Traditional Fraud Research Articles

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.

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

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

Proactive fraud defense: Machine learning’s evolving role in protecting against online fraud

As online fraud becomes more sophisticated and pervasive, traditional fraud detection methods are struggling to keep pace with the evolving tactics employed by fraudsters. This paper explores the transformative role of machine learning in addressing these challenges by offering more advanced, scalable, and adaptable solutions for fraud detection and prevention. By analyzing key models such as Random Forest, Neural Networks, and Gradient Boosting, this paper highlights the strengths of machine learning in processing vast datasets, identifying intricate fraud patterns, and providing real-time predictions that enable a proactive approach to fraud prevention. Unlike rule-based systems that react after fraud has occurred, machine learning models continuously learn from new data, adapting to emerging fraud schemes and reducing false positives, which ultimately minimizes financial losses. This research emphasizes the potential of machine learning to revolutionize fraud detection frameworks by making them more dynamic, efficient, and capable of handling the growing complexity of fraud across various industries. Future developments in machine learning, including deep learning and hybrid models, are expected to further enhance the predictive accuracy and applicability of these systems, ensuring that organizations remain resilient in the face of new and emerging fraud tactics.

Digital Twin and IoT-Enabled Online Learning Model for Real- Time Monitoring and Control of Fraudulent Transaction in the Banking industry

Fraudulent transactions have long been a challenge in the banking industry, causing significant financial losses and operational burdens. Traditional fraud detection systems often struggle to adapt and respond in real-time to the evolving tactics of fraud. This study aims to develop a digital twin and IoT-enabled online learning model for real-time monitoring and control of fraudulent transactions in banking. The proposed system integrates digital twin technology and IoT sensors to create a virtual replica of transaction environments, enabling continuous learning and adaptation using an online learning model. Performance metrics such as detection speed, prediction accuracy, precision, recall, and F1 score were used to evaluate the system. The system was tested using a dataset of banking transactions, including legitimate and fraudulent activities. Key results include detecting fraudulent transactions 40% faster than traditional methods, reducing detection time from 5 minutes to 3 minutes. The model achieved an accuracy of 92%, an 8.24% improvement over traditional fraud detection systems. The false positive rate was reduced by 28%, meaning fewer legitimate transactions were wrongly flagged as fraudulent. Precision increased by 5.95%, reaching 89%, and recall improved by 8.75%, reaching 87%. This led to a 7.32% increase in the F1 score, reaching 88%. The system offers enhanced real-time fraud detection capabilities, reducing false positives and improving overall security and operational efficiency in the banking industry.

Scalable and Robust Fraud Detection in Distributed Systems

The rise of distributed systems has increased the need for advanced fraud detection mechanisms. Cybercriminals increasingly exploit the distributed and decentralized nature of these systems, posing challenges for traditional fraud detection techniques that rely on centralized data analysis. In this paper, we propose a novel approach to fraud detection that is decentralized, scalable, and capable of real-time detection across diverse nodes in distributed systems. Our solution combines machine learning techniques, including anomaly detection and classification algorithms, with decentralized consensus mechanisms. We evaluate our approach using a large-scale financial dataset and outline its performance in terms of accuracy, latency, and scalability. This work also discusses challenges such as data privacy, adversarial attacks, and regulatory compliance, providing directions for future research

Integrating machine learning and blockchain: Conceptual frameworks for real-time fraud detection and prevention

Integrating machine learning (ML) and blockchain technologies presents a groundbreaking approach to real-time fraud detection and prevention, addressing the growing complexity and sophistication of financial fraud schemes. This integration leverages the strengths of both technologies: the predictive power of ML algorithms and the transparency, security, and immutability of blockchain. Machine learning algorithms are proficient at analyzing large datasets, detecting patterns, and identifying anomalies that signify potential fraud. By employing supervised learning models such as logistic regression, decision trees, and neural networks, financial institutions can classify transactions and predict fraudulent activities with high accuracy. Unsupervised learning techniques, such as clustering and anomaly detection, are instrumental in discovering new fraud patterns without the need for labeled data, thus enhancing the detection of novel fraudulent behaviors. Blockchain technology, on the other hand, provides a decentralized and tamper-proof ledger that ensures data integrity and traceability. Transactions recorded on a blockchain are immutable and transparent, allowing for real-time monitoring and auditing. Smart contracts, self-executing contracts with the terms directly written into code, can be programmed to trigger alerts or actions when suspicious transactions are detected, further automating the fraud prevention process. The conceptual framework for integrating ML and blockchain involves several key components. First, data from financial transactions are continuously collected and stored on a blockchain, ensuring transparency and security. ML algorithms analyze this data in real time, identifying suspicious patterns and flagging potential fraud. When a potential fraud is detected, a smart contract is executed, which can instantly block the transaction, alert the relevant authorities, or initiate further verification processes. This integrated approach addresses several challenges in traditional fraud detection systems. The decentralized nature of blockchain eliminates single points of failure and reduces the risk of data tampering. The transparency of blockchain enhances the trustworthiness of the detection process, while the predictive capabilities of ML provide high accuracy and adaptability to new fraud tactics. Additionally, the real-time processing capabilities of both technologies ensure prompt detection and prevention of fraudulent activities. In conclusion, the integration of machine learning and blockchain offers a robust framework for real-time fraud detection and prevention. This synergy not only enhances the security and reliability of financial transactions but also paves the way for more advanced and automated compliance systems, ultimately strengthening the financial ecosystem against fraudulent threats.

