Potential Fraud Research Articles

The Influence of Performance Expectancy, Effort Expectancy, And Social Influence on Use Behavior with Behavioral Intention as A Mediator (A Case Study of Cash on Delivery (COD) System Users in Marketplaces)

Online shopping has gained significant popularity due to the numerous conveniences it offers. Several marketplaces provide various premium services, including the Cash on Delivery (COD) payment system, which has become a prominent feature in Indonesia. COD caters to individuals who prefer traditional payment methods and are skeptical about online transactions. This payment method allows customers to mitigate concerns about potential fraud, such as non-delivery of goods or receiving empty packages, thereby offering a sense of security and trust in their purchases. This study aims to investigate the relationships between the variables of Performance Expectancy, Effort Expectancy, Social Influence, Behavioral Intention, and Use Behavior. The sample comprises individuals who have shopped on a marketplace using the COD payment system, with a total of 348 respondents. This research employs a quantitative approach, utilizing nonprobability sampling and data collection facilitated by the G-Power application. Data analysis was conducted using Smart-PLS version 4 software. The findings reveal a positive and significant impact of performance expectancy on behavioral intention, a positive and significant effect of effort expectancy on behavioral intention, and a positive and significant influence of social influence on behavioral intention. Furthermore, behavioral intention positively and significantly affects use behavior. Additionally, performance expectancy, effort expectancy, and social influence positively and significantly impact use behavior through behavioral intention.

Flow Injection Analysis–Mass Spectrometry for the fast detection of frauds in Coleus forskohlii food supplements

Coleus forskohlii supplements (FKSs) are widely consumed for their obesity-preventing effects, mainly related to forskolin, its main bioactive compound. However, these supplements are susceptible to adulteration and various types of frauds have been described. Therefore, the development of rapid and sensitive methodologies is of great interest in order to determine the quality of these products.In this work a new method by Flow Injection Analysis–Mass Spectrometry (FIA-MS) has been developed and validated for the detection of potential frauds in FKSs. A Box-Behnken experimental design was used for the optimization of MS parameters under both positive and negative polarity modes of the electrospray ionization source. The best results were obtained for positive polarity mode at 3000 V of capillary voltage, a flow rate of 12 L min−1 of drying gas, 300 °C of nebulization gas temperature, a pressure of 30 psi and a fragmentor voltage of 149 V, using 1% of 1 mM sodium acetate in the carrier. When 22 FKS were analysed by the optimised FIA-MS method, 45% of the samples were found to be free of forskolin. There was also a high percentage (69 %) of mismatches between the quantified forskolin content and that declared on the FSK labels. In general, results were comparable with those obtained by LC-MS. Thus, it has been proved that FIA-MS is a fast, cost-effective and simple tool to evaluate the authenticity of FKSs.

Medical Audit of Hemophilia Services for National Health Insurance Participants in Bogor: A Case Report

BPJS Kesehatan is a legal entity established to implement social security programs, provide health service benefits through regulations, and manage (purchasing and investing) carefully, prudently, transparently, efficiently, and effectively. One of the health service benefits provided by BPJS Kesehatan is for hemophilia. Hemophilia is a hereditary blood clotting disorder that is currently the most prevalent globally and is based on X-linked recessive genetic inheritance. Since the Regulation of the Minister of Health of the Republic of Indonesia Number 3 Year 2023, claims for hemophilia services, including the top-up of anti-hemophilia factor drugs in 2023, have increased. The method used to write this article is quantitative descriptive with a case report approach in the form of a report from BPJS Kesehatan, which conducted an administrative claim audit on the potential fraud committed in an attempt to inflate costs, which is then followed by a medical audit. The result of the medical audit indicated that there were inaccuracies in the financing of hemophilia services based on medical service standards that were not yet met, leading to sanctions that the hospital must undergo, namely the recovery of financing losses.

