Financial Fraud Research Articles

Fraud Factor Analysis Hexagon in Detecting Financial Report Fraud In Listed Companies in Indonesia: A Systematic Literature Approach

Financial reporting fraud is a significant challenge across industries, undermining stakeholder trust and the reliability of financial data. The Fraud Model Hexagon expands upon earlier models, like the Fraud Triangle and Fraud Diamond, by introducing six elements pressure, opportunity, rationalization, capability, ego, and collusion that offer a comprehensive view of the factors enabling fraud. This literature review systematically examines the model's effectiveness in detecting financial fraud within Indonesian corporations, especially those listed on the Indonesia Stock Exchange or connected to political and governmental entities. Ten studies from 2018 to 2024 were analyzed, focusing on each Fraud Hexagon element's role in fostering fraud. Findings highlight that pressure, opportunity, and collusion are primary drivers of fraud in politically connected sectors, with capability and rationalization further facilitating fraud, while ego emboldens managers to act without fear of repercussions. The Fraud Hexagon provides a nuanced framework for risk assessment, prompting recommendations for stronger internal controls and ethical standards. It also serves as a valuable diagnostic tool for auditors and regulators, particularly in politically sensitive sectors. This study contributes to understanding the social and psychological dimensions of financial fraud and offers a foundation for future cross-industry research.

Financial Fraud Transaction Prediction Approach Based on Global Enhanced GCN and Bidirectional LSTM

Financial Fraud Transaction Prediction Approach Based on Global Enhanced GCN and Bidirectional LSTM

Societal Impact of Test Automation: Reducing Human Error in Critical Systems

This article explores the profound societal impact of test automation across critical sectors such as healthcare, finance, transportation, and energy. It examines how automated testing processes significantly reduce human error, enhance system reliability, and improve service quality. The article presents compelling evidence from various studies and reports, demonstrating substantial improvements in medical diagnostics, financial fraud detection, transportation safety, and energy distribution efficiency. Beyond error reduction, the article discusses broader societal benefits, including enhanced accuracy in data processing, faster emergency response times, improved service quality, and strategic resource allocation. The article underscores the crucial role of test automation in addressing the challenges posed by increasingly complex technological systems and its far-reaching implications for public safety, economic stability, and overall quality of life.

DeepFake Image Detection: Fake Image Detection using CNNs and GANs Algorithm

Deep learning is a powerful and versatile technique that has seen extensive applications in areas such as natural language processing, machine learning, and computer vision. Among its most recent applications is the generation of deepfakes, which are high-quality, realistic altered videos or images that have garnered significant attention. While innovative uses of deepfake technology are being explored, its potential for misuse has raised serious concerns. Harmful applications, such as spreading fake news, creating celebrity pornography, financial fraud, and revenge pornography, have become increasingly prevalent in the digital age. As a result, public figures, including celebrities and politicians, face heightened risks from deepfake content. In response, substantial research has been conducted to explore the mechanics behind deepfakes, leading to the development of various deep learning-based algorithms for their detection. This study provides a comprehensive review of deepfake creation and detection techniques, focusing on different deep learning approaches. Additionally, it discusses the limitations of existing methods and the availability of datasets for research. The lack of a highly accurate and fully automated deepfake detection system presents a significant challenge as the ease of generating and distributing such content continues to grow. Nonetheless, recent efforts in deep learning have shown promising results, surpassing traditional detection methods.

Banking System of the Russian Federation: Theoretical and Practical Issues of Ensuring the financial Sovereignty of the State

This work is devoted to two main issues: understanding the category of “sovereignty” and the role of the banking system in ensuring the financial sovereignty of Russia. Based on the analysis of doctrinal sources, including past historical eras, sovereignty is considered not only as a constitutional and legal institution, but as a general legal institution as a whole. The types of sovereignty presented in the literature are examined and the question is raised about the need to achieve terminological certainty, a conclusion is made about the possibility of the existence of only such an integral part of state sovereignty as economic, including financial sovereignty, which is understood as the financial security of the state from external and internal threats. The issues of ensuring financial sovereignty in relation to the functioning of the Russian banking system are explored. It is proposed to enshrine financial sovereignty in legislation as a principle — a norm of the banking system. It is stated that the condition for ensuring financial sovereignty is the resolution of a number of internal problems, which include the formation of trust in the banking system, financial fraud, and unfair practices. Important issues are building an effective banking system and systematizing banking legislation.