An Intelligent Financial Fraud Detection Model Using Knowledge Graph-Integrated Deep Neural Network

Financial fraud detection has been an urgent technical demand in cyberspace. It highly relies on clear extraction and deep representation toward complex relationships inside financial social networks. As consequence, this study combines both knowledge graph and deep learning to deal with such issue. Thus, an intelligent financial fraud detection model based on knowledge graph guidance and deep neural network is proposed in this paper. First, a new knowledge graph based on financially related systems is constructed, which includes multiple entities and relationships. Then, an adversarial learning-based neural network structure is formulated to extract financial attributes. Finally, the detection results can be output according to the extracted factors. Empirically, the proposal is implemented on a real-world dataset for performance evaluation. The experimental results show that it has higher accuracy and effectiveness compared to traditional fraud detection methods. The proposed detection model can not only identify known fraudulent behaviors, but also predict potential fraud patterns based on consumer habits, thereby improving the security and reliability of financial transactions. It can also update the knowledge graph in real-time, enabling it to cope with emerging fraud methods and variants.

Cloud-Based Transaction Fraud Detection: An In-depth Analysis of ML Algorithms

Context: Cloud-based services are increasingly central in financial technology, enabling scalable and efficient transactions. However, they also heighten vulnerability to fraud, challenging the security of online financial activities. Traditional fraud detection struggles against sophisticated tactics, highlighting the need for advanced, cloud-compatible solutions. Objectives: This study assesses machine learning (ML) algorithms' ability to detect fraud in cloud environments, focusing on Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVM), and XGBoost (XGB). It uses a comprehensive dataset to determine which ML model best identifies fraudulent transactions, aiming to optimize these models for accuracy, precision, and efficiency in real-time detection. Results: The XGBoost model outperformed others in fraud detection accuracy, with Random Forest and Decision Trees also showing high effectiveness. These models were particularly good at balancing precision and recall, minimizing false positives, and accurately identifying fraud in complex transaction patterns. Conclusion: ML, especially ensemble and boosting models like XGBoost and Random Forest, offers a strong approach to detecting transaction fraud in cloud-based financial systems. Their capacity to handle vast data volumes and adapt to new fraud patterns enhances cloud transaction security. Implication: The study provides a guide for financial and cloud services to implement sophisticated fraud detection systems. It emphasizes the importance of continual ML innovation to tackle digital finance fraud, suggesting that adopting these advanced ML models can significantly reduce transaction fraud risks, ensuring a secure, efficient, and trustworthy platform for users.

Potential COVID-19 test fraud detection: Findings from a pilot study comparing conventional and statistical approaches.

Some COVID-19 testing centres have reported manipulated test numbers for antigen tests/rapid tests. This study compares statistical approaches with traditional fraud detection methods. The extent of agreement between traditional and statistical methods was analysed, as well as the extent to which statistical approaches can identify additional cases of potential fraud. Outlier detection marking a high number of tests, modeling of the positivity rate (Poisson Regression), deviation from distributional assumptions regarding the first digit (Benford's Law) and the last digit of the number of reported tests. The basis of the analyses were billing data (April 2021 to August 2022) from 907 testing centres in a German city. The positive agreement between the conventional and statistical approaches ('sensitivity') was between 8.6% and 24.7%, the negative agreement ('specificity') was between 91.3% and 94.6%. The proportion of potentially fraudulent testing centres additionally identified by statistical approaches was between 7.0% and 8.7%. The combination of at least two statistical methods resulted in an optimal detection rate of test centres with previously undetected initial suspicion. The statistical approaches were more effective and systematic in identifying potentially fraudulent testing centres than the conventional methods. Testing centres should be urged to map paradata (e.g. timestamps of testing) in future pandemics.

