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This study examines the application of Transparent Artificial Intelligence (AI) for fraud detection in public welfare programs using publicly available administrative data. Persistent challenges in welfare governance such as misallocation, fraud, and data inaccuracy necessitate analytical frameworks that are both effective and explainable. The research aims to design and evaluate an interpretable anomaly detection system capable of identifying irregularities in welfare distribution while maintaining transparency and accountability. Methodologically, the study employs two unsupervised models Isolation Forest and Local Outlier Factor (LOF) to detect anomalies in sub-district-level welfare data, incorporating features such as population size, number of beneficiaries, and coverage ratio. An Explainable AI (XAI) framework integrating surrogate Random Forests, Permutation Feature Importance (PFI), and local linear surrogates (LIME-like) is applied to ensure interpretability of both global and local model behaviors. Findings reveal that receivers per 1000 population and percentage coverage are dominant determinants of anomaly scores. Fifteen administrative units were flagged for potential inconsistencies suggesting over- or under-reporting of beneficiaries. Cross-validation between IF and LOF models confirmed consistency in identifying anomalous regions. The integrated XAI explanations enhance transparency, enabling policymakers and auditors to trace the rationale behind detected anomalies. In conclusion, the proposed Transparent AI framework demonstrates that combining anomaly detection with interpretability tools can strengthen accountability and fairness in welfare administration. It offers a reproducible, ethical, and data-driven approach to social program monitoring, reinforcing public trust and supporting responsible AI governance.

Nowadays, people are more likely to make transactions online. Online transactions are the same as offline transactions, only the online buying and selling process is carried out via the internet rather than offline, where sellers and buyers meet.  In fact, when carrying out online transactions, some customers become disappointed and disadvantaged, such as when the product ordered is not suitable or is not sent by the seller, especially for customers who experience fraud with large amounts of money.  As a result, it is very important to follow Islamic business ethics when making transactions on Tokopedia.  This research uses qualitative descriptive research where researchers use the literature study method to obtain data.  The research results show that Islamic business ethics at Tokopedia fulfill several Islamic ethical principles

Local governments manage substantial public resources under conditions of decentralization, fiscal complexity, and heightened accountability demands, making them particularly vulnerable to financial mismanagement and fraud. In this context, risk-based internal oversight has increasingly been promoted as a governance-oriented alternative to traditional compliance-based supervision. This literature review article examines how risk-based internal oversight is conceptualized, operationalized, and linked to fraud prevention and control in the management of local government finance. The study addresses a central problem in the existing literature: the fragmentation of analytical perspectives across risk-based internal auditing, fraud risk management, internal control systems, public financial management, and public accountability, which has limited a comprehensive understanding of how internal oversight contributes to safeguarding public funds. The primary objective of this article is to synthesize and integrate these strands of literature to clarify the role of risk-based internal oversight as a systemic governance mechanism for fraud prevention and control at the subnational level. Methodologically, the study employs an integrative literature review approach, drawing on peer-reviewed journal articles and authoritative institutional publications indexed in major academic databases over the past decade. A structured search, screening, and thematic synthesis process was applied to identify patterns, convergences, and divergences across conceptual, empirical, and policy-oriented studies. The findings indicate a clear shift from compliance-oriented inspection toward risk-based internal oversight that prioritizes high-risk financial processes—particularly procurement, grants, and asset management—where fraud risks are most pronounced. The synthesis further shows that effective fraud prevention depends on the alignment of risk-based oversight with fraud risk management practices, robust internal control systems (including SPIP).

This study analyzes the influence of Islamic business ethics principles on online buying and selling transactions in the predominantly Muslim community of South Sumatra. Using a quantitative approach through a survey of 200 respondents in Palembang with a Likert scale questionnaire, the data were analyzed using multiple linear regression with SPSS 26. The results showed that the principle of honesty (sidq) had the strongest significant influence (β = 0.029, p = 0.002), followed by unity (tawhid) (β = 0.016, p = 0.049), while other principles such as balance, free will, and responsibility showed a positive but insignificant influence. The regression model explained 10.3% of the variation in transaction behavior (R² = 0.103). This study concluded that the application of Islamic ethics can increase trust and sustainability in e-commerce in the region, with implications for the development of halal businesses. In the future, the application of these principles is expected to mitigate ethical issues in e-commerce, such as fraud and uncertainty that often occur, as well as strengthen transparency and fairness in online transactions.

