Fraud Rate Research Articles

Blockchain in Supply Chain Management: Enhancing Transparency, Efficiency, and Trust

Supply chain management has become an area of focus as organizations around the world work to leverage blockchain technology to increase supply chain visibility, productivity, and reliability. As a novel approach to innovation, this paper aims at assessing whether or not blockchain technology could help overcome problems that have attributed lack of consensus between SCM and its stakeholders including, inadequate information flow, complex logistics, ad inadequate inter-organizational trust. As such, using scientific data I investigate how innovations built on the blockchain enhance efficiency, contain costs, and create instant permanent transparency, including in the supply chain. The research methods include a literature search and analysis, calculations of blockchain benefits in relation to the most important SCM indicators: transaction time, cost cutting, and transparency of suppliers. From our research, it can be suggested that advanced use of blockchain results in improved efficiency by around 20-25% of OM costs, while the error rate will be decreased up to 30%. In addition, by enhancing the history of each product, blockchains decrease fraud rates in high-risk sectors, including the pharmaceutical marketplace, by a half. These findings emphasize the positive impact that blockchain implements to alter the supply chain by promoting trust between the chain’s participants and allowing for secure transfer of necessary information. This research helps to complete the existing literature by giving concrete information about blockchain’s efficiency in the real business environment which could be useful for those companies and government agencies which are aiming to implement blockchain-based solutions in supply chain management. Finally, the ethical issues and implications related to the practical realization of blockchain in SCM are highlighted and analyzed with the help of corresponding recommendations.

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.

Impact and Significance of Accounting Standards in Enhancing Financial Transparency within Pakistan’s Textile Industry

The investigation assesses the extent to which accounting standards foster financial openness in Pakistan's textile sector, which makes a substantial economic contribution to the country through employment, exports, and income generation. Accounting standards guarantee financial reporting's comparability, consistency, and dependability, which are critical for stakeholders' decision-making, including lenders, investors, and regulatory agencies. Implementing local standards established by the Institute of Chartered Accountants of Pakistan and International Financial Reporting Standards improves accountability and transparency in Pakistan's textile sector. Even with their advantages, the complicated regulatory environment, shifting market conditions, and disparities in compliance among various firms make it difficult to adopt these standards. This evaluation looks at how following these guidelines affects the transparency of financial statements, lowers fraud rates, and lessens the chance of financial misreporting. It also discusses the sector-specific issues that Pakistan's textile industry faces, such as differences in revenue recognition, inventory value, and related party transaction disclosure that can skew evaluations of financial health. The evaluation emphasizes the role of sound accounting procedures in promoting investor trust and, importantly, in streamlining regulatory supervision, which should give the audience confidence in the oversight of the industry. Additionally, it assesses current measures to enhance compliance, such as ICAP's attempts to harmonize national standards with IFRS and government incentives for better corporate governance in the sector. Standardized accounting procedures enable sustainable growth in Pakistan's textile sector and improve the nation's standing internationally by fostering a transparent financial environment.

An Overview of Credit Card Fraud Detection Techniques

Credit card fraud is the first thing that comes to mind when the word fraud is uttered. The volume of credit card transactions has increased significantly in recent years, along with a corresponding spike in credit card fraud. Monitoring users' and customers' spending patterns helps detect fraud and stop bad behavior. There is a rising rate of credit card fraud as they become the most widely used payment mechanism for both online and offline transactions. The goal of fraud detection is to identify fraudulent conduct as soon as it is possible and to document it. The utilization of charge cards is normal in present day culture. The multimillion-dollar industry of extortion is growing each. Extortion influences the world economy fundamentally. Different contemporary strategies, for example, information mining, AI, fluffy rationale, hereditary programming, and man-made consciousness, have been produced for identifying charge card extortion. This study tells the best way to successfully consolidate information mining methods to keep a low or high misleading problem rate while accomplishing high extortion inclusion.

