This study investigates the dark side of the non-fungible token(NFT)marketplace,with a focus on understanding the risks,and underlying factors driving fraud in the NFT ecosystem.Using the fraud triangle framework,this...This study investigates the dark side of the non-fungible token(NFT)marketplace,with a focus on understanding the risks,and underlying factors driving fraud in the NFT ecosystem.Using the fraud triangle framework,this study examines pressure,opportunity,and rationalization from individual and organizational perspectives.The research provides a comprehensive understanding of the contributing factors to NFT marketplace fraud by analyzing the reasons behind fraudulent actions.A conceptual framework is developed that includes ten propositions to aid in understanding the complexity of this issue.This study’s outcomes will assist policymakers in crafting efficient approaches to mitigate fraud within the NFT marketplace.展开更多
This research aims to understand the causes of fraud through the approach of the Fraud Triangle Theory,which includes three main factors:pressure,opportunity,rationalization.By using the Systematic Literature Review m...This research aims to understand the causes of fraud through the approach of the Fraud Triangle Theory,which includes three main factors:pressure,opportunity,rationalization.By using the Systematic Literature Review method from various relevant international journals,it analyzed systematically to identify patterns,trends,and theoretical contributions to efforts in detecting and preventing fraud.The results of this study show that the three factors in the Fraud Triangle Theory significantly contribute to the occurrence of fraud.Opportunity as the most dominant factor is caused by weak internal control systems and lack of oversight.In addition,economic pressure,and a permissive organizational environment,as well as the rationalization processes by individuals,also increase the tendency for a person to commit fraud.These findings emphasize the need for a comprehensive approach in fraud prevention strategies,through ethical values,the establishment of an organizational culture with integrity,and the implementation of more effective internal control and oversight.展开更多
Reducing the risk of fraud in credit card transactions is crucial for the competitiveness of companies,especially in Latin American countries.This study aims to establish measures for preventing and detecting fraud in...Reducing the risk of fraud in credit card transactions is crucial for the competitiveness of companies,especially in Latin American countries.This study aims to establish measures for preventing and detecting fraud in the use of credit cards in shops through analytical methods(data mining,machine learning and artificial intelligence).To achieve this objective,the study employs a predictive methodology using descriptive and exploratory statistics and frequency,frequency&monetary(RFM)classification techniques,differentiating between SMEs and large businesses via cluster analysis and supervised models.A dataset of 221,292 card records from a Latin American merchant payment gateway for the year 2022 is used.For fraud alerts,the classification model has been selected for small and medium–sized merchants,and the multilayer perceptron(MLP)neural network has been selected for large merchants.Random forest or Gini decision tree models have been selected as backup models for retraining.For the detection of punctual fraud patterns,the K-means and partitioning around medoids(PAM)models have been selected,depending on the type of trade.The results revealed that the application of the identified models would have prevented between 48 and 85%of fraud transactions,depending on the trade size.Despite the promising results,continuous updating is recommended,as fraudsters frequently implement new fraud techniques.展开更多
This paper proposes a novel hybrid fraud detection framework that integrates multi-stage feature selection,unsupervised clustering,and ensemble learning to improve classification performance in financial transaction m...This paper proposes a novel hybrid fraud detection framework that integrates multi-stage feature selection,unsupervised clustering,and ensemble learning to improve classification performance in financial transaction monitoring systems.The framework is structured into three core layers:(1)feature selection using Recursive Feature Elimination(RFE),Principal Component Analysis(PCA),and Mutual Information(MI)to reduce dimensionality and enhance input relevance;(2)anomaly detection through unsupervised clustering using K-Means,Density-Based Spatial Clustering(DBSCAN),and Hierarchical Clustering to flag suspicious patterns in unlabeled data;and(3)final classification using a voting-based hybrid ensemble of Support Vector Machine(SVM),Random Forest(RF),and Gradient Boosting Classifier(GBC).The experimental evaluation is conducted on a synthetically generated dataset comprising one million financial transactions,with 5% labelled as fraudulent,simulating realistic fraud rates and behavioural features,including transaction time,origin,amount,and geo-location.The proposed model demonstrated a significant improvement over baseline classifiers,achieving an accuracy of 99%,a precision of 99%,a recall of 97%,and an F1-score of 99%.Compared to individual models,it yielded a 9% gain in overall detection accuracy.It reduced the false positive rate to below 3.5%,thereby minimising the operational costs associated with manually reviewing false alerts.The model’s interpretability is enhanced by the integration of Shapley Additive Explanations(SHAP)values for feature importance,supporting transparency and regulatory auditability.These results affirm the practical relevance of the proposed system for deployment in real-time fraud detection scenarios such as credit card transactions,mobile banking,and cross-border payments.The study also highlights future directions,including the deployment of lightweight models and the integration of multimodal data for scalable fraud analytics.展开更多