Data Analytics in Insurance Product Management

Data analytics as a part of insurance product management is revolutionizing the industry because with huge and constantly increasing piles of customer and claims data at their fingertips, insurers can make better decisions and improve many aspects of their operations. This paper discusses how the adaptation of risk models and artificial intelligence models helps insurers to improve evaluation criteria and policy premiums, as well as predict the occurrence of claims with a high degree of certainty. Challenging customer segments can be detected using big data analytics, which helps insurers better serve their clients and gain their trust. In addition, there is also the function of using data analytics to detect frauds hence predictive models detect potential frauds since they identify cer tain patterns. However, the application of data analytics in insurance product management has some difficulties in terms of data quality, data privacy, and human resources to analyze sophisticated data. These challenges demand strong investments in infrastructure for storage, terrible, and processing as well as the recruitment and training of skilled data professionals, together with a solid data governance mentality. This abstract establishes that, in addition to improving the internal processes within insurance organizations, data analytics also increases market competitiveness, innovation, and customer focus in insurance products.

Development of an Integrated AI Model Based on CNN-SVM for Electricity Theft Detection

This research presents the development and implementation of an integrated artificial intelligence model for electricity theft detection, combining Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). The primary objective was to create a more accurate, efficient, and scalable method for identifying fraudulent electricity consumption patterns. Our CNN-SVM hybrid model leverages CNNs for automatic feature extraction from complex consumption data and SVMs for effective classification. This synergy allows for superior performance in detecting subtle anomalies indicative of electricity theft. The methodology involved pre-processing a large dataset of electricity consumption records, training the CNN to extract relevant features, and optimising the SVM classifier to distinguish between normal and fraudulent patterns. We evaluated the model's performance using metrics including accuracy, precision, recall, F1-score, and ROC AUC. Results demonstrated that our integrated CNN-SVM model significantly outperformed conventional machine learning techniques and standalone models in electricity theft detection. The model achieved an accuracy of 96.6%, with a precision of 97.2% and a recall of 96.1%. Comparative analysis against other state-of-the-art algorithms revealed consistently superior performance across all evaluation metrics. To enhance practical applicability, we developed and deployed a web application that implements the model, allowing for user-friendly interaction and real-time theft detection. This addition bridges the gap between research and real-world implementation, providing utility companies with an accessible tool for fraud detection. The study also explored the model's potential for real-time application and scalability to large-scale utility operations. Our findings suggest that the computational efficiency of the CNN-SVM model, coupled with the web application, offers utility companies a powerful and accessible tool for rapid response to potential fraud. This research contributes to the field of electricity theft detection by introducing a novel, high-performance AI model with a practical web-based implementation. The proposed approach not only improves detection accuracy but also offers potential for immediate real-world application, paving the way for more effective fraud prevention in the utility sector.

EVALUATING THE IMPACT OF MULTI FACTOR AUTHENTICATION ON CYBERSECURITY EFFECTIVENESS

In today's digital age, the convenience of online financial transactions is accompanied by rising cybersecurity risks. The shift to digital platforms for conducting daily financial activities has significantly exposed customers to cyber threats. These threats often result from unauthorized access, phishing attacks, and fraud attempts by cybercriminals. Ensuring the security of these transactions is crucial to maintain users' trust and to protect sensitive financial data. This study proposes a comprehensive security framework to strengthen the safety of online financial transactions, using a combination of multi-factor authentication (MFA) and a machine learning-based fraud detection system, termed "E-cyberboost." The framework operates on two primary layers, each designed to address a different aspect of security: identity verification and real-time fraud detection. The first layer involves multi-factor authentication, where users must verify their identity. This layer typically combines something the user knows (like a password) with something they have (such as a one-time password or OTP sent to their mobile device) or something they are (such as fingerprint or facial recognition). MFA significantly reduces unauthorized access by ensuring that even if one authentication factor is compromised, additional verification is required to gain access. The second layer introduces an advanced machine-learning component. Named "E-cyberboost," this machine learning model actively monitors ongoing transactions, detecting patterns that may indicate fraudulent activity. If the system identifies any unusual or suspicious transaction behaviour, it immediately triggers a security response, such as additional user verification or temporarily blocking the transaction. This proactive approach allows the system to address potential fraud in real time, adapting to evolving threats by learning from previous incidents and continuously improving its detection accuracy. By combining the preventive measures of MFA with the responsive capabilities of machine learning, this framework provides robust security against both common and sophisticated cyber threats. The study discusses the methodology, implementation, and effectiveness of this layered approach in reducing the risk of unauthorized transactions and protecting users' financial information. The proposed framework aims to enhance trust in online financial platforms and ensure a safer digital transaction experience for users. Index Terms—Multifactor authentication, machine learning, fraud detection, E-cyberboost