Deep Learning based Credit Card Fraudulency Detection System

Abstract: Huge increase in the internet usage has been observed since last decade. It led to the emergence of services like ecommerce, tap and pay systems, online bill payment systems, etc. have proliferated and become more widely used. Due to various online payment options introduced by e- commerce and other numerous websites, the possibility of online fraud has risen drastically. Thus, due to an increase in fraud rates, research on analyzing and detecting fraud in online transactions has begun utilizing various machine learning techniques. The Deep Learning techniques viz., Convolutional Neural Network Architecture is used to detect credit card frauds in the proposed model. The Principal Component Analysis transformation gives a set of numerical input variables as output which are taken as the features to be considered. Due to confidentiality concerns, some of the original characteristics and background information about the data are not entirely disclosed. Features of credit card frauds must be chosen carefully as they play important role when deep learning techniques are used for credit card fraud detection. The TensorFlow and Keras are used for the development of the current proposed model. The proposed model aims to predict credit card frauds with 91.9% accuracy

Dampak Penerapan Akuntansi Forensik Dan Audit Investigasi Serta Mengevaluasi Proses Pengungkapan Tindak Pidana Fraud Pengadaan Barang/Jasa

As a result of the forensic accounting and investigative audit processes as well as procedures for detecting criminal acts related to the procurement of goods and services, fraud occurred related to community needs as follows: Procurement of goods and services. This usually happens in Indonesia. This research uses the Partial Least Squares (PLS) analysis technique and the SmartPLS 3.2.8 software testing tool. The results show that forensic accounting practices and investigative audit results influence the detection rate of fraud in the procurement of goods and services.

Fraud risk assessment in car insurance using claims graph features in machine learning

The article proposes a process for claims assessment in car insurance, which makes it possible to calculate the fraud rate on the annual set of claims using a reduced set of attributes and graph vertex properties. This approach improves the security of insurance companies’ assets against fraudulent attacks. A method for constructing a claims graph and extracting additional features from it for evaluation is described. It is shown that in order to build a graph, it is not necessary to have data on the connection of the claim participants. Two tests were carried out on a real opensource datasets with labelling of fraudulent cases. The results of the first one show the increase in classification metrics when using attributes obtained from the graph. The application of the proposed approach resulted in doubling the area under the Precision-Recall curve. The experimental results demonstrated high quality metrics for fraud detection, with a Recall rate of 83.33% and a Specificity rate of 91.05%. The second test confirmed the possibility of determining the insurance fraud level based on decision rule, which includes the condition of claims being connected to each other. The rule is able to detect claim groups with a high concentration of fraud, in which every second participant is a fraudster.

A Comprehensive Approach to Safeguard Credit Card Transactions and Fraud Prevention

The escalating prevalence of financial fraud within the financial sector poses profound challenges. Detecting credit card fraud in online transactions necessitates data mining due to inherent complexities. Addressing two key issues—evolving patterns in legitimate and fraudulent behaviors and highly skewed datasets of credit card frauds—renders the task challenging. This paper scrutinizes the performance of naive Bayes, KNN, and logistic regression on significantly imbalanced credit card fraud data comprising 284,807 transactions from European cardholders. The dataset's skewness is addressed through a hybrid under-sampling and oversampling approach. The three techniques are applied to both unprocessed and preprocessed data. Fraud detection, defined as a set of activities thwarting illicit acquisition of assets or funds through deceptive means, varies across industries and methods. Credit card fraud, particularly susceptible due to its ease and prevalence in e-commerce and online platforms, prompted the adoption of diverse machine learning strategies to combat rising fraud rates. This paper employs machine learning algorithms for credit card fraud detection, utilizing a publicly available credit card dataset for model evaluation. While acknowledging that achieving 100% accuracy in fraud detection is elusive, the paper emphasizes the real-world applicability of its findings through the analysis of credit card data from a financial institution. In addition to assessing model efficacy, the study introduces noise into the data samples to evaluate algorithm robustness. Experimental outcomes underscore the effectiveness of the majority voting method, achieving commendable accuracy rates in detecting credit card fraud cases. The study sheds light on the pressing issue of credit card fraud, emphasizing the importance of deploying robust machine learning approaches for timely and accurate detection in real-world scenarios.

