Fraud Prevention Research Articles

An integrated GRC approach to combating fraud in microloan services

This research aims to reduce fraud risks in Indonesian banks and non-bank financial institutions providing microloan services. The study employs data analytics and machine learning techniques using employee data from Bank X spanning 2017-2019 with samples of 28,004 workers (2017-2018) and 27,274 employees (2019). Confirmatory factor analysis and XGBoost predictive modelling are applied within the fraud triangle framework to identify critical fraud risk indicators related to employee pressure. An algorithmic approach categorizes personnel based on fraud risk ratings enabling the detection of potentially suspicious activities for proactive intervention. The analysis reveals that incorporating data analytics into governance, risk management and compliance (GRC) systems can accurately forecast fraud probability by focusing on factors associated with employee pressure and opportunities. This facilitates targeted fraud prevention solutions by integrating control mechanisms, risk processes and auditing standards. The predictive model provides valuable insights for policymakers to combat fraud by enhancing governance and risk management practices specific to microloans. This research concludes that the predictive model is a pragmatic decision-making tool for banks offering micro-loans. It mitigates dangers by detecting high-risk personnel and transactions. Integrating data analytics with robust GRC frameworks enables financial institutions to uphold integrity through proactive fraud monitoring and targeted preventive interventions tailored to identify risk profiles. The study offers an integrated technological organizational approach to protect microlending activities.

Assessing the Impact of Effective Internal Controls in Fraud Prevention in Zambian Beverage Companies, a Case of Falls Beverages Zambia Limited

Internal controls encompass policies, procedures, and organizational structures designed to provide reasonable assurance that a company achieves its business objectives and prevents or detects undesired risk events, whether initiated by management concerns or compliance requirements. The study aimed to evaluate how effective internal controls impact fraud prevention, using a survey to collect data from 109 employees, including an internal auditor. Findings revealed that existing internal controls significantly aided in preventing major fraudulent activities and ensuring measures were in place to detect and deter fraud. Over 50% of respondents confirmed effective monitoring and implementation of internal controls capable of uncovering fraudulent behaviors within the company. The study emphasized that fraudulent activities adversely affected company operations and recommended measures such as fostering better management-staff relations, strengthening the audit department, offering incentives for fraud reporting, and implementing stringent controls. In conclusion, improving internal controls was identified as crucial for preventing fraud and safeguarding Falls Beverages from financial losses and resource mismanagement. Keywords: Internal Controls, Fraud Prevention

The role of artificial intelligence in preventing corporate crime

This article explores the novel and transformative role AI plays in enhancing corporate compliance and monitoring to mitigate corporate crime. It delves into how AI technologies, through advanced data analysis and predictive analytics, are revolutionizing the detection and prevention of financial fraud, insider trading, and non-compliance with environmental regulations. The integration of AI into the financial sector is emphasized as pivotal for developing robust fraud prevention algorithms, which analyze vast data volumes to identify malicious activities and prevent financial losses. Moreover, the article probes into the ethical and privacy concerns surrounding the deployment of AI, highlighting the balance needed between efficient crime prevention and safeguarding individual privacy rights. Garcia-Segura's analysis also underscores the need for international cooperation and legal adaptation to uphold ethical standards in AI’s application, ensuring it promotes fairness and justice in combating corporate crime.

A Survey on Diamond Security System Using Machine Learning and Blockchain

The pervasive challenges of counterfeit in the diamond industry, cybersecurity vulnerabilities, and the looming threat of large-scale heists, akin to the infamous Antwerp money heist of 2003, underscore the critical need for innovative security solutions. This initiative addresses these pressing concerns through the fusion of machine learning algorithms and blockchain technology. Our methodology entails a comprehensive dataset comprising 32 parameters for advanced detection and 5 dominant parameters for basic detection, catering to both expert and novice users. Leveraging machine learning techniques including random forest and ensemble learning, our system meticulously analyzes these parameters to discern between natural and artificial diamonds with heightened accuracy. The accuracy of our models varies, with the highest achieving an impressive 98%, ensuring robust authentication and fraud prevention mechanisms. Furthermore, the verified data of natural diamonds is securely stored on a blockchain ledger, fortifying ownership records against tampering and ensuring transparency. This integrated approach not only safeguards the integrity of the diamond industry but also fosters trust and transparency among stakeholders. By providing a paradigm shift in security solutions, our system sets a new standard for authentication and fraud prevention in the diamond market, mitigating risks and empowering stakeholders with reliable, data-driven tools for decision-making.

