Financial Fraud Research Articles

Factors Impacting the Use of Donation-Based Crowdfunding Platforms in Saudi Arabia: The Case of Ehsan Platform

This research investigates the factors influencing people's intentions to use donation-based crowdfunding platforms in Saudi Arabia, focusing on the Ehsan platform supervised by the Saudi Data and Artificial Intelligence Authority (SDAIA). Ehsan aims to ensure proper monetary distribution to genuinely needy individuals, thereby reducing financial fraud and corruption. For the platform's success, it is essential that citizens effectively utilize it. Employing the Unified Theory of Acceptance and Use of Technology, the study analyzes the determinants that influence intentions to use Ehsan and actual use behaviors. An empirical study was conducted with 224 Saudi citizens from various age groups, and the research model was validated using the Partial Least Squares (PLS) approach. Findings reveal that performance expectancy and facilitating conditions significantly impact intentions to use Ehsan, while effort expectancy and social influence do not. The results indicate a positive association between participants' intentions to use Ehsan and their actual usage. This study enhances the understanding of factors affecting donation-based crowdfunding platforms in Saudi Arabia and offers practical insights for platform developers and decision-makers to consider in their design and modifications.

To Study on Cyber Crime Burgeoning in India and Crucial Actions Need for Every Cybercrime Victim

India is facing a great deal of challenges due to the rapid digitalization and broad adoption of technology in many different areas. One such concern is cybercrime. In this paper explores the nature, scope, and impact of cybercrime in the Indian context. The rising ubiquity of internet usage has rendered people, companies, and governmental organizations more susceptible to a range of cyber threats, such as identity theft, financial fraud, data breaches, and cyber harassment. Despite developments in cyber security protection, the lack of awareness and poor regulatory frameworks has aggravated the situation. To address these issues, the Indian government has put in place a number of efforts, including the National Cyber Security Policy and the Information Technology Act, 2000. But enforcement is still patchy, and a lot of crimes go unreported or ignored. The problem has been made worse by insufficient regulatory frameworks and a lack of knowledge, even in spite of advances in cyber security measures. But enforcement is still patchy, and a lot of crimes go unreported or ignored. This research paper emphasizes India's cybercrime victims have several legal options, the need for a multimodal strategy that includes improved cyber security infrastructure, better legislative frameworks and greater public awareness in order to tackle cybercrime. In light of the transnational character of cyber threats, it also highlights the significance of international cooperation. In the end, a thorough plan is necessary to protect India's digital environment and guarantee the security and safety of its people in a world that is becoming more interconnected by the day

Cryptocurrency Transaction Fraud Detection Based on Imbalanced Classification With Interpretable Analysis

This study introduces an interpretable imbalanced data classification method for detecting cryptocurrency transaction fraud. We address data imbalance using SMOTE oversampling and data augmentation through contrastive learning. Next, we introduce a Transformer-based deep learning model that learns sample relevance. The model undergoes pre-training with a contrastive loss and fine-tuning through Bayesian optimization to effectively extract high-dimensional, higher-order, and fraud-related features. We employ a SHAP-based interpreter along with attention scores to elucidate the role of various transaction features in fraud detection. Comparative results demonstrate the model's remarkable recall performance in identifying cryptocurrency transaction fraud. Furthermore, it achieves an excellent F1 value, striking a balance between accuracy and recall. This research not only enriches financial fraud detection but also enhances cryptocurrency transaction security, promotes market development, and contributes to economic stability and social security.

Research on the Construction of Professional Ethics of Accounting Personnel

Abstract: Accounting basic professional ethics refers to the behavior and norms that accountants should follow. It reflects the professional skills, professional ethics and influence on the social economic environment of accountants. With the development of the times, financial information has become the basis for the parties to participate in the decision of the social market economy, so the importance of the basic professional ethics of accounting has become increasingly prominent. In recent years, accounting fraud has become more and more serious due to the irregularities of accountants' basic professional ethics, which has affected the development of the social market economy. Therefore, it is urgent to strengthen the construction of accounting professional ethics, improve the moral standard of accountants, resolutely crack down on false accounts, standardize the order of our accounting market and eliminate corruption. Although accounting ethics receives a lot of attention today, many scholars still treat it as a separate discipline, rather than just combining it with false financial statements, fraud, and fraudulent activities. This article delves into the ethical standards of accounting practitioners in China and explores the reasons for this phenomenon from different perspectives. Through this work, it proposes some effective measures to promote the fairness and transparency of China's accounting work and create a healthy financial environment for society.