Predict and Optimize Financial Services Risk Using AI-driven Technology

With the rapid development of internet technology, many industries have embarked on a digital transformation. However, while the Internet has brought convenience to users, it has also become a breeding ground for criminals to commit fraud. On the one hand, a large number of users on the Internet more or less left data, criminals can use this information to practice accurate fraud users, improve the success rate of fraud; On the other hand, online financial transactions such as banking and e-commerce also provide more opportunities for criminals to commit fraud. Therefore, all kinds of fraud methods emerge in an endless flow, through the telephone, information, fishing and other means of fraud, not only to bring hundreds of millions of losses to society every year, but also to the security of people's lives have a huge threat. Monitoring and preventing online fraud is an important part of the cybersecurity industry. For known network fraud, based on the domain name of the phishing site, the account number and mobile phone number that send fraudulent information, simple and effective monitoring and defence can be carried out through the blacklist. However, it is difficult for traditional means to effectively defend against undocumented fraud. With the development of machine learning technology, it is the main research direction of fraud detection methods to discover the information sources and characteristics of information content through machine learning technology, and make real-time and continuous accurate judgments. This paper realises credit fraud detection by generating adversarial network technology, so as to prevent network security risks.

Financial Fraud and Socio-Economic Development in Contemporary Nigeria: Interrogating the Nexus

The study interrogated the effect of financial fraud on the socio-economic development in Nigeria from 2015 to 2022. Nigeria is characterized by high rate of financial fraud with growing concerns on its impact on the socio-economic development of Nigeria. The specific objectives of the study, therefore, are to examine the nature of financial fraud in the country from 2015-2022, determine how financial fraud affected socio-economic development in Nigeria from 2015-2022, and identify how financial fraud can be cubed in order to boost socio-economic development in the country. To achieve the objectives of this study, the following questions were posed: (i)What is the nature of financial fraud in Nigeria from 2015 to 2022? (ii),How has financial fraud affected socio-economic development in Nigeria from 2015 and 2022? and (iii) In what ways can financial fraud be curbed in order to boost socio-economic development in Nigeria? The study is anchored on the elite’ s theory propounded by Vilfredo Pareto in 1848. It adopted ex post facto research design and documentary method for data collection, while qualitative descriptive method was used for data analysis. Among other things, the study found out that financial fraud was high in Nigeria from 2015-2022. It equally found out that financial fraud adversely affected socio-economic underdevelopments in Nigeria from 2015-2022 as some measures put in place to curb financial fraud did not effectively achieve the objectives. In view of the findings, the study proffered some recommendations as solutions to the findings and they include the need to investigate all forms of financial allegations and offenders swiftly punished in accordance with

Dampak Tanggungjawab dan Pelatihan Auditor Internal terhadap Pendeteksian Kecurangan yang Dimoderasi Audit Jarak Jauh

The entities suffer material and non-material losses from fraudulent acts by employees. Indonesia is one of the countries with the highest corporate fraud rates in Asia Pacific. To minimize the occurrence of fraud, the role of internal auditors is needed. To detect fraud, internal auditors shall receive continual training. As is known, since the pandemic broke out, internal auditors have also conducted audits remotely. This study aims to examine the effect of internal auditors’ responsibility and training on fraud detection moderated by remote auditing in Indonesia. This study uses primary data. The population of this research is internal auditors from various types of institutions in Indonesia. Judgment sampling method is used. The sample consisted of 43 respondents. The research hypotheses were tested using multiple regression analysis. The results of the study prove that the responsibility and training of internal auditors have positive and significant effects on fraud detection. However, remote auditing is not proven to moderate the effects of internal auditors' responsibility and training on fraud detection. This study enriching the literature regarding the determination factors of responsibility and training of internal auditors on fraud detection. In addition, this research also proves that remote auditing in Indonesia is still relatively difficult to do, so that it can be used as an evaluation material for various entities to improve in the near future

Addressing fraudulent responses in online surveys: Insights from a web‐based participatory mapping study

Abstract Web‐based studies of human dimensions are increasing across environmental and socio‐ecological disciplines. However, the prevalence of fraud threatens research quality. Increased fraud rates should be expected as surveys move progressively more online, motivated by expanding reach, cost savings and/or in response to COVID‐19. Web‐based research must better account for fraud to maintain confidence in findings. Practical diagnostic tools and data quality protocols are required to detect fraud and ensure results quality. Drawing on our experience using an online participatory mapping case study, we discuss methods to detect potentially fraudulent responses—and identify some limitations. We begin by reviewing the current state of knowledge on fraudulent responses or ‘fraudsters’ and its relative absence in environmental and socio‐ecological disciplines. We then describe our research approach, the indicators and variables we used to detect and assess fraud and our decision‐making process to eliminate suspicious responses without jeopardizing research integrity. We found that despite several preventative measures, many fraudulent respondents could provide survey responses and effectively mimicked legitimate respondents at first glance. By assuming each response to be ‘potentially fraudulent’, we determined that the complete screening of each respondent, while time‐consuming, can limit the prevalence of fraud. We also determined that the most common data consistency checks (e.g. duration, trap questions and straight‐liner checks) are unlikely to guarantee valid respondents. If not acknowledged and addressed, fraud has the potential to undermine data integrity, discredit research findings and limit the utility of results for policy. This study contributes to environmental and socio‐ecological research by reviewing existing fraudster literature and using our experience with fraud to provide recommendations for researchers to address this problem. We encourage researchers implementing online qualitative research methods to thoroughly assess and report fraud, when possible, to ensure widespread knowledge of this growing threat. Read the free Plain Language Summary for this article on the Journal blog.