Federated machine learning in finance: A systematic review on technical architecture and financial applications

Abstract. With the advancement of technology, an increasing amount of data is stored online, including substantial amounts of personal information. Especially in the financial industry, where large sums of money are often involved, protecting personal privacy is crucial. Federated learning, as a privacy-preserving distributed machine learning method, offers a solution to these challenges by enabling data privacy while addressing the difficulties of data sharing in the financial sectorissues that frequently impede innovation, risk management, and fraud detection. This paper delves into the principles of federated learning algorithms, exploring their mechanisms in detail. Besides, applications and case studies in applications such as financial fraud detection, supply chain financing prediction, and other financial services are examined. Furthermore, sample dataset will be introduced. Moreover, the potential benefits and challenges of implementing federated learning within financial contexts are also assessed. Finally, promising research directions for the application of federated learning in the financial industry are outlined.

Detecting financial statements fraud: Evidence from listed companies in China

Financial statement fraud is the deliberate misrepresentation of a company’s financial statements. Financial statement fraud has been a global concern since it not only harms the investors and creditors but also undermine the public confidence of the capital market. Based on the fact that a common incentive for companies to manipulate financial statement is a decline in the company’s financial prospects, this paper applies the mixture hazard early warning model to identify the key impacting financial characteristics in detecting the financial statement fraud for listed companies in China. We find that in the construction industry the warning sign of suspcious business practices is a falling return on assets, while in the real estate industry the financial red flag is an increase in the inventory level. The estimation results indicate that the financial characteristics may have different implications in different industries in detecting financial statement fraud. This research has shed light on setting specific financial characteristics for fraud monitoring and detecting by the regulators.

Роль банків у процесах тінізації економіки України

Introduction. The relevance of the article is due to the growing role of banking institutions in the process of shadowing the Ukrainian economy, which negatively affects the financial stability of the state. Problem Statement. The shadow economy has been identified as one of the main threats to the economic stability and security of the state. The study of the role of banking institutions in illegal transactions by entities whose purpose is money laundering, using various schemes and methods of financial fraud, is considered from the standpoint of substantiating directions and effective tools for countering shadowing of the economy. The implementation of such measures at the national and corporate level in accordance with international norms will make it possible to strengthen control over banking transactions, increase stability of the banking and financial systems and, ultimately, improve the level of economic security of the country. Purpose. Analysis of the role of banking institutions in the shadow economy, justification of directions and tools for counteracting the participation of banks in the shadow economy. Methods. The methodological basis of the study is the fundamental provisions of economic theory, theories of management and state regulation of the economy, economic and mathematical modeling, the theory of money, finance and credit, scientific achievements on the shadow economy and measures to counteract its manifestations. Results. The study identifies the role of banking institutions and proposes a typology of forms of their participation in the shadow economy, including: legalization of proceeds of crime; use of correspondent accounts; use of fictitious companies and front persons; offshore accounts; lending and deposit operations of banks; securities manipulation; and other financial fraud. The impact of these mechanisms on the economic stability of the state and the banking system is analyzed. A comparative analysis of international experience in combating money laundering is carried out, and effective tools for preventing shadow operations are identified. The trends and factors of shadowing the Ukrainian economy are analyzed. To assess the impact of banking institutions on the spread of the shadow economy, it is proposed to use the Simpson distribution for shadow cash flows and discount rates with the participation of banking institutions. The instruments of counteracting the participation of banks in the processes of shadowing the Ukrainian economy are substantiated. Conclusions. Further research is planned to focus on the impact of modern digital technologies, such as blockchain and cryptocurrencies, on money laundering through banking institutions. A promising area is the development of innovative methods of financial monitoring and identification of suspicious transactions, in particular with the use of artificial intelligence and machine learning. It is also important to study the effectiveness of international cooperation and develop recommendations for improving the legislative framework for combating money laundering through banking institutions.