How Does Judges’ Personal Exposure to Financial Fraud Affect White‐Collar Sentencing?

ABSTRACTWe study whether federal judges’ personal exposure to financial fraud affects their professional behavior, in the form of sentencing outcomes in white‐collar cases. Following the methodology outlined in our registered report, we construct a novel measure of financial fraud exposure based on judges’ direct shareholdings in firms that commit financial fraud. Using this measure, we exploit the random assignment of cases to judges to examine whether judges exposed to fraud in one firm are (1) less likely to rule in favor of defendants in white‐collar cases involving other firms and (2) less likely to grant favorable pretrial motions to defendants. We find minimal evidence in support of either (1) or (2), concluding that for all but the most serious frauds, judges are unlikely to let their personal victimhood experience affect their professional sentencing behavior with respect to related cases. Our study broadens our understanding of the spillover effects of financial fraud enforcement and contributes to the literature on how judges’ personal experiences can shape judicial decision‐making.

Ontology Development for Asset Concealment Investigation: A Methodological Approach and Case Study in Asset Recovery

The concealment of assets is a critical challenge in financial fraud and asset recovery investigations, posing significant obstacles for creditors and regulatory authorities. National governments commonly possess the necessary data for detecting and combating this type of fraud, typically related to personal data and asset ownership. However, this information is often dispersed across different departments within the same government and sometimes in databases shared by other countries. This leads to difficulty semantically integrating this large amount of data in various formats and correlating entities through identifying hidden relationships, which are essential in this type of analysis. In this regard, this work proposes an ontology to support the data integration process in the domain of asset concealment and recovery and fill the gap in the existence of a public ontology for this domain. The applicability of this ontology in the context of integration between data from different departments and countries was validated. The use of the ontology in a pilot project in the context of a tool for investigating this type of fraud was conducted with a Brazilian government agency, and the users validated its applicability. Finally, a new method for constructing ontologies is proposed. The proposed process was evaluated during the asset concealment ontology building and proved to be more suitable than the similar processes analyzed concerning the partial reuse of existing ontologies and the construction of ontologies for data with a transnational scope.

Mitigating Financial Fraud in Indonesia: A Grounded Theory Approach to Fraud Detection in P2P Lending, Rural Banks, and Conventional Banks

The rapid digitalization of Indonesia’s financial sector, driven by the growth of Peer-to-Peer Lending (P2PL) platforms, Rural Credit Banks (BPR), and conventional banks, has expanded financial inclusion but also heightened the risk of fraud. This study investigates the fraud detection systems (FDS) employed by these diverse financial institutions using a grounded theory approach. By conducting semi-structured interviews with key stakeholders—including directors, compliance managers, and fraud control officers—the research identifies critical factors influencing fraud detection in Indonesia’s financial ecosystem. The study reveals that while larger institutions leverage advanced machine learning models and real-time transaction monitoring, smaller entities like BPRs rely heavily on manual oversight due to resource constraints. Furthermore, the integration of multi-subject perception differences is highlighted as a novel and effective approach for fraud detection, particularly in P2PL platforms, by analyzing multiple data sources to reduce bias and increase accuracy. However, the study also uncovers significant challenges in implementing robust FDS, including high false-positive rates, regulatory complexities, and resource limitations, particularly in smaller institutions. The findings emphasize the need for a tailored fraud detection framework that addresses the unique needs of each sector, balancing technological advancements with operational feasibility.