This study explores ethical violations and audit failures in various large-scale corporate fraud cases.Using a qualitative descriptive method and based on secondary data from documented fraud cases and published audit...This study explores ethical violations and audit failures in various large-scale corporate fraud cases.Using a qualitative descriptive method and based on secondary data from documented fraud cases and published audit reports,the study applies the Fraud Triangle framework,focusing on how weak integrity,objectivity,and professional competence have undermined public trust in the auditing profession.Using a qualitative descriptive method and the Fraud Triangle framework,which includes pressure,opportunity,and rationalization,the study analyzes cases from Indonesia(SNP Finance,Jiwasraya),China(Evergrande),and Germany(Wirecard).The analysis reveals that many audit failures observed in this study appear to stem more from ethical challenges than from technical incapability,driven by client pressure,weak internal controls,and compromised auditor independence.These cases demonstrate a recurring global pattern in which auditors neglect their responsibility to act in the public interest.展开更多
This study explores ethical violations and audit failures in various large-scale corporate fraud cases.Using a qualitative descriptive method and based on secondary data from documented fraud cases and published audit...This study explores ethical violations and audit failures in various large-scale corporate fraud cases.Using a qualitative descriptive method and based on secondary data from documented fraud cases and published audit reports,the study applies the Fraud Triangle framework,focusing on how weak integrity,objectivity,and professional competence have undermined public trust in the auditing profession.Using a qualitative descriptive method and the Fraud Triangle framework,which includes pressure,opportunity,and rationalization,the study analyzes cases from Indonesia(SNP Finance,Jiwasraya),China(Evergrande),and Germany(Wirecard).The analysis reveals that many audit failures observed in this study appear to stem more from ethical challenges than from technical incapability,driven by client pressure,weak internal controls,and compromised auditor independence.These cases demonstrate a recurring global pattern in which auditors neglect their responsibility to act in the public interest.展开更多
Credit card fraud is one of the primary sources of operational risk in banks,and accurate prediction of fraudulent credit card transactions is essential to minimize banks’economic losses.Two key issues are faced in c...Credit card fraud is one of the primary sources of operational risk in banks,and accurate prediction of fraudulent credit card transactions is essential to minimize banks’economic losses.Two key issues are faced in credit card fraud detection research,i.e.,data category imbalance and data drift.However,the oversampling algorithm used in current research suffers from excessive noise,and the Long Short-Term Memory Network(LSTM)based temporal model suffers from gradient dispersion,which can lead to loss of model performance.To address the above problems,a credit card fraud detection method based on Random Forest-Wasserstein Generative Adversarial NetworkTemporal Convolutional Network(RF-WGAN-TCN)is proposed.First,the credit card data is preprocessed,the feature importance scores are calculated by Random Forest(RF),the features with lower importance are eliminated,and then the remaining features are standardized.Second,the Wasserstein Distance Improvement Generative Adversarial Network(GAN)is introduced to construct the Wasserstein Generative Adversarial Network(WGAN),the preprocessed data is input into the WGAN,and under the mutual game training of generator and discriminator,the fraud samples that meet the target distribution are obtained.Finally,the temporal convolutional network(TCN)is utilized to extract the long-time dependencies,and the classification results are output through the Softmax layer.Experimental results on the European cardholder dataset show that the method proposed in the paper achieves 91.96%,98.22%,and 81.95%in F1-Score,Area Under Curve(AUC),and Area Under the Precision-Recall Curve(AUPRC)metrics,respectively,and has higher prediction accuracy and classification performance compared with existing mainstream methods.展开更多
Purpose:This study aims to integrate large language models(LLMs)with interpretable machine learning methods to develop a multimodal data-driven framework for predicting corporate financial fraud,addressing the limitat...Purpose:This study aims to integrate large language models(LLMs)with interpretable machine learning methods to develop a multimodal data-driven framework for predicting corporate financial fraud,addressing the limitations of traditional approaches in long-text semantic parsing,model interpretability,and multisource data fusion,thereby providing regulatory agencies with intelligent auditing tools.Design/methodology/approach:Analyzing 5,304 Chinese listed firms’annual reports(2015-2020)from the CSMAD database,this study leverages the Doubao LLMs to generate chunked summaries and 256-dimensional semantic vectors,developing textual semantic features.It integrates 19 financial indicators,11 governance metrics,and linguistic characteristics(tone,readability)with fraud prediction models optimized through a group of Gradient Boosted Decision Tree(GBDT)algorithms.SHAP value analysis in the final model reveals the risk transmission mechanism by quantifying the marginal impacts of financial,governance,and textual features on fraud likelihood.Findings:The study found that LLMs effectively distill lengthy annual reports into semantic summaries,while GBDT algorithms(AUC>0.850)outperform the traditional Logistic Regression model in fraud detection.Multimodal fusion improved performance by 7.4%,with financial,governance,and textual features providing complementary signals.SHAP analysis revealed financial distress,governance conflicts,and narrative patterns(e.g.,tone anchoring,semantic thresholds)as key fraud indicators,highlighting managerial intent in report language.Research limitations:This study identifies three key limitations:1)lack of interpretability for semantic features,2)absence of granular fraud-type differentiation,and 3)unexplored comparative validation with other deep learning methods.Future research will address these gaps to enhance fraud detection precision and model transparency.Practical implications:The developed semantic-enhanced evaluation model provides a quantitative tool for assessing listed companies’information disclosure quality and enables practical implementation through its derivative real-time monitoring system.This advancement significantly strengthens capital market risk early warning capabilities,offering actionable insights for securities regulation.Originality/value:This study presents three key innovations:1)A novel“chunking-summarizationembedding”framework for efficient semantic compression of lengthy annual reports(30,000 words);2)Demonstration of LLMs’superior performance in financial text analysis,outperforming traditional methods by 19.3%;3)A novel“language-psychology-behavior”triad model for analyzing managerial fraud motives.展开更多