Analisis Faktor - Faktor yang Mempengaruhi Financial Statement Fraud pada Perusahaan Bank Syariah Indonesia: Studi Kasus pada Perusahaan Bank Syariah yang Terdaftar pada Bursa Efek Indonesia Tahun 2021-2023

This study aims to determine the analysis of the analysis of factors that affect the financial statement fraud in the company of bank syariah indonesia quantitative research. The population that was the focus of this study was all Indonesian Islamic banks listed on the Indonesia Stock Exchange during the period 2021 to 2023. The selection of Islamic commercial banks as the subject of research is based on unique principles applied in its operations. Sampling technique using purposive sampling technique. The results of the study can be concluded that the financial targets, financial stability does not affect the financial statement fraud, external pressure affects the financial statement fraud.

Enhancing Counterfeit Banknote Analysis: Case Studies Using TOF-SIMS

Counterfeiting banknotes poses a significant threat to economies worldwide, particularly in developing countries where cash transactions remain predominant. Despite advancements in security features, counterfeiters continue to adapt, necessitating more sophisticated analysis methods. Time of flight Secondary Ion Mass Spectrometry (TOF-SIMS) offers a powerful approach to surface chemical imaging and depth profiling of counterfeit currencies. Real cases of five different counterfeit Lebanese banknotes are studied, using TOF-SIMS to address specific questions regarding the composition and placement of replicated security features, gaining insights into counterfeit banknote production and the raw materials used. The findings provide essential information to decision-makers at the national bank, supporting the design of future currencies through the selection of security features best suited to the local market, thereby enhancing protection against financial fraud..

Correlates of victim services for fraud and identity theft among victim service providers

Victim service providers assist thousands of victims of crime in the United States each year. However, little is known about the extent to which these organizations serve victims of crime often perpetrated online, such as identity theft and financial fraud. Moreover, absent from the literature is an empirical assessment of what factors predict the inclusion of services for victims of these crimes. Using a sample from the 2019 National Survey of Victim Service Providers (n = 1649) the current study investigated factors related to the provision of services for victims of financial fraud and identity theft, and the number of victims served for each of these crime types. Findings suggested that certain organizational services such as providing restitution claim assistance and document replacement services were associated with greater odds providing services to financial fraud and identity theft victims. Organizational characteristics, such as having a hotline/chatline, the number of external partnerships, and the number of volunteers also predicted providing those services. The findings suggest the importance of logistical constraints on providing these services and are discussed in light of previous research on the topic, as well as policy implications and limitations.

Polytope Fraud Theory

Polytope Fraud Theory (PFT) extends the existing triangle and diamond theories of accounting fraud with ten abnormal financial practice alarms that a fraudulent firm might trigger. These warning signals are identified through evaluation of the shorting behavior of sophisticated activist short sellers, which are used to train several supervised machine learning methods in detecting financial statement fraud using published accounting data. Our contributions include a systematic manual collection and labeling of companies that are shorted by professional activist short sellers. We also combine well-known asset pricing factors with accounting red flags in financial features selections. Using 80 % of the data for training and the remaining 20 % for out-of-sample test and performance assessment, we find that the best method is XGBoost, with a Recall of 72 % and F1-score of 82 %. Other methods have relatively lower performance, demonstrating the robustness of our results. This shows that the sophisticated activist short sellers, from whom the algorithms are learning, have excellent accounting insights, tremendous forensic analytical knowledge, and sharp business acumen. Our feature importance analysis indicates that potential short-selling targets share many similar financial characteristics, such as bankruptcy or financial distress risk, clustering in some industries, inconsistency of profitability, high accrual, and unreasonable business operations. Our results imply the possible automation of advanced financial statement analysis, which can both improve auditing processes and effectively enhance investment performance. Finally, we propose the Unified Investor Protection Framework, summarizing and categorizing investor-protection related theories from the macro-level to the micro-level.