Research on Fraud Detection Method of Financial Data of Listed Companies Based on HMCRAN

This study investigates the application of deep learning technology to improve accuracy and efficiency in detecting financial fraud in listed companies. Using data from the China Economic and Financial Research Database on Main Board A-share listed companies with disclosed fraudulent behaviors, a hybrid multi-channel residual attention network (HMCRAN) was constructed, combining multi-channel residual networks and transformers. The study involved feature extraction, data preprocessing, model training, and performance evaluation, validating the superiority of the HMCRAN model for financial fraud detection. Experimental results demonstrate that the model excels in handling high-dimensional financial data and capturing long-term dependencies, significantly improving detection accuracy and robustness. The HMCRAN model outperforms traditional machine learning methods across various evaluation metrics, demonstrating its high practical application value.

Budget participation and financial information asymmetry: Behavior of budget users and financial report fraud

Financial statement fraud is a problem often faced by organizations that can reduce their performance. An organization that successfully achieves its goals must be aware of information asymmetry and the need for moral justice in managing its finances. The research population comprises employees of the finance, planning, reporting, and budget preparation sections of the Gowa Regency regional performance unit. This research selected a saturated sample of 76 employees from the total population. The results show that participatory budgeting has a significant effect on financial report fraud and that information asymmetry has a substantial influence on financial statement fraud. The research results show that moral justice moderates the effect of participatory budgeting on financial report fraud. Moral justice cannot moderate the influence of information asymmetry on financial statement fraud. Employees must still have good moral justice in participatory budgeting to minimize the possibility of fraudulent financial reports. It is hoped that the findings of this research will enable organizations to create innovative information facilities to reduce information asymmetry so that organizational performance can be achieved.

The Fraud Pentagon Analysis In Financial Companies On The Indonesia Stock Exchange

This research was conducted to examine the effect of the fraud pentagon theory developed by Crowe Horwath on the possibility of fraud in financial statements. The sample used in this study came from 105 financial companies listed on the Indonesia Stock Exchange (IDX) during the 2020-2022 period. Sampling was carried out using the purposive sampling method. The data analysis method is logistic regression test analysis, processed using the Eviews application. The Pentagon theory posits five key factors: pressure, opportunity, competence, arrogance, and rationalisation. Our analysis revealed that while pressure and opportunity, as proxied by financial targets, stability, external pressure, and ineffective supervision, did not significantly influence fraud risk, the quality of the external auditor emerged as a significant determinant. Conversely, changes in auditors (rationalisation), directors (competence), and the frequency of CEO pictures (arrogance) did not appear to be significant predictors of fraud. This study suggests that external audit quality is more significant in preventing financial statement fraud than the traditional fraud triangle (pressure, opportunity, rationalisation) or other factors like competence and arrogance.

Analysis of Financial Fraud Based on the GONE TheoryA Case Study of Hangzhou Century Co., Ltd.

Convertible bonds allow creditors to convert their claims into company shares under certain conditions. Because of this debt-equity conversion feature, the issuance conditions, pricing and conversion value of convertible bonds are highly susceptible to financial information. Increasing the market value of a company's stock by exaggerating earnings or concealing debt may lead to convertible bond investment decisions based on misinformation, which in turn affects investors and the healthy operation of the market. Based on the GONE theory, this paper analyses the motivation of financial fraud in convertible bond documents and proposes corresponding preventive measures for its characteristics. Through the research and analysis of this type of financial forgery, the preventive governance of financial forgery can be further strengthened, which not only helps to protect the company's reputation and ensure the accuracy and reliability of the financial report, but also helps to attract and maintain the trust of investors, avoid possible legal risks and economic losses, and create a transparent and honest financial environment for the market to promote the efficiency of the market and the fair competition of enterprises.