Discovering and preventing the frauds which affect the business organizations negatively require a greater degree of specialism. Detecting a fraud in the organizations is very hard, because not only such a fraud is ex...Discovering and preventing the frauds which affect the business organizations negatively require a greater degree of specialism. Detecting a fraud in the organizations is very hard, because not only such a fraud is exercised by the people who have deep professional knowledge, but they also use some peculiar methods to hide their tricky activities. Therefore, it is obvious that it is necessary to have the fraud examiners and especially fraud auditors who should have deep professional knowledge and experience. The aim of this study is to give some general information about employee fraud, which targets the different functions of the companies, takes many forms, and reaches important levels in recent years, in qualitative point. In this study, firstly, forensic accounting is a highly dynamic area in nowadays which is related to fraud auditing and its profession, and its search area of frauds and employee frauds subjects have been reviewed. Finally, qualitative data were collected about fraud incidents which had occurred and been sent to the court in the province of Kars in Turkey. Actual case analysis method has been used in this study. The obtained data have been analyzed by using Statistical Package for the Social Sciences （SPSS） 17 statistics package program. Results of the study have been discussed and interpreted in details.展开更多
Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to cr...Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD,which significantly impact classification models’performance.To address these issues,this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks(MFGAN).The MFGAN model consists of two modules:a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space,and a balance module based on the generative adversarial network to decrease the class imbalance ratio.The effectiveness of theMFGAN model is validated on two actual credit card datasets.The impacts of different class balance ratios on the performance of the four resamplingmodels are analyzed,and the contribution of the two different modules to the performance of the MFGAN model is investigated via ablation experiments.Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of recall,F1,and Area Under the Curve(AUC)metrics,which means that the MFGAN model can help banks find more fraudulent transactions and reduce fraud losses.展开更多
Background:The reputation system has been designed as an effective mechanism to reduce risks associated with online shopping for customers.However,it is vulnerable to rating fraud.Some raters may inject unfairly high ...Background:The reputation system has been designed as an effective mechanism to reduce risks associated with online shopping for customers.However,it is vulnerable to rating fraud.Some raters may inject unfairly high or low ratings to the system so as to promote their own products or demote their competitors.Method:This study explores the rating fraud by differentiating the subjective fraud from objective fraud.Then it discusses the effectiveness of blockchain technology in objective fraud and its limitation in subjective fraud,especially the rating fraud.Lastly,it systematically analyzes the robustness of blockchain-based reputation systems in each type of rating fraud.Results:The detection of fraudulent raters is not easy since they can behave strategically to camouflage themselves.We explore the potential strengths and limitations of blockchain-based reputation systems under two attack goals:ballot-stuffing and bad-mouthing,and various attack models including constant attack,camouflage attack,whitewashing attack and sybil attack.Blockchain-based reputation systems are more robust against bad-mouthing than ballot-stuffing fraud.Conclusions:Blockchain technology provides new opportunities for redesigning the reputation system.Blockchain systems are very effective in preventing objective information fraud,such as loan application fraud,where fraudulent information is fact-based.However,their effectiveness is limited in subjective information fraud,such as rating fraud,where the ground-truth is not easily validated.Blockchain systems are effective in preventing bad mouthing and whitewashing attack,but they are limited in detecting ballot-stuffing under sybil attack,constant attacks and camouflage attack.展开更多
The fraudulent website image is a vital information carrier for telecom fraud.The efficient and precise recognition of fraudulent website images is critical to combating and dealing with fraudulent websites.Current re...The fraudulent website image is a vital information carrier for telecom fraud.The efficient and precise recognition of fraudulent website images is critical to combating and dealing with fraudulent websites.Current research on image recognition of fraudulent websites is mainly carried out at the level of image feature extraction and similarity study,which have such disadvantages as difficulty in obtaining image data,insufficient image analysis,and single identification types.This study develops a model based on the entropy method for image leader decision and Inception-v3 transfer learning to address these disadvantages.The data processing part of the model uses a breadth search crawler to capture the image data.Then,the information in the images is evaluated with the entropy method,image weights are assigned,and the image leader is selected.In model training and prediction,the transfer learning of the Inception-v3 model is introduced into image recognition of fraudulent websites.Using selected image leaders to train the model,multiple types of fraudulent websites are identified with high accuracy.The experiment proves that this model has a superior accuracy in recognizing images on fraudulent websites compared to other current models.展开更多