Examining Documentation Tools for Audit and Forensic Accounting Investigations

This study examines some of the documentation tools and techniques that forensic accountants, internal auditors, external auditors, and others can use to document accounting and financial reporting systems under investigation. While prior research has addressed these items piecemeal, this study is the first to incorporate them, along with current related research and theoretical foundation, and relate them in aggregate to the work of forensic accountants, internal auditors, external auditors, and others. Inputs, processes, and outputs of modern accounting and financial reporting systems are often difficult to fully grasp, with weaknesses obscured by the complexities of the system. These weaknesses make a system vulnerable to fraudsters, embezzlers, hackers, and others who will take advantage of system weaknesses to perpetrate financial fraud, embezzlement, or other financial crimes. Documentation tools and techniques examined in this study will be useful to forensic accountants, internal auditors, external auditors, and others for identifying the components, processes, and potential weaknesses of accounting and financial reporting systems.

Financial Fraud Detection Study - Based on Logit Model

The problem of financial fraud has always been a key issue of concern for researchers at home and abroad, and financial fraud of listed companies occurs from time to time at home and abroad, which produces a huge loss of interest for investors as well as the downturn and instability of the capital market, for this reason, it is necessary to carry out a study on the detection of financial fraud. This paper analyzes the financial fraud detection of Chinese listed companies by establishing a Logit model, firstly, obtain the financial statement sample data of Chinese listed companies from CSMAR, and divide the sample into training set and test set with the ratio of 9:1 to preprocess the sample data and fill in the missing values; secondly, this paper selects the financial statement data, and based on the previous research, selects the characteristics that are related to the risk of financial fraud and constitute the risk of financial fraud. high features, and constitute the feature indicators for financial fraud detection; again, since detecting financial fraud is a binary classification problem, the sample is divided into fraudulent companies and normal companies, so this paper studies the financial fraud problem through the discrete choice model in the econometric model, constructs a Logit model, conducts a goodness-of-fit test on the sample data of CSMAR, and estimates the parameters using the method of maximum likelihood estimation ; finally, the test set data is used to test the model's ability to predict financial fraud.

The Role of Internal Control on Financial Fraud: Evidence of Pre-Fraud and Post-Punishment from Chinese Firms

ABSTRACT Our study is the first to provide systematic evidence of the internal control (IC) prevention and punishment effect on financial fraud (FF). We examine the pre-fraud and post-punishment IC of Chinese firms. Results show that the lower the firm’s IC, the greater the possibility of FF. We find that IC five elements have different prevention effects. Information and communication cannot prevent FF, while the other four can. Executive compensation, audit fees and earnings management can weaken the negative correlation between IC and FF, while independent director attendance can strengthen the negative correlation between them. In addition, the IC of fraudulent firms has been improved after punishment. This article extends the role of IC and its five elements in FF and the response of IC to punishment, providing country-specific evidence for other emerging market research about IC prevent FF.

Enhancing financial fraud detection using adaptive machine learning models and business analytics

Financial fraud continues to be a critical threat to businesses and economies worldwide, necessitating advanced detection techniques. This paper reviews the role of adaptive machine learning (ML) models and business analytics in enhancing fraud detection systems. Traditional fraud detection methods often fall short in addressing the complexity and evolving nature of fraudulent activities, making adaptive ML models, such as decision trees and neural networks, more effective in identifying subtle patterns in large datasets. Organizations can refine ML models by integrating business analytics, ensuring real-time detection and continuous improvement of fraud detection systems. The paper also explores the challenges in deploying these technologies, including data privacy concerns and model accuracy, as well as the potential impact of emerging technologies such as blockchain and quantum computing. Future directions emphasize improving the interpretability of ML models and leveraging multi-modal data for a more holistic approach to fraud prevention. The synergy between adaptive machine learning and business analytics promises a more resilient and effective framework for combating financial fraud.

Leveraging big data analytics to combat emerging financial fraud schemes in the USA: A literature review and practical implications