Application of Technology Artificial Intelligence in Claim Settlement at PT. Asuransi Allianz Life Syariah Medan Branch

This research aims to analyse the effectiveness of implementing artificial intelligence (AI) technology in accelerating the settlement process of Sharia insurance claims at PT Asuransi Allianz Life Syariah Medan Branch. The main focus of this research is to measure the impact of AI technology on customer satisfaction and the company's performance in handling Sharia insurance claims. Using qualitative research methods, data were collected through in-depth interviews with company representatives and analysis of the company's internal documents. The research results indicate that AI technology has significantly contributed to improving operational efficiency, speed, and accuracy in claim resolution. The implementation of AI enables more effective process automation, faster decision-making, and more accurate identification of potential fraud. Furthermore, the use of AI also supports the enhancement of compliance with Sharia principles, ensuring that all processes and decisions are in line with Sharia law. This research provides valuable insights for insurance companies looking to implement AI technology in their operations, particularly within the scope of Sharia insurance.

The Basics of Robotic Process Automation in Insurance Claims

Through integrating Robotic Process Automation (RPA) and artificial intelligence, insurance claims processing has achieved unprecedented efficiency and accuracy. Key results include a 90% reduction in processing time (from 72 hours to under 5 minutes), 40-70% cost reductions in claims operations, and 99% accuracy rates for standard forms. Insurance carriers implementing RPA report transformative benefits: a 65% decrease in customer service costs, 92% automation of routine customer interactions, and improvement in Net Promoter Scores by 30 points. The technology has particularly excelled in fraud detection, with AI-augmented systems identifying 53% more potential fraud indicators and saving mid-sized insurers an average of $4.2 million annually. Leading organizations are now achieving straight-through processing rates of up to 75% for standard claims, demonstrating how RPA is setting new benchmarks for operational excellence in insurance claims management

Pengaruh Audit Internal Dan Sistem Informasi Akuntansi Terhadap Kecurangan Akuntansi Pada PT Pegadaian (Persero) Cabang Ngupasan

This research aims to analyze the influence of internal audit and accounting information systems on accounting fraud at PT Pegadaian (Persero) Ngupasan Branch. Effective internal audits and accounting information systems are expected to be able to identify potential fraud and strengthen internal controls. This research technique is quantitative with a data acquisition method using a questionnaire distributed to employees who play a role in internal audit and management of accounting information systems. The results of this research indicate that internal audit and accounting information systems at PT Pegadaian (Persero) Ngupasan Branch do not have a significant effect on accounting fraud. This indicates that other factors outside internal audit and accounting information systems may contribute more to influencing accounting fraud, and further research is needed to identify these factors.

Financial Inclusion and Climate Resilience: The Role for an AI-Enhanced Digital Wallet in Caribbean SIDS

Caribbean Small Island Developing States (SIDS) are highly vulnerable to extreme weather events and climate change. Caribbean SIDS climate vulnerability is worsened by their high level of financial exclusion. Many people do not have bank accounts and access to electronic fund transfer (EFT). As such, they cannot electronically receive funds before or after a natural disaster to cope with the effects. The financial exclusion problem can be addressed through a digital wallet. A digital wallet is a financial transaction application that securely stores a user's banking and payment information on a cloud interface and allows the user to perform a transaction while hiding their banking information from a vendor. The biggest concern of users with regard to the use of digital wallets is its convenience and security. While digital wallets offer outstanding convenience of purchasing goods and services, data privacy and fraud risks deter people from adopting mobile payment. Potential fraud risk to digital wallets can be identified with anomaly detection techniques. The research problem investigated in this study is how the implementation of an artificial intelligence (AI)-enhanced digital wallet can facilitate financial inclusion in the Caribbean, particularly in the context of disaster preparedness and recovery. Since security is an important aspect of a digital wallet, a sub-objective of this study is to derive an appropriate model for anomaly detection in financial transactions in a digital wallet. This study modifies the standard Gated Transformer Networks (GTN) architecture to allow for univariate time series classification. The corresponding new model is referred to as the Univariate Gated Transformer Network (UGTN). The UGTN is used for anomaly detection in financial data from a digital wallet. This study also provides policy recommendations for the implementation of a digital wallet to facilitate financial inclusion and climate resilience in Caribbean SIDS. Received: 27 August 2024 | Revised: 9 October 2024 | Accepted: 5 November 2024 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in github at https://github.com/doncharles005/Digital-Wallet-and-Beryl/tree/main. Author Contribution Statement Don Charles: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration.