The feature analysis of fraudulent websites is of great significance to the combat,prevention and control of telecom fraud crimes.Aiming to address the shortcomings of existing analytical approaches,i.e.single dimensi...The feature analysis of fraudulent websites is of great significance to the combat,prevention and control of telecom fraud crimes.Aiming to address the shortcomings of existing analytical approaches,i.e.single dimension and venerability to anti-reconnaissance,this paper adopts the Stacking,the ensemble learning algorithm,combines multiple modalities such as text,image and URL,and proposes a multimodal fraudulent website identification method by ensembling heterogeneous models.Crossvalidation is first used in the training of multiple largely different base classifiers that are strong in learning,such as BERT model,residual neural network(ResNet)and logistic regression model.Classification of the text,image and URL features are then performed respectively.The results of the base classifiers are taken as the input of the meta-classifier,and the output of which is eventually used as the final identification.The study indicates that the fusion method is more effective in identifying fraudulent websites than the single-modal method,and the recall is increased by at least 1%.In addition,the deployment of the algorithm to the real Internet environment shows the improvement of the identification accuracy by at least 1.9%compared with other fusion methods.展开更多
Currently,telecom fraud is expanding from the traditional telephone network to the Internet,and identifying fraudulent IPs is of great significance for reducing Internet telecom fraud and protecting consumer rights.Ho...Currently,telecom fraud is expanding from the traditional telephone network to the Internet,and identifying fraudulent IPs is of great significance for reducing Internet telecom fraud and protecting consumer rights.However,existing telecom fraud identification methods based on blacklists,reputation,content and behavioral characteristics have good identification performance in the telephone network,but it is difficult to apply to the Internet where IP(Internet Protocol)addresses change dynamically.To address this issue,we propose a fraudulent IP identification method based on homology detection and DBSCAN(Density-Based Spatial Clustering of Applications with Noise)clustering(DC-FIPD).First,we analyze the aggregation of fraudulent IP geographies and the homology of IP addresses.Next,the collected fraudulent IPs are clustered geographically to obtain the regional distribution of fraudulent IPs.Then,we constructed the fraudulent IP feature set,used the genetic optimization algorithm to determine the weights of the fraudulent IP features,and designed the calculation method of the IP risk value to give the risk value threshold of the fraudulent IP.Finally,the risk value of the target IP is calculated and the IP is identified based on the risk value threshold.Experimental results on a real-world telecom fraud detection dataset show that the DC-FIPD method achieves an average identification accuracy of 86.64%for fraudulent IPs.Additionally,the method records a precision of 86.08%,a recall of 45.24%,and an F1-score of 59.31%,offering a comprehensive evaluation of its performance in fraud detection.These results highlight the DC-FIPD method’s effectiveness in addressing the challenges of fraudulent IP identification.展开更多
Understanding the root causes of fraud and learning about the most effective fraud prevention mechanisms are critical in reducing the incidence of financial fraud. Therefore, this study solicits the views of fraud inv...Understanding the root causes of fraud and learning about the most effective fraud prevention mechanisms are critical in reducing the incidence of financial fraud. Therefore, this study solicits the views of fraud investigators on the existence and effectiveness of fraud prevention mechanisms within Government-Linked Companies （GLCs）. Specifically, effective fraud prevention mechanisms （FPM） should be undertaken by five critical groups, namely the board of directors, audit committees, external auditors, internal auditors, and anti-fraud specialists. A total of 147 questionnaires were distributed to fraud investigators of all public listed GLCs in Malaysia. Out of those, a total of 45 usable responses were received, which represents an effective response rate of 30.6%. In terms of existence, the respondents ranked ＂management review of internal controls＂ and ＂external audits of financial statements＂ as the top-most FPMs. This was followed by other mechanisms such as operational audits, internal audits and internal control review/improvements by departments. Out of the 27 fraud prevention mechanisms, fraud investigators perceiyed surprise audits, fraud hotline, fraud prevention program and training, anti-fraud policy, fraud vulnerability reviews, operational audits, whistle-blowing policy, internal audit or fraud examination department, and, imposing penalty and disciplinary action as more effective than the others. Overall, this study provides important insights to practitioners and organizations in identifying fraud prevention mechanisms that are most effective.展开更多
This work is concerned with GSM subscription fraud detection system using some network techniques. Fraud is a hitch around the globe with huge loss of income. Fraud has an effect on the credibility and performance of ...This work is concerned with GSM subscription fraud detection system using some network techniques. Fraud is a hitch around the globe with huge loss of income. Fraud has an effect on the credibility and performance of telecommunication companies. The most difficult problem that faces the industry is the fact that fraud is dynamic, which means that whenever fraudsters feel that they will be detected, they devise other ways to circumvent security measures. In such cases, the perpetrators’ intention is to completely avoid or at least reduce the charges for using the services. Subscription fraud is one of the major types of telecommunication fraud in which a customer obtains an account without intention to pay the bill. Thus at the level of a phone number, all transactions from the number will be fraudulent. In such cases abnormal usage occurs throughout the active period of the account;which is usually used for call selling or intensive self usage. This provides a means for illegal high profit business for fraudsters requiring minimal investment and relatively low risk of getting caught. A system to prevent subscription fraud in GSM telecommunications with high impact on long distance carriers is proposed to detect fraud. The system employs adaptive flexible techniques using advanced data analysis like Artificial Neural Networks (ANN). This study aims at developing a fraud detection model occurrence in GSM Network. The paper also gives analysis of the fraud detection Systems, fraud detection and prevention, fraud prevention methods etc. Fraud affects us all and is of particular concern to those who manage large government and business organisations where the potential losses are greatest. The operation of a mobile network is complex, and fraudsters invest a lot of energy to find and exploit every weakness of the system. A typical example would be subscription fraud, where a fraudster acquires a subscription to the mobile network under a false identity;and start reselling the use of his phone to unscrupulous customers (typically for international calls to distant foreign countries) at rate less than the regular tariff.展开更多