Root cause analysis of credit card fraud

Fraud detection has always been a major problem in society, especially in the banking, insurance and medical sectors. As more and more people are using credit cards to pay, the fraud rates tend to get higher. This situation is a considerable risk for the credit card company. At the same time, credit cards and their related industries have become an indispensable part of the economic construction of various countries. Therefore through descriptive analysis, regression and factor analysis, this paper analyzes the root cause of fraudulent transactions and makes simple predictions. Discusses the characteristics of cardholders and influencing factors of fraudulent transactions, The subjective psychological factors have the biggest influence on fraudulent transactions and puts forward some suggestions to prevent fraudulent transactions.

Fraud Diamond: Four Elements of Financial Report Fraud Detection – Study on Coal Producers

The aim of this research is to examine the impact of fraud diamonds on the identification of fraudulent financial statements. Fraud diamonds, a framework used to detect fraudulent financial statements, considers various factors such as pressure (measured by ROA), opportunity (measured by Ineffective monitoring (IND)), rationalization (measured by auditor turnover (AUDCHANGE)), and capability (measured by the change of directors (DIRCHANGE)). The research sample comprised of 9 companies selected through purposive sampling. Logistic regression analysis was employed as the analytical method, with rationalization and capability serving as the independent variables, assessed through a binary variable with values 0 and 1. The findings indicate that pressure significantly influences the detection of fraudulent financial statements. Specifically, higher ROA values correspond to a lower likelihood of fraudulent financial statements. Opportunity also has an impact on the detection of fraudulent financial statements, as an increase in ineffective monitoring levels leads to a higher detection rate of financial statement fraud. On the other hand, rationalization does not affect the identification of fraudulent financial statements, suggesting that auditor turnover rate does not play a role in detection. Similarly, capability, represented by the replacement of the board of directors, does not impact the detection of fraudulent financial statements in companies.

Users accused of over-confidence as deepfake fraud rates rocket

Users accused of over-confidence as deepfake fraud rates rocket

CCNN: An Artificial Intelligent based Classifier to Credit Card Fraud Detection System with Optimized Cognitive Learning Model

Nowadays digital transactions play a vital role in money transaction processes. Last 5 years statistical report portrays the growth of internet money transaction especially credit card and unified payments interface. Mean time increasing numerous banking threats and digital transaction fraud rates also growing significantly. Data engineering techniques provide ultra supports to detect credit card forgery problems in online and offline mode transactions. This credit card fraud detection (CCFD) and prevention-based data processing issues raising because of two major reasons first, classification rate of legitimate and forgery uses is frequently changing, and next one is fraud detection dataset values are vastly asymmetric. Through this research work investigating performance of various existing classifier with our proposed cognitive convolutional neural network (CCNN) classifier. Existing classifiers like Logistic Regression (LR), K-nearest neighbor (KNN), Decision Tree (DT) and Support Vector Machine (SVM). These models are facing various challenges of low performance rate and high complexity because of low hit rate and accuracy. Through this research work we introduce cognitive learning-based CCNN classifier methodology with artificial intelligence for achieve maximum accuracy rate and minimal complexity issues. For experimental data analysis uses dataset of credit card transactions attained from specific region cardholders containing 284500 transactions and its various features. Also, this dataset contains unstructured and non-dimensional data are converted into structured data with the help of over sample and under sample method. Performance analysis shows proposed CCNN classifier model provide significant improvement on accuracy, specificity, sensitivity and hit rate. The results are shown in comparison. After cross-validation, the accuracy of the CCNN classification algorithm model for transaction fraudulent detection archived 99% which using the over-sampling model.