The Role of Artificial Intelligence in Banking and Fraud Prevention: A Cross Sectional Study in Ghana

The Role of Artificial Intelligence in Banking and Fraud Prevention: A Cross Sectional Study in Ghana

Impact of Whistleblowing System Implementation Level on Company's Operational Efficiency in Indonesia Consumer Goods Sector

Indonesia faces significant issues with fraud across various business sectors, including consumer goods, which negatively impacts operational efficiency. This highlights the need for effective fraud prevention methods to mitigate these effects. This study uses panel data regression analysis, based on annual reports and financial statements from 2019 to 2023, to examine how the implementation level of whistleblowing systems affects operational efficiency, with fraud prevention as a mediating variable, in consumer goods companies listed on the Indonesia Stock Exchange (IDX). The findings indicate that a higher level of whistleblowing system implementation significantly enhances operational efficiency. However, fraud prevention does not mediate this relationship, as the analysis shows insignificant results regarding the impact of whistleblowing systems on fraud prevention and the effect of fraud prevention on operational efficiency. This suggests that while whistleblowing systems directly improve operational efficiency, their role in fraud prevention does not significantly influence this improvement. This research contributes to the literature by demonstrating the direct positive impact of whistleblowing systems on operational efficiency in the Indonesian consumer goods sector. It also offers practical recommendations for companies to enhance their whistleblowing systems to boost operational efficiency. Additionally, the study calls for future research to explore alternative mechanisms through which whistleblowing systems impact operational efficiency beyond fraud prevention and to investigate other methods for assessing whistleblowing system implementation and fraud incidence to reduce bias.

Fraud Prevention: Effectiveness of Sharia Governance in Sharia Banks

Fraud Prevention: Effectiveness of Sharia Governance in Sharia Banks

Applications of knowledge graph in medical and financial fields: Data integration and intelligent decision-making from an interdisciplinary perspective

With the rapid advancement of AI and deep learning technologies, knowledge graphs have emerged as a key technology for improving the performance of intelligent decision-making systems and driving interdisciplinary innovation. This article outlines the core principles and structure of knowledge graphs, including how they construct knowledge networks to support complex queries and intelligent reasoning. It reviews their innovative applications in the healthcare and financial industries, emphasizing their significant roles in data integration, decision support, and risk assessment. In the healthcare domain, knowledge graphs contribute to improving the accuracy of medical diagnoses, accelerating drug discovery, and enabling intelligent semantic searches. In the financial sector, they optimize risk management and aid in fraud prevention. The article also looks ahead to the future potential of knowledge graphs, stressing the importance of interdisciplinary collaboration and technological innovation in their development. It aims to provide valuable references for further research and application of knowledge graphs.

Identification and prevention of financial securities fraud based on deep learning

Identification and prevention of financial securities fraud based on deep learning

A typology framework for unethical medical practices under public health insurance schemes in India: Analysis of evidence over the past 12 years.

Unethical provider practices in public healthcare schemes adversely impact beneficiaries' health, result in the loss of public funds, and also bring disrepute to the schemes. There is extensive literature on the typologies of unethical practices in healthcare in developed countries. This study aims to develop a typology framework which is applicable in the Indian context. In this study, 25 media reports and research studies were analysed on unethical provider practices under public health insurance schemes in India over the past 12 years from 2010 to 2022. The reports were collated from de-empanelment orders issued by state health authorities against various erring entities, and research studies conducted on the abuse of these schemes. Based on the analysis and classification of the cases reported, an "Unethical Provider Practices" typology for healthcare fraud has been defined. Additional fraud typologies are found to be prevalent in India in addition to those captured by existing frameworks. These include patient harm, ID theft of beneficiary data to create cards for non-beneficiaries, and collusion between providers and different entities. Fraud control mechanisms leveraging technology such as AI-enabled digital apps for medical audits, biometric technology at the point of care and rigourous checks of ID documents before beneficiary cards are issued as well as having more specific legal provisions in place for healthcare fraud will enable enhanced prevention, detection and deterrence of healthcare fraud.

Fraud Prevention Strategy at PT. PLN (Persero) Lhokseumawe Customer Service Unit

This research describes the implementation of fraud prevention strategies through internal control at PT. PLN (Persero) Lhokseumawe Customer Service Implementation Unit, with a focus on anti-bribery management system programs. The methods used include Gap Analysis to measure readiness to implement an anti-bribery management system as well as SWOT analysis combined with Strategic Factor Analysis to formulate implementation strategies in the short, medium and long term. The research results show that although PT. PLN (Persero) Lhokseumawe Customer Service Implementation Unit is ready to implement an anti-bribery management system, there is still a need for further development. Some of the main findings are that not all employees have received training regarding the anti-bribery management system and there has been no risk assessment of bribery. To overcome this, the research recommends several implementation strategies, namely Anti-Bribery Management System Training, Anti-Gratification Socialization, Bribery Risk Assessment, Activity Budgeting and Policy/Guideline Making. This strategy is expected to help PT. PLN (Persero) Lhokseumawe Customer Service Implementation Unit in strengthening good governance and reducing the risk of fraud through implementing an effective and sustainable anti-bribery management system

Do Directors’ Network Positions Affect Corporate Fraud?

Corporate fraud poses a significant obstacle for sustainable business development. Drawing on social network analysis, this paper used data originated from Chinese-listed companies from 2009 to 2022 and found that directors’ network position significantly mitigates corporate fraud. Mechanism tests indicated that the quality of external auditors and internal control play a mediating role in this relationship. Further analysis showed that the network positions of independent directors, non-independent directors, and female directors individually inhibit the inclination of corporate fraud when considering various types of directors. Of note, the busy director hypothesis was not applicable in explaining the impact of directors’ network position on corporate fraud. This study provides a new approach to improving the sustainability of enterprises in newly emerging markets via the analysis of director networks. It is also beneficial to the research on director networks and corporate fraud in companies, offering insights for corporate governance and fraud prevention in companies and regulatory agencies.