With the increased accessibility of global trade information,transaction fraud has become a major worry in global banking and commerce security.The incidence and magnitude of transaction fraud are increasing daily,res...With the increased accessibility of global trade information,transaction fraud has become a major worry in global banking and commerce security.The incidence and magnitude of transaction fraud are increasing daily,resulting in significant financial losses for both customers and financial professionals.With improvements in data mining and machine learning in computer science,the capacity to detect transaction fraud is becoming increasingly attainable.The primary goal of this research is to undertake a comparative examination of cutting-edge machine-learning algorithms developed to detect credit card fraud.The research looks at the efficacy of these machine learning algorithms using a publicly available dataset of credit card transactions performed by European cardholders in 2023,comprising around 550,000 records.The study uses this dataset to assess the performance of well-established machine learning models,measuring their accuracy,recall,and F1 score.In addition,the study includes a confusion matrix for all models to aid in evaluation and training time duration.Machin learning models,including Logistic regression,random forest,extra trees,and LGBM,achieve high accuracy and precision in the credit card fraud detection dataset,with a reported accuracy,recall,and F1 score of 1.00 for both classes.展开更多
The 2011 Global Economic Crime Survey instituted by PricewaterhouseCoopers （PwC） confirms the economic crime in Malaysia to be on the increase and, therefore, requires immediate attention to stem the tides. In antic...The 2011 Global Economic Crime Survey instituted by PricewaterhouseCoopers （PwC） confirms the economic crime in Malaysia to be on the increase and, therefore, requires immediate attention to stem the tides. In anticipation of the challenges occasioned due to a shift from the modified cash basis to the accrual basis of accounting, the Malaysian State＇s determination to move from a developing nation to a developed nation, and to be ranked among the first 10 in 2020, this paper presents the need for forensic accountant and auditor capability （i.e., mindset and skills） on forensic accountant and auditor competence （i.e., task performance fraud risk assessment （TPFRA）） in the Malaysian public sector. It also draws the attention of the users of public sector accountants and auditors to the understanding of fraud mechanisms and how to deal with fraudsters. The population of this study comprised the accountants and auditors in the office of the Accountant General and Auditor General of Malaysia. The objective of this paper is to investigate the competence requirements of accountants and auditors in the effective and efficient utilization of capability requirements, which have the potentials to usher in the best global practices in fighting fraud in the Malaysian public sector.展开更多
Auditing standards require external auditors to provide reasonable assurance that the financial statements are free from material misstatements, either due to fraud or error. Inability of the external auditors to dete...Auditing standards require external auditors to provide reasonable assurance that the financial statements are free from material misstatements, either due to fraud or error. Inability of the external auditors to detect the material misstatements, particularly fraud, may expose them to litigation. The present study aims to examine the effect of personality factors （i.e., neuroticism, extraversion, conscientiousness, openness to experience and agreeableness） on the external auditors＇ ability to detect the likelihood of fraud. An experimental approach is adopted by sending case materials to audit partners and audit managers attached to auditing firms operating in Malaysia. The result shows that personality does not have a positive effect on the external auditors＇ ability to detect the likelihood of fraud.展开更多
With increasingly rampant telephone fraud activities,the social impact and economic losses caused to China have increased dramatically.Precise,convenient,and efficient fraudulent phone call recognition has become a ch...With increasingly rampant telephone fraud activities,the social impact and economic losses caused to China have increased dramatically.Precise,convenient,and efficient fraudulent phone call recognition has become a challenge since telephone fraud became more varied and covert.To deal with this problem,many researchers have extracted some statistical features of telephone fraud behavior and proposed some machine learning algorithms on the field of fraudulent phone call recognition.In this paper,the calling detail records are utilized to construct a classifier for fraudulent phone call recognition.Meantime,a deep learning approach based on convolutional neural network(CNN)is proposed for better features learning and compared with the existing state-of-the-art machine learning algorithms.It learns phone number and call behavior features of telephone fraud,and improves the accuracy of classification.The evaluation results show that the proposed algorithm outperforms competitive algorithms.展开更多
文摘This study investigates the dark side of the non-fungible token(NFT)marketplace,with a focus on understanding the risks,and underlying factors driving fraud in the NFT ecosystem.Using the fraud triangle framework,this study examines pressure,opportunity,and rationalization from individual and organizational perspectives.The research provides a comprehensive understanding of the contributing factors to NFT marketplace fraud by analyzing the reasons behind fraudulent actions.A conceptual framework is developed that includes ten propositions to aid in understanding the complexity of this issue.This study’s outcomes will assist policymakers in crafting efficient approaches to mitigate fraud within the NFT marketplace.