CEO Demographics, Board Gender Diversity and Financial Statements Fraud among Listed Firms in East Africa Community

CEO Demographics, Board Gender Diversity and Financial Statements Fraud among Listed Firms in East Africa Community

The Ripple Effects of Financial Fraud: Investor Trust and Risk Appetite Post-Kangmei Pharmaceutical

The financial fraud at Kangmei Pharmaceutical (stock code: 600518) has had profound implications for retail investors. This paper, through data analysis, explores the correlation between information asymmetry and investment outcomes for retail investors. Information asymmetry often leaves retail investors unable to access the true financial state and operational conditions of companies, leading them to make speculative investments. Post-exposure, as information transparency increased, retail investors became aware of the company's financial issues and rushed to sell, causing a stock plunge. This highlights how information asymmetry not only affects investment decisions but also amplifies market volatility. To mitigate risks from information asymmetry, regulators must strengthen oversight and penalties, improve disclosure transparency and authenticity, while investors need to enhance self-protection awareness and information gathering and analysis capabilities. Only then can we minimize the impact of information asymmetry on retail investors and uphold fairness and stability in financial markets. This paper suggests that by implementing a robust regulatory framework and encouraging proactive investor education, we can better safeguard retail investors from the detrimental effects of information asymmetry, thereby fostering a more resilient and equitable financial market ecosystem.

Mendeteksi Kecurangan Laporan Keuangan Dengan Menggunakan Analisis Rasio Keuangan

The aim of this research is to test financial ratios as a tool for detecting fraudulent financial statements. This research uses the monitoring premise of agency theory to explain the association between financial ratios and financial statement fraud. Proxies for measuring financial ratios include four financial ratios, namely debt ratio, profitability, cash flow and accruals. Financial reporting fraud uses an occurrence-based proxy, namely a dummy for regulatory violations in sample companies. The samples tested were manufacturing companies registered on the IDX during 2016-2018. The results of this research show that empirically financial ratios are not yet fully capable of being used to detect fraudulent financial statements.

Harnessing AI for Next-Generation Financial Fraud Detection: A Data-Driven Revolution

Artificial Intelligence in Financial Fraud Detection: A Comprehensive Approach to Enhancing Financial Security The rise of artificial intelligence (AI) offers an opportunity to significantly strengthen financial security by combating financial fraud, which has become increasingly complex and widespread. Traditional detection methods are often insufficient in identifying and preventing fraudulent activities, prompting a shift towards AI-based solutions. This study explores the application of AI, particularly machine learning algorithms, in improving the accuracy and efficiency of fraud detection. By analyzing large financial datasets, AI can detect anomalies that may indicate fraudulent behavior more effectively than traditional approaches. This research adopts a two-phase methodology. The first phase involves a thorough review of existing financial fraud detection methods, comparing traditional techniques with AI-based models to identify gaps. Various machine learning approaches, including supervised, unsupervised, and deep learning algorithms, are reviewed for their effectiveness in detecting fraud. The second phase involves developing and testing an AI model to identify fraudulent patterns within transactional data. The model uses machine learning algorithms to process vast datasets and detect deviations from typical financial behaviors, flagging potentially fraudulent activities. The expected results indicate that AI systems can outperform traditional fraud detection methods by significantly reducing false positives and improving the detection rate of genuine fraud. This reduction in false positives is vital for financial institutions, as it reduces unnecessary investigations and saves valuable resources. Additionally, enhanced fraud detection protects both institutions and consumers from financial losses.The findings of this study aim to provide financial institutions with practical insights into the implementation of AI-driven fraud detection systems. Furthermore, the research highlights the need for continuous refinement of AI models to adapt to the evolving nature of financial fraud. By leveraging AI technologies, financial institutions can revolutionize their approach to fraud detection, making financial systems more secure and responsive to emerging threats.

Non-Shariah Compliance Risks (NCR) and the Danger of Potential Fraud in the Islamic Financial Institutions: A Conceptual Framework

This research paper is a conceptual study that aims to examine financial fraud and its relation to non-compliance to Shariah for the Islamic financial institution in Malaysia. The study is unique because it seeks to understand how deviations from Shariah guidelines could contribute to financial misconduct, examining the specific non-Shariah compliance risks that arise when the deviation of internal control fails to align with Islamic law. Utilizing quantitative methodology, this study adopts and adapts from an existing structured questionnaire survey to collect data. This study also employs Structural Equation Modelling (SEM) in its analysis of the data. The implication of this study for policymakers includes the development of an effective strategy for mitigating financial fraud and ensuring Shariah compliance, thus ensuring the integrity and reputation of the Islamic financial sectors. This will undoubtedly contribute to the stability of the economy.