The ABC Laundry Service Information System Based on Web using SDLC Method

In this modern era, the laundry service business has experienced rapid development along with the increasing public need for practical and instant services. Laundry ABC, a laundry business located in the Kimpulan area near the UII campus, faces significant challenges in managing customer data and transactions because it still uses a manual system. This manual system often causes recording errors, service delays, and potential fraud that impact financial reports and customer satisfaction. This study aims to design and develop a web-based laundry service information system using the System Development Life Cycle (SDLC) method with a waterfall approach. The proposed information system will automate the management of customer data, transactions, and financial reports, as well as improve the operational efficiency of Laundry ABC. This system is web-based and integrated with a database to ensure accurate recording, speed up the service process, and provide better data management. With this system, Laundry ABC is expected to improve the quality of service and gain a competitive advantage in the market.

OPTIMIZATION OF DIGITAL TECHNOLOGY AND ETHICS IN FRAUD PREVENTION

This study aims to empirically test the effects of information technology audits and professional ethics have a positive effect on fraud prevention efforts in financial services companies. To support this study, the method used by the researcher is a descriptive and verification analysis method with a quantitative approach to 40 auditors from 12 financial services companies. Data were collected by distributing questionnaires via Google Form with data analysis carried out using the multiple linear regression method with the help of SPSS software version 2.3. The results of the analysis show that information technology audits and professional ethics both have a significant positive effect on fraud prevention efforts. This finding emphasizes the importance of integration between information technology, audit practices, and professional ethics in maintaining the security and integrity of the company from potential fraud. This study provides an important contribution to understanding how these factors interact in the context of fraud prevention in companies.

Pencegahan Tindak Korupsi di dalam Lingkungan Perkantoran

Preventing acts of corruption in the office environment involves a series of strategic steps to create a culture of transparency and accountability. These efforts include implementing a code of ethics, training on integrity, and strict supervision of the decision-making process. In addition, the application of information technology to monitor financial transactions and reports can help detect potential fraud. By building awareness and commitment of all employees towards preventing corruption, organizations can increase public trust and create a healthier work environment. Preventing acts of corruption in the office environment is a crucial step in creating good governance and improving institutional integrity. Corruption can hinder economic growth and damage public trust. To prevent this, a comprehensive approach is needed, including implementing transparency policies, strengthening supervisory systems, and ethical education for employees. In addition, an organizational culture that supports openness and accountability must be built. This research discusses effective strategies that can be implemented to minimize the risk of corruption, including the use of information technology and participation in community oversight. The results are expected to provide recommendations for government and private agencies in strengthening efforts to prevent corruption