文摘This research aims to understand the causes of fraud through the approach of the Fraud Triangle Theory,which includes three main factors:pressure,opportunity,rationalization.By using the Systematic Literature Review method from various relevant international journals,it analyzed systematically to identify patterns,trends,and theoretical contributions to efforts in detecting and preventing fraud.The results of this study show that the three factors in the Fraud Triangle Theory significantly contribute to the occurrence of fraud.Opportunity as the most dominant factor is caused by weak internal control systems and lack of oversight.In addition,economic pressure,and a permissive organizational environment,as well as the rationalization processes by individuals,also increase the tendency for a person to commit fraud.These findings emphasize the need for a comprehensive approach in fraud prevention strategies,through ethical values,the establishment of an organizational culture with integrity,and the implementation of more effective internal control and oversight.
基金supported by project Finance for all(F4A),funded by the"Institución Gran Duque de Alba"and"Diputación provincial deávila"under the grant 3364/2022.
文摘Reducing the risk of fraud in credit card transactions is crucial for the competitiveness of companies,especially in Latin American countries.This study aims to establish measures for preventing and detecting fraud in the use of credit cards in shops through analytical methods(data mining,machine learning and artificial intelligence).To achieve this objective,the study employs a predictive methodology using descriptive and exploratory statistics and frequency,frequency&monetary(RFM)classification techniques,differentiating between SMEs and large businesses via cluster analysis and supervised models.A dataset of 221,292 card records from a Latin American merchant payment gateway for the year 2022 is used.For fraud alerts,the classification model has been selected for small and medium–sized merchants,and the multilayer perceptron(MLP)neural network has been selected for large merchants.Random forest or Gini decision tree models have been selected as backup models for retraining.For the detection of punctual fraud patterns,the K-means and partitioning around medoids(PAM)models have been selected,depending on the type of trade.The results revealed that the application of the identified models would have prevented between 48 and 85%of fraud transactions,depending on the trade size.Despite the promising results,continuous updating is recommended,as fraudsters frequently implement new fraud techniques.
基金funded by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia[Grant No.KFU241683].
文摘This paper proposes a novel hybrid fraud detection framework that integrates multi-stage feature selection,unsupervised clustering,and ensemble learning to improve classification performance in financial transaction monitoring systems.The framework is structured into three core layers:(1)feature selection using Recursive Feature Elimination(RFE),Principal Component Analysis(PCA),and Mutual Information(MI)to reduce dimensionality and enhance input relevance;(2)anomaly detection through unsupervised clustering using K-Means,Density-Based Spatial Clustering(DBSCAN),and Hierarchical Clustering to flag suspicious patterns in unlabeled data;and(3)final classification using a voting-based hybrid ensemble of Support Vector Machine(SVM),Random Forest(RF),and Gradient Boosting Classifier(GBC).The experimental evaluation is conducted on a synthetically generated dataset comprising one million financial transactions,with 5% labelled as fraudulent,simulating realistic fraud rates and behavioural features,including transaction time,origin,amount,and geo-location.The proposed model demonstrated a significant improvement over baseline classifiers,achieving an accuracy of 99%,a precision of 99%,a recall of 97%,and an F1-score of 99%.Compared to individual models,it yielded a 9% gain in overall detection accuracy.It reduced the false positive rate to below 3.5%,thereby minimising the operational costs associated with manually reviewing false alerts.The model’s interpretability is enhanced by the integration of Shapley Additive Explanations(SHAP)values for feature importance,supporting transparency and regulatory auditability.These results affirm the practical relevance of the proposed system for deployment in real-time fraud detection scenarios such as credit card transactions,mobile banking,and cross-border payments.The study also highlights future directions,including the deployment of lightweight models and the integration of multimodal data for scalable fraud analytics.
文摘This study explores ethical violations and audit failures in various large-scale corporate fraud cases.Using a qualitative descriptive method and based on secondary data from documented fraud cases and published audit reports,the study applies the Fraud Triangle framework,focusing on how weak integrity,objectivity,and professional competence have undermined public trust in the auditing profession.Using a qualitative descriptive method and the Fraud Triangle framework,which includes pressure,opportunity,and rationalization,the study analyzes cases from Indonesia(SNP Finance,Jiwasraya),China(Evergrande),and Germany(Wirecard).The analysis reveals that many audit failures observed in this study appear to stem more from ethical challenges than from technical incapability,driven by client pressure,weak internal controls,and compromised auditor independence.These cases demonstrate a recurring global pattern in which auditors neglect their responsibility to act in the public interest.
文摘This study explores ethical violations and audit failures in various large-scale corporate fraud cases.Using a qualitative descriptive method and based on secondary data from documented fraud cases and published audit reports,the study applies the Fraud Triangle framework,focusing on how weak integrity,objectivity,and professional competence have undermined public trust in the auditing profession.Using a qualitative descriptive method and the Fraud Triangle framework,which includes pressure,opportunity,and rationalization,the study analyzes cases from Indonesia(SNP Finance,Jiwasraya),China(Evergrande),and Germany(Wirecard).The analysis reveals that many audit failures observed in this study appear to stem more from ethical challenges than from technical incapability,driven by client pressure,weak internal controls,and compromised auditor independence.These cases demonstrate a recurring global pattern in which auditors neglect their responsibility to act in the public interest.