Leveraging Cryptographic Hash Functions for Credit Card Fraud Detection

Credit card fraud remains a significant challenge in the financial industry, posing substantial financial losses to both consumers and businesses. Traditional fraud detection methods often rely on rule-based approaches and statistical models, which may struggle to keep pace with evolving fraud tactics and sophisticated cyber threats. In this paper, we propose a novel approach to credit card fraud detection leveraging cryptographic hash functions. Cryptographic hash functions offer robust security guarantees, including collision resistance and preimage resistance, making them well-suited for ensuring the integrity and authenticity of transaction data. Our proposed system employs cryptographic hash functions, such as SHA-256, to generate unique hash values for credit card transactions. These hash values serve as digital fingerprints of the transaction data, enabling secure verification and auditing of transactions on the blockchain. We conducted experiments using a dataset of 100,000 credit card transactions, evaluating the performance of our system in terms of accuracy, precision, recall, and F1-score. The results demonstrate the effectiveness of our approach in accurately identifying fraudulent transactions while minimizing false positives. Furthermore, we discuss the implications of our findings and explore future research directions, including the integration of advanced cryptographic techniques and blockchain technology to enhance the security and privacy of credit card transactions. Overall, our study underscores the importance of cryptographic hash functions in building robust and secure fraud detection systems capable of combating emerging fraud threats in the digital era.

Credit Card Fraud Detection System

Globally, credit card fraud is a serious threat to people, businesses, and financial institutions. With the rise of online transactions, fraudsters have developed clever ways to take advantage of loopholes in payment systems. Traditional fraud detection methods based on manual inspections and rules-based systems are unable to counteract this new and evolving risk. As a result, the use of data analytics and machine learning has become a viable option for real-time detection and prevention of credit card fraud. The paper looks at using machine learning algorithms such as logistic regression, decision trees, random forests, neural networks, etc. to detect fraudulent transactions We go over the importance of data sources and components, analytical metrics, and how fraud detection on the effectiveness of examples. In addition, we list the current challenges and directions in which credit card fraud detection is likely to continue, including the use of blockchain technology and sophisticated AI techniques. Overall, this study highlights the importance of credit card theft detection and the promise of machine learning in mitigating this ubiquitous problem financial institutions use advanced machine learning algorithms and analytics function to detect fraudulent behaviour, protect customer interests, and maintain payment environment integrity to improve their capabilities.

Research on the Application of Artificial Intelligence and Big Data Technology in Financial Fraud Detection

Financial fraud is a hidden criminal activity that has a serious impact on the stability of financial markets and investor confidence. Traditional fraud detection methods are often inefficient and unable to meet the rapidly changing fraudulent means. The development of artificial intelligence and big data technology has provided new solutions for financial fraud detection. This article aims to explore the current application status and methods of artificial intelligence and big data technology in financial fraud detection, and analyze the challenges and future development directions.

Machine Learning in Financial Transaction Fraud Detection and Prevention

In this paper, we delve into the application of machine learning technology in financial transaction fraud detection and prevention, including how it works, real-world examples, the challenges it faces, and its potential impact on the future of the financial security space. As the digitalisation process of the financial industry accelerates, the means of financial transaction fraud are becoming increasingly complex and varied, bringing great risks to individuals, enterprises and even the entire financial system. In this context, traditional fraud detection methods are increasingly difficult to effectively respond to emerging frauds due to their inherent limitations. In contrast, machine learning, with its powerful data processing capability, complex pattern recognition ability, and self-learning and adaptation, is considered a powerful tool against financial transaction fraud. Through real-world case studies, we demonstrate how the application of machine learning techniques in financial fraud detection and prevention has helped financial institutions improve detection efficiency and accuracy. Covering a wide range of financial fraud types from credit card fraud to account hijacking to money laundering, these cases illustrate how machine learning models can play a role in monitoring transaction activities in real time, identifying unusual behaviours, and adapting to new fraudulent tactics. In addition, we also discuss the main challenges faced when implementing machine learning techniques, including data quality and privacy protection, model interpretability, the cost of implementing the technology, and integration issues with existing systems. While machine learning shows great potential for financial transaction fraud detection and prevention, there are a number of technical and practical challenges that need to be overcome in order to take full advantage of its benefits. These include improving the efficiency of data collection and processing, ensuring transparency and interpretability of models, reducing the cost of technology implementation and enhancing cross-industry collaboration. In the future, as machine learning and related technologies continue to advance, and as financial institutions and regulators gain a deeper understanding of these technologies, it is reasonable to believe that machine learning will play an even more important role in the financial security field, contributing to the building of a safer, more efficient and smarter financial system. The goal of this paper is to provide financial institutions, technology developers, and policy makers with a comprehensive view of the application of machine learning in financial transaction fraud detection and prevention, pointing out future directions and potential strategies by analysing its advantages, challenges, and practical examples. Through this research, we hope to facilitate broader industry discussions, promote innovation and development of financial security technologies, and jointly address the challenges of financial fraud.