Applying Bayesian Networks for Detecting Bank Fraudulent Transactions in Nigeria

Bank fraud is an increasingly prevalent issue, causing substantial financial losses for both financial institutions and customers. Also, with the increasing prevalence of bank fraud, especially facilitated by online banking and digital payments, there is a pressing need for potential expertise deployment and advanced fraud detection techniques in real-world banking systems. This study specified the Bayesian Network models for detecting fraudulent bank transactions. Bayesian Networks offer a probabilistic graphical modeling approach that can effectively capture complex relationships and dependencies within financial data. Thus, the research aimed at developing a custom Bayesian network model trained on a large dataset of bank transactions comprising over one million bank transactions to classify instances as fraudulent or non-fraudulent. Python 3.11.10 was used for the graphical representations. Down-sampling was employed to reduce the dataset from its initial size of 1 million observations to 17,650 observations, ensuring a balanced representation of both fraud and non-fraud instances. Various estimation techniques: Maximum Likelihood, Bayesian Networks, and Expectation Maximization were employed and evaluated to learn the model parameters. The model's performance was measured using metrics: accuracy, precision, recall, F1-score, and ROC-AUC. The Bayesian Networks estimator achieved an overall accuracy of 66.18%, precision of 67.73%, F1-score of 64.06%, ROC-AUC of 66.13%, Recall of 60.77%, Sensitivity of 60.77% and Specificity of 71.50%. Also, the Maximum Likelihood achieved an overall Accuracy of 66.77%, Precision of 69.22%, F1-score of 63.96%, ROC-AUC of 66.71%, Recall of 59.45%, Sensitivity of 59.45% and Specificity of 73.97%. Likewise, the Expectation Maximization achieved an overall Accuracy of 66.83%, Precision of 69.31%, F1-score of 64.00%, ROC-AUC of 66.77%, Recall of 59.45%, Sensitivity of 59.45% and Specificity of 74.09%. On the confusion matrix, the model correctly classified 1272 instances as Non-Fraudulent transactions (True Negative). Also, it was observed that the model incorrectly classified 507 instances as fraudulent transactions when they were Non-Fraudulent (False Positive). The model similarly incorrectly classified 687 instances as Non-Fraudulent transactions when they were Fraudulent (False Negative). Finally, it correctly classified 1064 instances as fraudulent transactions (True Positive). These results demonstrated the Bayesian Network's ability to identify fraudulent transactions while minimizing false alarms accurately. The findings highlight the potential of Bayesian Networks as a robust framework for fraud detection in the banking sector, contributing to enhanced security and reduced financial losses. The developed model can be introduced into existing banking systems to strengthen fraud prevention strategies.

VOTEMAT: A Blockchain Based Voting System

This study aims to show that a secure, trustable and immutable voting system can be established with Blockchain technology. Decentralized structure of the Blockchain excludes the central authority and provides transparency. Moreover, its cryptographic functions enable secure transactions. Therefore, the operation is prevented from potential frauds, such as multiple votes, fake vote attempts, and fraudulent vote counts. The proposed method, VOTEMAT, covers both electronic voting and paper ballot as a complete solution. A mobile application and a Website, connected to Ethereum private Blockchain network, were developed for the voters who prefer to cast their votes remotely. It is also possible to vote in the voting centres via the mobile device or paper ballot placed in the vote boxes; but these votes are also recorded in the same Blockchain and equally secure. For the remote users, a two-step authentication is designed, based on the information on the national identity card and face recognition. An additional encryption based security measure is used to avoid hacking attempts, such as man in the middle attacks. Since the proposed system is more practical than the traditional voting methods, it can increase the participation and be utilized in all kinds of local or national elections.

Optimizing Machine Learning Classifiers for Credit Card Fraud Detection on Highly Imbalanced Datasets Using PCA and SMOTE Techniques

Card fraud detection refers to the process of identifying unauthorized or suspicious transactions made using credit or debit cards. It employs machine learning models, rule-based systems, and anomaly detection techniques to detect patterns indicating potential fraud. There is a growing need for systems that can accurately predict and prevent fraudulent transactions. Reducing financial loss by Implementing advanced detection models to safeguard it from fraud or malicious transactions. Therefore, we proposed machine learning models that will predict credit card fraud at an early stage. Also, the study used feature scaling, Principal Component Analysis (PCA), and the Synthetic Minority Over-sampling Technique (SMOTE) to deal with the class imbalance on the dataset. Moreover, SMOTE is applied to balance the classes by synthesizing examples of the minority class, making classifiers more robust. The results show that LR, SVM, KNN, and XGBoost models correctly predict 97\% of fraudulent and non-fraudulent cases. The Decision Tree and the Random Forest models are capable of achieving at least 96\%, respectively. This research combines advanced machine learning methodologies with real-time processing to give insights into predictive analytics in financial fraud detection, which may enhance accuracy and efficiency in financial security systems.