The phenomenonoccurring in various universities shows an increase in cases of academic fraud, such as cheating, plagiarism, research data manipulation, and misuse of technology in the learning process. This behavior not only undermines academic integrity, but also has the potential tocreate a mindset of cheating that carries over into the workplace. Integrityis a fundamental value that forms the moral foundation of every aspect oflife, both in education and in the workplace. In the context of higher education, students are expected not only to excel academically,but also to possess honesty, responsibility, and strong professional ethics.Accountants are future professionals who will be directly involved inthe management and financial reporting of organizations. This profession demandsa very high level of honesty and integrity, because the accuracy andreliability of financial reports form the basis for business and public decision-making. However, in practice, there are many cases of accounting fraud, such as budget mark-ups, financial statement manipulation, creative accounting, and data misstatement, which occur due to weak values of integrity and moral responsibilityamong individuals. If dishonest behavior has already been established during college—for example, in the form of cheating, manipulating assignment data, or falsifying lab reports—then the risk of bringing similar habits into the workplace will be even higher. Integrity is a core competency (core comp) that determines a person's quality as an organizational asset

A competitive company is basically a company that is able to maintain consistency and stability of profits in various business activities, without having to commit acts of fraud that can harm internal and external parties. Achieving high-quality profits is an important indicator of the company's sustainability because it reflects management's ability to effectively manage assets, resources, and business strategies. In the context of this study, the main focus is directed to the effect of leverage, liquidity, and profitability on the quality of profit with the size of the company as a variable of moderation. The study was conducted on food and beverage subsector manufacturing companies listed on the Indonesia Stock Exchange (IDX) during the period 2019 to 2023. The research method used is quantitative with analytical descriptive approach. The selection of samples was carried out by purposive sampling technique so that 25 companies were obtained as samples with a total of 125 financial statement data for five years of observation. Based on the results of the analysis, it was found that leverage, liquidity and profitability have a negative and significant influence on the quality of profit. This finding shows that the higher the three variables, the quality of profit actually decreases. Furthermore, the results revealed that the size of the company is not able to moderate the relationship between leverage, liquidity, and profitability to the quality of profit.

Cases of non-performance of contract. on the part of the aggrieved party bring the case to the criminal realm. The charge reported is the fraud article, even though the criminal offense has not been fulfilled. So that it has the potential to cause wrong criminalization, the main problem is the inaccurate use of the fraud article against defaults which are basically civil. The research method uses normative juridical with decision analysis and criminal and civil law studies related to non-performance of contract. and fraud. The results show that non-performance of contract. can only turn into a criminal offense of fraud if there are elements of intent, bad faith, use of deception, or falsification of agreements with the intention of benefiting oneself against the law. Therefore, law enforcement officials should be careful in distinguishing non-performance of contract. and fraud cases to avoid abuse of the law that harms the parties concerned. The hopes and solutions proposed are proportional law enforcement by paying attention to the element of malicious intent in determining criminal offenses, as well as legal education to the public so that they understand the difference between the civil and criminal domains in contractual disputes.

The development of digital technology has changed global business patterns, including in Indonesia, through the growth of e-commerce, social media, and online platforms. This change opens up great opportunities for students as the educated young generation to develop online businesses. This research aims to explore students' online business readiness from a digital mindset perspective through a phenomenological study approach among students in Malang. The research focuses on online business readiness, digital mindset, attitudes towards technology, and the contribution of entrepreneurship learning, which are analyzed using the Theory of Planned Behavior framework(Ajzen, 1991) and Entrepreneurial Mindset Theory. The research used qualitative methods with in-depth interviews with online business students. The results show that students have a basic understanding of digital business, especially in utilizing social media and marketplaces. A positive attitude towards technology has been formed, indicated by perceived benefits and ease of use. However, a gap emerges between technical skills and long-term strategic planning capabilities. Innovation and problem-solving tend to be reactive, simply following trends, while psychological resistance such as fear of fraud and data security remain barriers. This research confirms that online business readiness is not only determined by technical skills, but also by the strengthening of an adaptive, innovative digital mindset. This research confirms that online business readiness is not only determined by technical skills, but also by strengthening an adaptive, innovative, and resilient digital mindset.The novelty of this research is the integration of TPB and Entrepreneurial Mindset Theory to understand students' readiness to face digital business challenges.