基金supported by the National Natural Science Foundation of China under Grant No.62466001the Talent Plan Project of Fuzhou City of Jiangxi Province of China under the Grant No.2021ED008+1 种基金the Opening Project of Jiangxi Key Laboratory of Cybersecurity Intelligent Perception under the Grant No.JKLCIP202202the Priority Unveiled Marshalling Project of Fuzhou City of Jiangxi Province of China under the Grant No.2023JBB026.
文摘Credit card fraud is one of the primary sources of operational risk in banks,and accurate prediction of fraudulent credit card transactions is essential to minimize banks’economic losses.Two key issues are faced in credit card fraud detection research,i.e.,data category imbalance and data drift.However,the oversampling algorithm used in current research suffers from excessive noise,and the Long Short-Term Memory Network(LSTM)based temporal model suffers from gradient dispersion,which can lead to loss of model performance.To address the above problems,a credit card fraud detection method based on Random Forest-Wasserstein Generative Adversarial NetworkTemporal Convolutional Network(RF-WGAN-TCN)is proposed.First,the credit card data is preprocessed,the feature importance scores are calculated by Random Forest(RF),the features with lower importance are eliminated,and then the remaining features are standardized.Second,the Wasserstein Distance Improvement Generative Adversarial Network(GAN)is introduced to construct the Wasserstein Generative Adversarial Network(WGAN),the preprocessed data is input into the WGAN,and under the mutual game training of generator and discriminator,the fraud samples that meet the target distribution are obtained.Finally,the temporal convolutional network(TCN)is utilized to extract the long-time dependencies,and the classification results are output through the Softmax layer.Experimental results on the European cardholder dataset show that the method proposed in the paper achieves 91.96%,98.22%,and 81.95%in F1-Score,Area Under Curve(AUC),and Area Under the Precision-Recall Curve(AUPRC)metrics,respectively,and has higher prediction accuracy and classification performance compared with existing mainstream methods.
基金supported by the 2021 Guangdong Province(China)Science and Technology Plan Project“Research and Application of Key Technologies for Multi-level Knowledge Retrieval Based on Big Data Intelligence”(Project No.2021B0101420004)the 2022 commissioned project“Cross-border E-commerce Taxation and Related Research”from the State Taxation Administration Guangdong Provincial Taxation Bureau,China.
文摘Purpose:This study aims to integrate large language models(LLMs)with interpretable machine learning methods to develop a multimodal data-driven framework for predicting corporate financial fraud,addressing the limitations of traditional approaches in long-text semantic parsing,model interpretability,and multisource data fusion,thereby providing regulatory agencies with intelligent auditing tools.Design/methodology/approach:Analyzing 5,304 Chinese listed firms’annual reports(2015-2020)from the CSMAD database,this study leverages the Doubao LLMs to generate chunked summaries and 256-dimensional semantic vectors,developing textual semantic features.It integrates 19 financial indicators,11 governance metrics,and linguistic characteristics(tone,readability)with fraud prediction models optimized through a group of Gradient Boosted Decision Tree(GBDT)algorithms.SHAP value analysis in the final model reveals the risk transmission mechanism by quantifying the marginal impacts of financial,governance,and textual features on fraud likelihood.Findings:The study found that LLMs effectively distill lengthy annual reports into semantic summaries,while GBDT algorithms(AUC>0.850)outperform the traditional Logistic Regression model in fraud detection.Multimodal fusion improved performance by 7.4%,with financial,governance,and textual features providing complementary signals.SHAP analysis revealed financial distress,governance conflicts,and narrative patterns(e.g.,tone anchoring,semantic thresholds)as key fraud indicators,highlighting managerial intent in report language.Research limitations:This study identifies three key limitations:1)lack of interpretability for semantic features,2)absence of granular fraud-type differentiation,and 3)unexplored comparative validation with other deep learning methods.Future research will address these gaps to enhance fraud detection precision and model transparency.Practical implications:The developed semantic-enhanced evaluation model provides a quantitative tool for assessing listed companies’information disclosure quality and enables practical implementation through its derivative real-time monitoring system.This advancement significantly strengthens capital market risk early warning capabilities,offering actionable insights for securities regulation.Originality/value:This study presents three key innovations:1)A novel“chunking-summarizationembedding”framework for efficient semantic compression of lengthy annual reports(30,000 words);2)Demonstration of LLMs’superior performance in financial text analysis,outperforming traditional methods by 19.3%;3)A novel“language-psychology-behavior”triad model for analyzing managerial fraud motives.