Internal Factors Influencing Vulnerability to Telecom Fraud

With the popularity of the Internet and electronic devices, the rate of telecommunication fraud is getting higher and higher. Most of the current research focuses on the prediction of telecom fraud through machine learning. This study, on the other hand, focuses on the people who have been defrauded and finds out how people's internal factors "cognitive flexibility", "critical thinking", "material desires" and social attributes influence "vulnerability to Telecom Fraud", trying to find out which mental characteristics make people more vulnerable to telecom fraud. This study uses SPSS 25.0 software for data analysis and draws results and conclusions through the methods of questionnaire survey and hypothesis testing. It discovers both cognitive flexibility and critical thinking are negatively correlated with vulnerability to telecom fraud, meaning that people with more cognitive flexibility and critical thinking were less likely to be telecom scammed. It also finds that people with higher disposable income are also more vulnerable to wire scams. Finally, this study discusses the discovered insights and put forward constructive suggestions for scholars and related departments.

Academic Dishonesty in the Dental Faculty: Relations Among Students’ Behavior, Attitudes, and Interpretation

Background: Academic dishonesty is the most important educational concern. According to previous studies, it is more common in several groups of students. To prevent academic dishonesty, it is important to know the extent of the problem. Accordingly, this study was designed to investigate the behaviors, attitudes, and interpretations of dental students regarding exam fraud in the 2015-2016 academic years. Methods: For this purpose, a three-part questionnaire was prepared, including demographic characteristics and specific questions. The specific questions included students’ behavior, attitudes, and interpretation in the form of three scenarios. A total of 163 questionnaires were collected, and the statistical analysis was performed using SPSS, version 20. The Mann-Whitney U test and the Kruskal-Wallis test were used to analyze the data. Results: The students consisted of 90 males (55.2%) and 73 females (44.8%), and their average age was 22.72±2 years (22.3±2.87 and 23.23±2.37 years for boys and girls, respectively). The results revealed that around 65.6% of students were generally aware of the fraud problem in the faculty and knew the cheaters (63.1%). Further, 55.2% of students believed that instructors should prevent cheating during the exam. Data analyses demonstrated that there were no significant differences between boys and girls in all research variables. Finally, the average behavior proportion and attitude of the first-year students were higher than those of other students. Conclusion: Based on the findings, the rate of fraud was high in dentistry schools and possibly in other medical schools, highlighting the importance of the creating culture in changing students’ attitudes.

Fraud Detection in Credit Cards System Using ML with AWS Stage Maker

Abstract: Credit card firms must be able to recognize fraudulent credit card transactions so that clients are not charged for products that they did not purchase. Data Science may be used to solve these issues, and coupled with machine learning, it is of utmost relevance. The goal of this project is to demonstrate how to model a data set using machine learning for credit card fraud detection. The Credit Card Fraud Detection Problem entails modelling previous credit card transactions using information from those that were later determined to be fraudulent. While the globe was under lockdown and movement was confined to an absolute emergency- millions were introduced to the world of internet shopping. The simplicity of internet buying helped ecommerce platforms generate unprecedented sales. It is not surprising that during that time, the rate of online financial fraud also skyrocketed. During the COVID-19 pandemic in 2020 compared to 2019, there was a historic increase of 225 percent in online fraud cases involving credit and debit cards.

A Novel Machine learning Algorithms used to Detect Credit Card Fraud Transactions

During the Covid-19 pandemic, the world was under lockdown, and everyone was inside their home. There are so many restrictions for going out, so many companies introduced online shopping, and this online shopping helped more people; the e-commerce platform also increased their revenue; at the same time, online fraud has also risen worldwide. Everyone adopted online shopping during the pandemic. In 2019 India's 2019 credit/debit card fraud rate was 365, according to the National Crime Record Bureau. The developed countries are the highest rate of credit card fraud in 2020 compared to India; for that reason, we have to implement mechanisms that can detect credit theft. The machine learning algorithm with the R program will play an essential role in credit card fraud detection. The following machine learning algorithm will have used for credit card fraud, Random Forest, Logistic regression, Decision trees, and Gradient Boosting Classifiers. The European bank dataset used in our research and the dataset size is 284808. Here we used two classes, the first one is called the positive class (fraud transactions), and the second one is the negative class (genuine transactions). The final result will show us the outperforms of our proposed system.