The development of artificial intelligence technology has given birth to deepfake technology which has the potential to be misused to damage a person's digital identity. This research aims to analyze the mode of misuse of deepfake technology in Indonesia, examine its legal implications based on applicable laws and regulations, and examine the urgency of special regulation of deepfake in the Indonesian legal system. Applicable, as well as examining the urgency of special arrangements against deepfake in the Indonesian legal system. The research method used is normative juridical legal research with statutory and conceptual approaches, and data collection is carried out through literature study of primary, secondary, and tertiary legal materials tertiary. The results show that deepfake abuse can take the form of spreading fake videos, face forgery and digital identity-based fraud that harms victims socially, legally and psychologically. Although several articles in the ITE Law, Personal Data Protection Law, and Criminal Code can be used to charge perpetrators, these regulations have not specifically and explicitly regulated the characteristics and technical aspects of deepfake-based crimes. This creates obstacles in law enforcement and victim protection. Therefore, it is necessary to establish special regulations or amend existing laws so that the legal system in Indonesia is able to answer the challenges of deepfake technology appropriately, fairly, and effectively.

The rapid growth of social media has yielded vast digital traces with high potential for improving corporate forensic auditing. Their utilization, however, lags behind through technological reliability, privacy, and adherence to the law. The aim of this study is to explore effective utilization of social media digital traces in forensic auditing and develop a functional framework that lags neither behind through technological efficiency nor adherence to the law and ethics. A mixed-method design was utilized, combining quantitative machine learning analysis with qualitative document analysis and semi-structured interview insight. Quantitative data drawn from social media digital traces were processed using Random Forest algorithm with SMOTE for class balancing, while qualitative data were processed using thematic analysis. The results indicated high model performance with 91.3% accuracy and AUC-ROC of 0.94, together with three emergent themes: digital integration, ethics and privacy, and regulation and legality. The results demonstrate that digital footprints may serve as an effective early and reliable indicator for fraud detection, provided they are accompanied by clear regulatory and ethical frameworks. Its principal contribution lies in the development of an operational model that combines machine learning with legal and ethical perspectives, a new strategy which matures methodological refinement and practical application in today's forensic auditing.

The development of information technology has encouraged institutions, including hospitals, to adopt digital systems to improve operational efficiency. One important aspect is the employee attendance system, which previously relied on fingerprints. This method has limitations, such as difficulty detecting when fingers are not in ideal condition and causing queues during peak hours. This research aims to design and implement an Android-based attendance system using the Eigenface facial recognition method as a faster, safer, and more accurate alternative. Eigenface works by extracting facial features using Principal Component Analysis (PCA), thus being able to efficiently recognize individual identities. The system was developed with MySQL database integration and tested on employees of Khalishah General Hospital. The implementation results showed that the system can recognize faces with a good level of accuracy and increase the effectiveness of attendance recording. Furthermore, the use of facial-based attendance can minimize the potential for fraud and increase user comfort because it does not require physical contact. Thus, the Eigenface method has proven feasible to be implemented as a modern attendance solution to support employee attendance management in hospital work environments and other institutions.

This article employs a Critical Literature Review (CLR) approach, analyzing studies from 2013–2025 that include academic papers, nationally accredited journals (Sinta 2–4), and reputable international journals indexed by Scopus (Q1–Q4) to understand systemic fraud and audit failure in Indonesia.Previous re-search has mostly explained fraud from individual or organizational views, while few have addressed structural factors such as the normalization of fraud, weak regulatory oversight, and the crisis of auditor legitimacy. The synthesis reveals four main issues. First, systemic fraud in Indonesia has shifted from in-dividual misconduct to a structured socio-economic phenomenon rooted in culture. Second, auditing as a control mechanism remains technically focused, often failing to uncover collusive fraud involving top management. Third, an expectation gap exists between unqualified audit opinions and public demands for assurance against corruption, reducing trust in auditors. Fourth, conflicts of interest arising from audit fee dependence, weak regulation, and limited adoption of audit technology and forensic analytics indicate the need for institutional reform. Therefore, this article calls for an integrative framework that connects individual, organizational, regulatory, and socio-cultural dimensions to address systemic fraud and rein-force public accountability in Indonesia.