Forensic Accounting: Modern Tool for Fraud Prevention

Forensic accounting plays a very crucial role in accounting sector. Financial fraud in India increasing day by day which leads to increase in forensic accounting demand. There are so many scandals in financial sector which is not easy to investigate by traditional method. In this research paper researcher discuss about the needs and importance of forensic accounting. The concept of this paper includes- introduction, significance of study, review of literature, conceptual framework, objectives and methodology of study, procedure and problems of forensic accounting in India, suggestions and conclusion.

Village fund corruption mode: an anti-corruption perspective in Indonesia

Purpose This paper aims to highlight the corruption mode of village funds in Indonesia and provide recommendations to reduce such crime. Design/methodology/approach This paper uses Diamond Fraud theory to explain why the mode of corruption continues, using secondary data from journal articles, research reports and websites. Findings Corruption mode is carried out through fund misuse, cover-up, fictional reports, fictitious activities and projects and budget markup. Practical implications Prevention and detection of fraud can be more effective when considering pressure, opportunities, rationalization and individual abilities. Originality/value The novelty of this paper is to provide a more comprehensive view of the factors that can lead to fraud or corruption using the Fraud Diamond Theory.

The Role of International Experience in Executive Decision-Making and Fraud Prevention

The Role of International Experience in Executive Decision-Making and Fraud Prevention

Spade: A Real-Time Fraud Detection Framework

In this demonstration, we introduce Spade, a sophisticated real-time fraud detection framework adept at navigating the complex transaction graph. Unlike conventional methods that are limited by performance and lack incremental update capabilities, Spade leverages advanced incremental updates in dense subgraph peeling algorithms to enhance efficiency, usability, and reduce latency, achieving a significantly better fraud prevention ratio. The demo showcases an interactive GUI prototype, allowing users to customize and explore dense subgraphs with various metrics and algorithms. This interactive demonstration also effectively highlights Spade's robust capacity to unearth fraudulent transactions within varied settings, including Grab's services and cryptocurrency transactions.

Analysis The Influence of Internal Audit and Internal Control on Fraud Prevention: Literature Review Articles

The objective of this study is to examine the impact of internal audit and internal control on the prevention of fraud. The used approach involves doing a literature review by examining six (6) primary studies that have investigated the impact of internal audit and inter-nal control on fraud prevention. The research findings indicate that internal audit factors have an impact on fraud prevention, while internal control variables also have an affect on fraud prevention.

Assessing LLMs in malicious code deobfuscation of real-world malware campaigns

The integration of large language models (LLMs) into various cybersecurity pipelines has become increasingly prevalent, enabling the automation of numerous manual tasks and often surpassing human performance. Recognising this potential, cybersecurity researchers and practitioners are actively investigating the application of LLMs to process vast volumes of heterogeneous data for anomaly detection, potential bypass identification, attack mitigation, and fraud prevention. Moreover, LLMs’ advanced capabilities in generating functional code, interpreting code context, and code summarisation present significant opportunities for reverse engineering and malware deobfuscation.In this work, we comprehensively examine the deobfuscation capabilities of state-of-the-art LLMs. Specifically, we conducted a detailed evaluation of four prominent LLMs using real-world malicious scripts from the notorious Emotet malware campaign. Our findings reveal that while current LLMs are not yet perfectly accurate, they demonstrate substantial potential in efficiently deobfuscating payloads. This study highlights the importance of fine-tuning LLMs for specialised tasks, suggesting that such optimisation could pave the way for future AI-powered threat intelligence pipelines to combat obfuscated malware. Our contributions include a thorough analysis of LLM performance in malware deobfuscation, identifying strengths and limitations, and discussing the potential for integrating LLMs into cybersecurity frameworks for enhanced threat detection and mitigation. Our experiments illustrate that LLMs can automatically and accurately extract the necessary indicators of compromise from a real-world campaign with an accuracy of 69.56% and 88.78% for the URLs and the corresponding domains of the droppers, respectively.

Does the Internal Control System Play a Strong Safeguarding Role Against Fraud in Local Communities?

This paper investigates the application of appropriate mechanisms and procedures for identifying, reducing, and suppressing fraud. Challenges and obstacles faced by municipalities in the implementation of internal controls for the prevention of fraud are also discussed. A survey was conducted in various Belgrade municipalities, and 162 usable responses were received. It was observed that there is a lack of internal control or that, where it exists, it is not sufficient to effectively manage fraud. Further, the results of the study indicate that only one element of fraud risk management, the level of education of employees, is significant in reducing fraud incidents. Namely, the education and training of employees contribute the most to fraud detection. The implications of these findings are discussed. Through the analysis of the relevant literature and research, this paper offers recommendations for improving internal control in local municipalities in order to reduce the risk of fraud and to increase responsibility in the management of public resources.

Enhancing fraud prevention and internal control: the key role of internal audit in public sector governance

Enhancing fraud prevention and internal control: the key role of internal audit in public sector governance