Does financial literacy contribute to facilitating residents in safeguarding their rights as financial consumers?A three-stage study based on the perspective of “fraud” phenomenon

Utilizing microdata from six provinces in East China, this study centers on the phenomenon of “fraud," employing a three-stage Probit model with dual sample selectivity to explore the impact of financial knowledge on residents' safeguarding of financial consumer rights and interests. This methodological approach effectively addresses estimation bias arising from concerns related to dual sample selectivity. The process of residents safeguarding their rights and interests when confronted with fraud unfolds in three sequential stages: “pre-fraud prevention, during fraud loss avoidance, and post-fraud rights protection." Key findings indicate that, on the whole, higher levels of financial knowledge foster residents' protection of financial consumer rights and interests. Specifically, heightened financial literacy is associated with an increased likelihood of encountering fraud, a reduced probability of incurring losses post-fraud, and an enhanced propensity for pursuing rights protection thereafter. Moreover, heterogeneity analysis reveals that the impact of financial knowledge is more pronounced among middle-aged and elderly individuals, rural inhabitants, and women. Furthermore, concerning various types of fraud, the amplification effect of financial knowledge is particularly evident in cases of telecom fraud and online fraud, though not in traditional fraud cases. Additionally, moderation effect analysis underscores that, overall, heightened attention to information, improved digital literacy, and increased social interaction can reinforce the positive influence of financial knowledge. This paper provides valuable insights for strengthening the protection of residents' financial rights and ensuring the security of their assets.

Adaptive Strategies of Digital Transformation on Students’ Consumptive Behavior in the Era of Society 5.0

The digital era has seen consistent economic growth. The increasing number of online transactions every year shows this trend. One of the positive impacts of the development of the digital economy as a method for carrying out economic transactions is that it can increase knowledge and provide practicality in transactions. The progress of digitalization provides significant benefits in changing the order of economic behavior but will have negative impacts if not handled well. With the rapid development of E-commerce, it cannot be denied that crime in this field is on the rise in Indonesia, with fraud being the most common. Online fraud is essentially the same as traditional fraud; the difference lies in the way they operate, especially the use of electronic systems. This fraud can be interpreted as misuse of the news delivery system. The use of electronic media to display news, advertisements and other purposes that cause inconvenience to site users usually occurs without the victim's permission and is often undesirable. Consequently, adaptive strategies for this digital transformation are needed to increase awareness. Sharia Economics student IAIN Syekh Nurjati Cirebon was used as the research subject in this study. This research uses a qualitative approach, with data collection through interviews. According to the findings of this research, to avoid social media crimes in the era of society 5.0, knowledge of financial literacy and digital literacy is needed. Financial literacy is the ability to manage personal finances to avoid excessive consumption, while digital literacy is knowledge about how to use social media.

An Intelligent Machine Learning Approach for Fraud Detection in Medical Claim Insurance: A Comprehensive Study

Medical claim insurance fraud poses a significant challenge for insurance companies and the healthcare system, leading to financial losses and reduced efficiency. In response to this issue, we present an intelligent machine- learning approach for fraud detection in medical claim insurance to enhance fraud detection accuracy and efficiency. This comprehensive study investigates the application of advanced machine learning algorithms for identifying fraudulent claims within the insurance domain. We thoroughly evaluate several candidate algorithms to select an appropriate machine learning algorithm, considering the unique characteristics of medical claim insurance data. Our chosen algorithm demonstrates superior capabilities in handling fraud detection tasks and is the foundation for our proposed intelligent approach. Our proposed approach incorporates domain knowledge and expert rules, augmenting the machine learning algorithm to address the intricacies of fraud detection within the insurance context. We introduce modifications to the algorithm, further enhancing its performance in detecting fraudulent medical claims. Through an extensive experimental setup, we evaluate the performance of our intelligent machine-learning approach. The results indicate significant accuracy, precision, recall, and F1-score improvements compared to traditional fraud detection methods. Additionally, we conduct a comparative analysis with other machine learning algorithms, affirming the superiority of our approach in this domain. The discussion section offers insights into the interpretability of the experimental findings and highlights the strengths and limitations of our approach. We conclude by emphasizing the significance of our research for the insurance industry and the potential impact on the healthcare system's efficiency and cost-effectiveness.