Attendance is an essential activity in both educational institutions and companies, serving as an indicator of discipline, presence, and individual responsibility. Conventional attendance systems that still rely on manual journals often face several problems, such as vulnerability to manipulation, data loss, and physical damage. Meanwhile, modern methods such as fingerprint, QR code, RFID, and GPS are not entirely ideal since each has its own limitations in terms of cost, accuracy, user convenience, and potential misuse. For instance, fingerprint systems raise hygiene concerns due to shared use, while QR code and GPS methods are prone to fraud and location spoofing. To address these challenges, this study proposes a face-based attendance simulation system by integrating the YOLOv8 algorithm for face detection and Local Binary Pattern Histogram (LBPH) for face recognition. YOLOv8 was chosen for its ability to detect faces in real time with high speed and accuracy, while LBPH is employed for face recognition due to its robustness in handling variations in facial features and its relatively low computational requirements. This makes the system efficient even when implemented on medium-specification devices. The system was tested on 25 participants with a total of 250 attendance attempts. Based on the confusion matrix analysis, the system achieved outstanding performance with 98.4% accuracy, 98.4% precision, 100% recall, and a 99.2% F1-score. Furthermore, the system automatically recorded attendance dates and times with an average latency of 69.185 ms, proving its capability to operate quickly and reliably in real-world scenarios. Nevertheless, several limitations were observed, such as decreased accuracy when the face moved too quickly during image capture, as well as potential performance degradation under extreme lighting conditions. Despite these challenges, the proposed system demonstrates excellent performance and offers a promising solution for efficient, hygienic, and fraud-resistant attendance management applicable to both educational and professional environments.

This study aimed to develop a simple acid-base reaction-based screening method for distinguishing between GMO (Genetically Modified Organism) and non-GMO soybeans as a practical alternative to expensive and expensive molecular methods. Twenty GMO soybean samples, 20 non-GMO samples, and three mixtures with ratios of 10%, 25%, and 50% were analyzed using 0.01–0.1 N HCl and 0.1 N NaOH solutions. Observation parameters included color change, pH, and absorbance using a UV-Vis spectrophotometer. The results showed that at a concentration of 0.01 N HCl, the color difference was most pronounced. The GMO sample solution showed a red color, while the non-GMO sample solution showed a green color. At higher concentrations, the differences became more subtle or difficult to distinguish. Validation using a PCR assay as the gold standard yielded sensitivity, specificity, and accuracy of >95%, indicating the reliability of this method as an initial screening technique. This physicochemical approach is considered effective for rapid, inexpensive, and easily implemented screening in food industry laboratories, particularly for monitoring non-GMO soybean raw materials and preventing food fraud. Therefore, this acid-base method has the potential to be a practical alternative solution for industry and education in detecting indications of GMOs before further confirmation with molecular methods.

This paper explores the transformative influence of artificial intelligence (AI) on the accounting profession, particularly within the Accountant General of the Federation (OAGF). The research investigates how AI-driven innovations are reshaping traditional accounting practices and redefining the role of accountants. By conducting a systematic literature review, this study identifies three primary dimensions of AI’s impact: the automation of repetitive tasks such as data entry, transaction processing, and reconciliation; enhanced data analytics capabilities, which include predictive modeling and real-time decision support; and the evolution of accountants' roles toward more strategic and value-added activities, such as financial advisory and risk management. The automation of routine processes through AI allows accountants to focus on higher-level tasks that require judgment, creativity, and expertise, ultimately enhancing the overall efficiency of the accounting function. Furthermore, AI’s advanced data analytics tools provide more accurate insights, enabling accountants to offer more effective financial guidance and make more informed decisions. As AI reduces the time spent on manual processes, accounting professionals can improve their role in advising on business strategy, improving risk management, and identifying new growth opportunities. The study’s findings underscore the importance of embracing AI in the accounting profession, not only to improve operational efficiency, reduce costs, and scale operations but also to enable accountants to stay competitive in a rapidly evolving technological landscape. The paper concludes by emphasizing that adopting AI is essential for accountants to remain relevant and continue providing valuable contributions to their organizations. Future research should focus on the long-term implications of AI on accounting ethics and the development of necessary skills for accounting professionals to thrive in the age of AI.

The development of information technology has brought about significant changes in the social, economic, and legal life of global society. On the one hand, digitalization creates efficiency and convenience in various activities, such as financial transactions, communications, and access to information. However, on the other hand, this progress has also given rise to a new form of crime known as cybercrime. Cybercrime differs from conventional crime in that it is committed through electronic systems that can cross national borders and involve a large number of victims. This crime encompasses various forms, such as data hacking, online fraud, and malware distribution. This research aims to examine the development of cybercrime as a form of modern crime in the digital era and to assess the response of Indonesian criminal law to these challenges. The method used is normative legal, with a statutory, conceptual, and case study approach. This research also examines existing regulations, such as Law Number 11 of 2008 concerning Electronic Information and Transactions (ITE) and Law Number 27 of 2022 concerning Personal Data Protection, in addressing cybercrime issues. The research results show that although Indonesia has several regulations related to cybercrime, their implementation still faces various obstacles. Some of the main obstacles include suboptimal law enforcement, limited technology and human resources, and low levels of public digital awareness. Therefore, regulatory reform, institutional capacity development, and increasing public digital literacy are essential. This is part of a national strategy to address cybercrime comprehensively and sustainably, in order to create a safer and more trustworthy digital environment.