The rise of emerging financial fraud schemes in the USA has presented significant challenges for financial institutions, regulators, and law enforcement agencies. As fraud tactics become increasingly sophisticated, traditional methods of detection and prevention are proving insufficient. This literature review explores the role of big data analytics as a critical tool in combating financial fraud, focusing on its practical applications and effectiveness. By leveraging machine learning, predictive analytics, and artificial intelligence, big data analytics enables real-time monitoring and detection of fraudulent activities, offering a more dynamic and adaptive approach to fraud prevention. The review synthesizes key studies, case reports, and industry practices, highlighting the implementation of big data in analyzing large volumes of transactional and behavioral data to identify suspicious patterns. It further discusses the challenges associated with integrating big data technologies into existing systems, including issues related to data privacy, regulatory compliance, and the high cost of implementation. Additionally, the paper assesses the impact of big data solutions on fraud prevention, comparing their effectiveness to traditional methods and identifying best practices for institutions looking to adopt these technologies. Finally, the review proposes future directions, emphasizing the potential of emerging technologies such as artificial intelligence and blockchain to enhance fraud detection capabilities. It offers recommendations for financial institutions, regulators, and policymakers to collaborate in developing more robust strategies to safeguard the financial system from evolving fraud threats. This paper provides a comprehensive analysis of the current state of big data analytics in combating financial fraud and its practical implications for the financial services industry in the USA.

A comprehensive survey of cybercrimes in India over the last decade

Since the 1990s, the integration of technology into daily life has led to the creation of an extensive network of interconnected devices, transforming how individuals and organizations operate. However, this digital transformation has also spurred the rise of cybercrime, criminal activities perpetrated through networks or computer systems. Cybercrime has become a global concern, presenting significant challenges to security systems. Although advancements in digital technology have enhanced efficiency, they have also opened new avenues for exploitation by cybercriminals, highlighting the urgent need for advanced cybersecurity measures. The escalating number of cyberattacks and associated risks in the past decade highlights the critical importance of protecting sensitive data and safeguarding information systems. Cybercrimes range from financial fraud and phishing scams to identity theft and online harassment, posing substantial risks to both individuals and organizations. In response, governments, law enforcement agencies, and cybersecurity units have intensified their efforts to address these threats. In recent years, India has experienced a significant surge in cybercrime incidents, with a notable increase in cases involving ransomware, data breaches, and social engineering attacks. The growing penetration of internet services, the expansion of e-commerce, and the rapid adoption of digital payment systems have made individuals and organizations more vulnerable to cyber threats. Key areas affected include banking, healthcare, and government sectors, which are frequently targeted due to the sensitive nature of the data they handle. To combat these risks, there is an increasing focus on public awareness, cybersecurity education, and robust regulatory frameworks. This paper provides an in-depth analysis of cybercrime, with a focus on developing innovative prevention strategies, strengthening internal security protocols, and classifying key cybercrime terminologies to better understand their implications for digital infrastructure.

Leveraging data mining and cybersecurity techniques to enhance algorithmic trading performance and forensic investigations in financial markets

In the evolving landscape of financial markets, the convergence of data mining, cybersecurity, and algorithmic trading plays a pivotal role in enhancing trading performance and forensic investigations. This study investigates how data mining techniques are leveraged to extract meaningful patterns and trends from vast financial datasets, improving the accuracy and profitability of algorithmic trading strategies. By identifying historical trends, price movements, and trade margins, data mining enables traders to optimize decision-making processes and manage risks more effectively. Cybersecurity emerges as a critical factor in safeguarding both trading algorithms and sensitive financial data from cyber threats. The integration of advanced cybersecurity measures ensures the integrity, confidentiality, and availability of trading systems, reducing vulnerabilities that could be exploited by malicious actors. Additionally, forensic investigation techniques are employed to detect fraudulent trading activities, such as insider trading and market manipulation, thereby protecting market participants and maintaining regulatory compliance. This research highlights the importance of combining secure data mining practices with robust cybersecurity measures to enhance the overall performance of algorithmic trading systems. Furthermore, it explores how forensic methodologies can help detect anomalies and ensure the transparency and fairness of financial markets. Through an integrated approach, this study emphasizes the potential of data mining and cybersecurity in transforming trading operations while mitigating risks associated with cyber threats and financial fraud.

The Importance of Credit Card Fraud Detection in the Digital Age

It is true that credit card transactions pave part of daily life in today’s digital economy. Also, it is undeniable that this assurance comes at a cost: it has inadvertently opened the floodgate on one of the most daunting challenges to financial safety-credit card fraud. Credit card fraud causes billions of dollars in losses annually, which is brought about by unauthorized transactions carried out using information related to stolen or cloned credit cards. As such, fraud is very difficult to trace and prevent because advanced technologies and strategies are involved.