Credit Card Fraud Detection for Contemporary Financial Management Using XGBoost-Driven Machine Learning and Data Augmentation Techniques

The rise of digital transactions and electronic payment systems in modern financial management has brought convenience but also the challenge of credit card fraud. Traditional fraud detection methods are struggling to cope with the complexities of contemporary fraud strategies. This study explores the potential of machine learning, specifically the XGBoost (eXtreme Gradient Boosting) algorithm, combined with data augmentation techniques, to enhance credit card fraud detection. The research demonstrates the effectiveness of these techniques in addressing imbalanced datasets and improving fraud detection accuracy. The study showcases a balanced approach to precision and recall in fraud detection by leveraging historical transaction data and employing techniques like Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors (SMOTE-ENN). The implications of these findings for contemporary financial management are profound, offering the potential to bolster financial integrity, allocate resources effectively, and strengthen customer trust in the face of evolving fraud tactics.

National Payment Switches and the Power of Cognitive Computing against Fintech Fraud

National Payment Switches (NPSs) and International Payment Switches (IPSs), including major players such as SWIFT, Mastercard, and CHIPS, have become vital to the financial infrastructure, facilitating secure and efficient transactions among local financial institutions. Nonetheless, the growing adoption of digital payments has heightened the risk of financial fraud. Consequently, NPSs, under the direct ownership of Central Banks (CBs), are increasingly adopting advanced technologies, such as cognitive computing, to bolster their fraud detection capabilities in their respective countries. This article delves into the role of cognitive computing in detecting financial fraud within NPSs. It examines the advantages of cognitive computing in recognising patterns of fraudulent behaviour and analysing vast amounts of data. Additionally, the study highlights the importance of focusing on how cognitive computing can augment traditional fraud detection methods, such as rule-based systems and data analytics. Nineteen real-world cases from eighteen countries are analysed, exploring the cognitive computing tools employed by NPSs to identify fraudulent transactions. The challenges and limitations of implementing cognitive computing in fraud detection and potential solutions to address these issues are identified. The primary assumption that cognitive computing is crucial for detecting financial fraud in NPSs is substantiated. Its ability to analyse large datasets and pinpoint patterns of fraudulent behaviour proves invaluable for financial institutions seeking to protect themselves against financial fraud in a progressively digital world. The conclusions drawn from the overview of the cases aim to identify best practices, potentially trigger new benchmarking standards, and facilitate the development of integrated cross-border solutions to combat financial fraud on a global scale effectively. The purpose of this research is to examine the role of cognitive computing in detecting financial fraud within NPSs, identify its advantages, challenges and limitations, and provide real-world case examples.

Health insurance fraud detection by using an attributed heterogeneous information network with a hierarchical attention mechanism

BackgroundWith the rapid growth of healthcare services, health insurance fraud detection has become an important measure to ensure efficient use of public funds. Traditional fraud detection methods have tended to focus on the attributes of a single visit and have ignored the behavioural relationships of multiple visits by patients.MethodsWe propose a health insurance fraud detection model based on a multilevel attention mechanism that we call MHAMFD. Specifically, we use an attributed heterogeneous information network (AHIN) to model different types of objects and their rich attributes and interactions in a healthcare scenario. MHAMFD selects appropriate neighbour nodes based on the behavioural relationships at different levels of a patient’s visit. We also designed a hierarchical attention mechanism to aggregate complex semantic information from the interweaving of different levels of behavioural relationships of patients. This increases the feature representation of objects and makes the model interpretable by identifying the main factors of fraud.ResultsExperimental results using real datasets showed that MHAMFD detected health insurance fraud with better accuracy than existing methods.ConclusionsExperiment suggests that the behavioral relationships between patients’ multiple visits can also be of great help to detect health care fraud. Subsequent research fraud detection methods can also take into account the different behavioral relationships between patients.

Prospective Solution of Today’s Fraud Risk where Dark Triad Personality is in the Executive Ranks

The nature of human beings is an attempt to override on boundary and truth can’t always be established by law. Auditing is side language of business where users concentrate to get reliable, truthiness and full disclosure information. But recent behavioral science claimed that abnormal personality may have the gross impact on auditing which is called dark triad personality (DTP). The aim of this study is to examine and find out prospective solution where traditional fraud triangle collapses to a single dimension under DTP. In this study library and empirical research used to search new window to repair bad personality who creates and uses the opportunity of fraud. Verily the fear of Allah and His scourge in the day of reckoning may be the scathing guideline to the reckless individual who has propensity to make fraud with DTP.