretation model is designed to output the importance of different views and ensure the trustworthiness of end‐to‐end anomaly detection. The framework based on an eight‐month real‐world dataset collected from one of the largest logistics platforms in China is evaluated, involving 6128 merchants and 16 million historical order consignor records in Beijing. Experimental results show that the proposed model outperforms other baselines in both AUC‐ROC and AUC‐PR metrics.

The Influence of Corporate Governance Mechanisms on Fraud Probability: Evidence from Russian Companies

This study examines the impact of corporate governance mechanisms on the probability of corporate fraud occurrence. We evaluate the board size, the degree of independence, and the frequency of meetings of the board and its committees. We also attempt to analyse the board’s gender diversity, but since boards are not gender-diverse in Russia, the significance of this variable cannot be tested. Our empirical study is based on 160 observations of MOEX-listed Russian companies, among which fraudulent behaviour has been revealed in 32 companies over a 5-year period from 2014 to 2018. The relationship between the probability of fraud occurrence and corporate governance was investigated employing a logit model. The data was collected from firms’ annual reports and Thomson Reuters Eikon. Data on fraud cases is based on the evidence from the press (including the leading news sources and specialised websites). We detected a significant negative relationship between nomination and remuneration committee chairmen’s independence, the share of independent directors, the independence of board and audit committee chairmen and the likelihood of fraud. We also discovered the insignificant influence of board and its committees’ size and their meetings’ frequency on fraud probability. This paper contributes to the academic research on the relationship between corporate governance mechanisms and probability of fraud occurrence, emphasizing the special role of the establishment of nomination and remuneration committee chairman independence in Russian companies.

A Comprehensive Approach to Safeguard Credit Card Transactions and Fraud Prevention

The escalating prevalence of financial fraud within the financial sector poses profound challenges. Detecting credit card fraud in online transactions necessitates data mining due to inherent complexities. Addressing two key issues—evolving patterns in legitimate and fraudulent behaviors and highly skewed datasets of credit card frauds—renders the task challenging. This paper scrutinizes the performance of naive Bayes, KNN, and logistic regression on significantly imbalanced credit card fraud data comprising 284,807 transactions from European cardholders. The dataset's skewness is addressed through a hybrid under-sampling and oversampling approach. The three techniques are applied to both unprocessed and preprocessed data. Fraud detection, defined as a set of activities thwarting illicit acquisition of assets or funds through deceptive means, varies across industries and methods. Credit card fraud, particularly susceptible due to its ease and prevalence in e-commerce and online platforms, prompted the adoption of diverse machine learning strategies to combat rising fraud rates. This paper employs machine learning algorithms for credit card fraud detection, utilizing a publicly available credit card dataset for model evaluation. While acknowledging that achieving 100% accuracy in fraud detection is elusive, the paper emphasizes the real-world applicability of its findings through the analysis of credit card data from a financial institution. In addition to assessing model efficacy, the study introduces noise into the data samples to evaluate algorithm robustness. Experimental outcomes underscore the effectiveness of the majority voting method, achieving commendable accuracy rates in detecting credit card fraud cases. The study sheds light on the pressing issue of credit card fraud, emphasizing the importance of deploying robust machine learning approaches for timely and accurate detection in real-world scenarios.

Approaching the gateway to doping hypothesis through personality analysis

The relation between dark personality traits and fraudulent behaviour like doping in sports is empirically well established. However, the factors that lead athletes to start using illegal substances are less known. The gateway to doping hypothesis posits that widespread dietary supplement usage among athletes could potentially desensitise them, increasing the inclination towards doping use. To examine this hypothesis, the personality structure of athletes using dietary supplements for diverse motives is examined, aiming to identify patterns indicative of doping propensity, including dark traits, perfectionism, ego and task orientation and attitude towards doping. The final sample comprises 687 athletes (479 women; mean age = 26.06 ± 9.21 years). The findings suggest that the overall use and frequency of dietary supplement intake are not significantly predicted by personality traits indicative of a predisposition towards doping. While athletes that use supplements to enhance performance, regeneration, and body shape display a slightly more favourable attitude towards doping, dark traits exhibit an inverse link across all motives (except body shape enhancement) with substantial effect sizes. Although doping usage is not assessed in this study, the outcomes indicate that personality structures associated with doping do not predict dietary supplement use, thus challenging the gateway to doping hypothesis.

Beyond Expectations: Corporate Governance's Insufficiency in Fraud Prevention

Objective –The specific objectives to be achieved in this research are to (1) test the Beneish model (1999) as a predictor of reliable manipulation or indicator to reveal signals or symptoms of financial reporting fraud. (2) Test whether corporate governance effectiveness reduces financial statement fraud.Design/Methodology –The research analyzed publicly listed non-financial firms listed in Indonesia's stock exchange (IDX) from 2017 to 2022. Simple random sampling was used to select 86 companies per year, resulting in 617 year-firm observations. Data analysis was carried out by logistic regression multiple with panel data. The test was carried out by adopting the Beneish benchmark index (Z-score) model which is effective for predicting indications of fraudulent financial statements. Results –The test results show that there is 99.8% of incidents indicated financial statement fraud and the other 0.2% did not indicate financial statement fraud. Corporate governance mechanisms consisting of institutional ownership, audit committees, and audit quality does not mitigate fraud. It is anticipated that the research's contribution will serve as a practical guide and reference for the examination of indicators of fraud in corporate finances. The findings of this study are expected to provide valuable insights for policymakers and regulators seeking to improve corporate governance practices.Research limitations/implications –This research does not incorporate the competency and capability specifications of governance structures such as the board of commissioners, audit committees, and external auditors. Future investigations are anticipated to delve more profoundly into the internal and external corporate governance capabilities and specifications.Novelty/Originality –This study reveals that, on the whole, a significant proportion of companies are implicated in fraudulent activities, while corporate governance mechanisms are deemed inadequate in overseeing management activities conducive to fraud. The outcomes of this research challenge the prevailing paradigm of corporate governance as an effective deterrent against fraudulent behavior.