Graph neural network for fraud detection via context encoding and adaptive aggregation

Financial fraud is a vital issue which causes considerable economic losses for financial institutions. Existing studies apply various methods to detect fraudsters from financial data. In particular, methods based on Graph Neural Networks(GNNs) are among the most effective. However, applying graph neural networks to fraud detection still faces challenges of capturing two types of fraudulent behaviors: the context-related behavior and camouflage-related behavior. To address the challenges, we present Context-encoding-and-Adaptive-aggregation-based Fraud Detector(CAFD), a novel GNN-based fraud detection model. To capture the context-related fraudulent behavior, we devise a context encoding module which encodes the time intervals of out-edges and out-degree for each node in the graph. In addition, we design an adaptive aggregator which identifies the camouflage-related behavior by performing random dropping on messages during the message passing process of GNNs. We evaluate the performance of CAFD with extensive experiments on two real-world datasets. Results of the experiments demonstrate that our model outperforms state-of-the-art baselines.

Can value-added tax refund policy inhibit corporate financial fraud? Evidence from China

This paper examines the impact of China's 2018 VAT refund policy on corporate financial fraud, using a difference-in-differences approach. Findings demonstrate that the policy significantly reduces corporate financial fraud, which is confirmed through various robustness tests. The VAT refund policy deters corporate financial fraud by addressing motivational pressures, limiting opportunities, and challenging justifications, effectively targeting the fraud triangle's core elements. The policy's effects are especially strong in high-tech and privately-held firms. The study extends knowledge of VAT policies' roles in corporate decision-making and offers policy guidelines to combat corporate malfeasance.

Leveraging AI Algorithms to Combat Financial Fraud in the United States Healthcare Sector

Financial fraud is a major problem in the healthcare industry because it causes large financial losses and compromises the integrity and trust of healthcare systems. The intricacy and sophistication of contemporary fraudulent operations make conventional fraud detection techniques which rely on manual audits and rule-based systems increasingly inadequate. AI algorithms have become a viable way to improve financial fraud detection and prevention. Hence, this paper examines how AI algorithms can be used to detect and stop fraud in the healthcare industry, emphasizing how these algorithms could revolutionize fraud control procedures. This study suggests that AI algorithms greatly improve the identification of financial fraud in the healthcare industry by spotting intricate patterns and abnormalities frequently overlooked by already existing techniques. Machine learning models have proven to be highly accurate in predicting fraudulent claims and transactions. However, while AI provides numerous opportunities to improve fraud detection skills, its effective application necessitates resolving important issues, including ethical considerations, data governance, and model interpretability.

MORPHIC COMPUTING WITH MACHINE LEARNING FOR ENHANCED FRAUD DETECTION IN FINANCIAL APPLICATIONS

As financial fraud becomes increasingly complex, traditional detection methods struggle to keep pace, resulting in substantial financial losses globally. Morphic computing—a paradigm that emphasizes adaptable, context-aware processing—offers promising advancements for fraud detection in dynamic environments. Integrating morphic computing with machine learning models creates a responsive framework capable of discerning subtle and evolving fraud patterns. The proposed system utilizes a Convolutional Neural Network (CNN) enhanced with Morphic Layering, where layers adaptively morph in response to new data patterns. The dataset, sourced from real-time financial transactions, consists of 500,000 records, including 2,000 flagged fraudulent cases. The system was tested on a simulated environment over a six-month period, yielding an accuracy of 98.5% in fraud detection and reducing false positives by 40% compared to traditional machine learning models. Latency for real-time detection was minimized to 200 milliseconds, proving feasible for immediate application in transaction monitoring systems. By offering a flexible structure, this method surpasses existing approaches, as it continuously evolves to detect emerging fraud patterns, thus enhancing financial security.