This research uses the Fraud Control Plan (FCP) as a moderating variable to explore how human resource competency, internal control systems, and internal supervision affect regional government financial reporting. Due to financial reporting transparency and accountability issues, public sector corruption remains rampant, prompting the study. A quantitative approach was used using moderated regression analysis. Lampung Province's Regional Financial and Asset Management Agency (BPKAD) accounting and reporting workers received questionnaires to gather data. The results show that human resource competency, internal control mechanisms, and internal supervision improve financial reporting. The Fraud Control Plan strongly moderates the correlations between human resource competency, internal supervision, and financial reporting quality, but not the internal control system. These findings imply that improving human resource competences, internal supervision, and fraud control may significantly enhance regional government financial reporting openness and accountability.

Facial recognition, a branch of image processing, is widely used in attendance systems to improve efficiency and security. This study develops an employee attendance monitoring system that integrates facial recognition using the Eigenface algorithm in OpenCV. The system records each individual's facial data alongside a password, enabling automated attendance tracking. Testing results indicate that with a database of 10 facial entries, the system achieved 100% accuracy in recognizing individuals. However, as the database expanded beyond 10 entries, accuracy declined to 80%, influenced by factors such as lighting variations, differences in facial structures, and increased data volume. This study employed a Research and Development (R&D) methodology, with expert validation yielding a score of 3.4, categorizing the system as "Highly Valid." User testing with 11 participants resulted in an overall score of 36, classifying the system as "Very Good (Valid)." The findings highlight the potential of facial recognition in improving attendance management while minimizing fraudulent entries. Future research should focus on optimizing recognition accuracy in larger databases through refined preprocessing techniques, image quality adjustments, and deep learning models.

Organizational fraud represents a significant global challenge, with estimated annual losses of 5% of revenue, totaling approximately US$4.7 trillion worldwide. This systematic literature review examines whistleblowing effectiveness as a fraud detection mechanism, analyzing the tension between its role as a transparency pillar and the retaliatory risks faced by whistleblowers. The study employed a qualitative Systematic Literature Review methodology, analyzing 25 peer-reviewed articles published between 2020-2025 from SINTA, Scopus, and registered E-ISSN journals. Literature was selected using keywords related to whistleblowing effectiveness, fraud detection, transparency mechanisms, and retaliation risk. Results demonstrate that whistleblowing systems significantly enhance fraud detection and prevention across both public and private sectors, with 24 of 25 reviewed studies confirming positive impacts. Effectiveness is influenced by individual attitudes, perceived behavioral control, organizational commitment, professional commitment, moral reasoning, and organizational culture. The integration of whistleblowing with forensic and investigative audit mechanisms creates a mutually reinforcing fraud detection framework. However, retaliation risk remains a substantial challenge, potentially undermining whistleblowing effectiveness. The study concludes that organizations must develop comprehensive whistleblower protection frameworks, cultivate supportive organizational cultures, ensure transparent report handling processes, and leverage technology to guarantee anonymity. A balanced approach between promoting transparency through whistleblowing and protecting whistleblowers from retaliation is essential for maximizing fraud detection effectiveness. Organizations are advised to integrate whistleblowing into comprehensive anti-fraud strategies with adequate protection mechanisms and whistleblower incentives.

The use of Big Data Analytics (BDA) in the financial audit process has altered the traditional audit method by improving audit quality and efficiency. The purpose of this study is to critically assess BDA's role in reducing audit delays and increasing audit quality. Using a literature review approach, this study intends to consolidate findings from national and international studies published between 2017 and 2024 that focus on the role of BDA in audit procedural transformation, fraud detection, and real-time decision support. The findings reveal that BDA not only allows auditors to review massive amounts of data in real time, but it also improves evidence collection and risk assessment. Audit delay has emerged as a significant variable in the relationship between BDA and audit quality, however its effectiveness varies depending on technical infrastructure and auditor expertise. Furthermore, this study identified implementation problems for BDA, such as data security threats, technology literacy gaps among auditors, and organizational readiness, all of which must be addressed in order for BDA to reach its full potential. This study adds to the theoretical and practical discussions about the strategic use of BDA to alter audit methods in the digital era.