Rapid Classification of Milk Using a Cost-Effective Near Infrared Spectroscopy Device and Variable Cluster-Support Vector Machine (VC-SVM) Hybrid Models.

Removing fat from whole milk and adding water to milk to increase its volume are among the most common food fraud practices that alter the characteristics of milk. Usually, deviations from the expected fat content can indicate adulteration. Infrared spectroscopy is a commonly used technique for distinguishing pure milk from adulterated milk, even when it comes from different animal species. More recently, portable spectrometers have enabled in situ analysis with analytical performance comparable to that of benchtop instruments. Partial Least Square (PLS) analysis is the most popular tool for developing calibration models, although the increasing availability of portable near infrared spectroscopy (NIRS) has led to the use of alternative supervised techniques, including support vector machine (SVM). The aim of this study was to develop and implement a method based on the combination of a compact and low-cost Fourier Transform near infrared (FT-NIR) spectrometer and variable cluster-support vector machine (VC-SVM) hybrid model for the rapid classification of milk in accordance with EU Regulation EC No. 1308/2013 without any pre-treatment. The results obtained from the external validation of the VC-SVM hybrid model showed a perfect classification capacity (100% sensitivity, 100% specificity, MCC = 1) for the radial basis function (RBF) kernel when used to classify whole vs. not-whole and skimmed vs. not-skimmed milk samples. A strong classification capacity (94.4% sensitivity, 100% specificity, MCC = 0.95) was also achieved in discriminating semi-skimmed vs. not-semi-skimmed milk samples. This approach provides the dairy industry with a practical, simple and efficient solution to quickly identify skimmed, semi-skimmed and whole milk and detect potential fraud.

KONSTRUKSI MODEL PENCEGAHAN FRAUD MELALUI RED FLAG BASED SYSTEM DI PEMERINTAH KABUPATEN JEMBER (STUDI KASUS DI INSPEKTORAT KABUPATEN JEMBER)

This research aims to present a system that can prevent fraud in the form of compliance and internal control within the Jember Regency Government through a red flag-based system. The study uses a qualitative case study method. The Inspectorate currently requires indicators to prevent the occurrence of fraud. The techniques employed to collect data include observation, interviews, and documentation. The results indicate that all Regional Device Organizations (OPD) in Jember Regency analyze various risks. These risks will be further analyzed for their root causes and the impacts they may generate. Based on the risk map that has been created, there are a total of 412 risks identified across 72 Regional Device Organizations (OPD) in Jember Regency. The focus of the risks with potential fraud in this study lies within the category related to finance (finance risk). Specifically, there are 33 financial-related risks identified across all Regional Device Organizations (OPD) in Jember Regency.

FRAUD DETECTION IN FINANCIAL TRANSACTIONS THROUGH DATA SCIENCE FOR REAL-TIME MONITORING AND PREVENTION

This study presents a comprehensive review of the use of advanced technologies in credit card fraud detection, with a focus on machine learning, blockchain, and federated learning, to understand their transformative impact on the field. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a total of 97 articles were systematically reviewed and analyzed. The findings reveal that machine learning models, such as decision trees, support vector machines, and neural networks, have significantly improved fraud detection accuracy, reducing false positives and enhancing the ability to detect complex fraud patterns in real-time. Blockchain technology also plays a critical role by providing a decentralized, secure, and transparent framework for fraud detection, ensuring the integrity of transaction records and making fraudulent activities harder to conceal. Federated learning offers a privacy-preserving solution, enabling institutions to collaborate on fraud detection without sharing sensitive data, which is increasingly important in light of stringent regulatory requirements. Additionally, the study highlights the growing use of predictive analytics in forecasting potential fraud, allowing financial institutions to proactively prevent fraud before it occurs. Moreover, feedback loops integrated into fraud detection models allow for continuous improvement, ensuring that detection systems can adapt to new and evolving fraud tactics. Overall, the review underscores the importance of adopting these advanced technologies to build more secure, efficient, and adaptive fraud detection systems capable of safeguarding financial transactions in the modern digital economy.

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.