文摘Discovering and preventing the frauds which affect the business organizations negatively require a greater degree of specialism. Detecting a fraud in the organizations is very hard, because not only such a fraud is exercised by the people who have deep professional knowledge, but they also use some peculiar methods to hide their tricky activities. Therefore, it is obvious that it is necessary to have the fraud examiners and especially fraud auditors who should have deep professional knowledge and experience. The aim of this study is to give some general information about employee fraud, which targets the different functions of the companies, takes many forms, and reaches important levels in recent years, in qualitative point. In this study, firstly, forensic accounting is a highly dynamic area in nowadays which is related to fraud auditing and its profession, and its search area of frauds and employee frauds subjects have been reviewed. Finally, qualitative data were collected about fraud incidents which had occurred and been sent to the court in the province of Kars in Turkey. Actual case analysis method has been used in this study. The obtained data have been analyzed by using Statistical Package for the Social Sciences （SPSS） 17 statistics package program. Results of the study have been discussed and interpreted in details.
基金supported by the National Key R&D Program of China(Nos.2022YFB3104103,and 2019QY1406)the National Natural Science Foundation of China(Nos.61732022,61732004,61672020,and 62072131).
文摘Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD,which significantly impact classification models’performance.To address these issues,this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks(MFGAN).The MFGAN model consists of two modules:a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space,and a balance module based on the generative adversarial network to decrease the class imbalance ratio.The effectiveness of theMFGAN model is validated on two actual credit card datasets.The impacts of different class balance ratios on the performance of the four resamplingmodels are analyzed,and the contribution of the two different modules to the performance of the MFGAN model is investigated via ablation experiments.Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of recall,F1,and Area Under the Curve(AUC)metrics,which means that the MFGAN model can help banks find more fraudulent transactions and reduce fraud losses.
文摘Background:The reputation system has been designed as an effective mechanism to reduce risks associated with online shopping for customers.However,it is vulnerable to rating fraud.Some raters may inject unfairly high or low ratings to the system so as to promote their own products or demote their competitors.Method:This study explores the rating fraud by differentiating the subjective fraud from objective fraud.Then it discusses the effectiveness of blockchain technology in objective fraud and its limitation in subjective fraud,especially the rating fraud.Lastly,it systematically analyzes the robustness of blockchain-based reputation systems in each type of rating fraud.Results:The detection of fraudulent raters is not easy since they can behave strategically to camouflage themselves.We explore the potential strengths and limitations of blockchain-based reputation systems under two attack goals:ballot-stuffing and bad-mouthing,and various attack models including constant attack,camouflage attack,whitewashing attack and sybil attack.Blockchain-based reputation systems are more robust against bad-mouthing than ballot-stuffing fraud.Conclusions:Blockchain technology provides new opportunities for redesigning the reputation system.Blockchain systems are very effective in preventing objective information fraud,such as loan application fraud,where fraudulent information is fact-based.However,their effectiveness is limited in subjective information fraud,such as rating fraud,where the ground-truth is not easily validated.Blockchain systems are effective in preventing bad mouthing and whitewashing attack,but they are limited in detecting ballot-stuffing under sybil attack,constant attacks and camouflage attack.
基金supported by the National Social Science Fund of China(23BGL272)。
文摘The fraudulent website image is a vital information carrier for telecom fraud.The efficient and precise recognition of fraudulent website images is critical to combating and dealing with fraudulent websites.Current research on image recognition of fraudulent websites is mainly carried out at the level of image feature extraction and similarity study,which have such disadvantages as difficulty in obtaining image data,insufficient image analysis,and single identification types.This study develops a model based on the entropy method for image leader decision and Inception-v3 transfer learning to address these disadvantages.The data processing part of the model uses a breadth search crawler to capture the image data.Then,the information in the images is evaluated with the entropy method,image weights are assigned,and the image leader is selected.In model training and prediction,the transfer learning of the Inception-v3 model is introduced into image recognition of fraudulent websites.Using selected image leaders to train the model,multiple types of fraudulent websites are identified with high accuracy.The experiment proves that this model has a superior accuracy in recognizing images on fraudulent websites compared to other current models.
基金supported by Zhejiang Provincial Natural Science Foundation of China(Grant No.LGF20G030001)Ministry of Public Security Science and Technology Plan Project(2022LL16)Key scientific research projects of agricultural and social development in Hangzhou in 2020(202004A06).
文摘The feature analysis of fraudulent websites is of great significance to the combat,prevention and control of telecom fraud crimes.Aiming to address the shortcomings of existing analytical approaches,i.e.single dimension and venerability to anti-reconnaissance,this paper adopts the Stacking,the ensemble learning algorithm,combines multiple modalities such as text,image and URL,and proposes a multimodal fraudulent website identification method by ensembling heterogeneous models.Crossvalidation is first used in the training of multiple largely different base classifiers that are strong in learning,such as BERT model,residual neural network(ResNet)and logistic regression model.Classification of the text,image and URL features are then performed respectively.The results of the base classifiers are taken as the input of the meta-classifier,and the output of which is eventually used as the final identification.The study indicates that the fusion method is more effective in identifying fraudulent websites than the single-modal method,and the recall is increased by at least 1%.In addition,the deployment of the algorithm to the real Internet environment shows the improvement of the identification accuracy by at least 1.9%compared with other fusion methods.
基金funded by the National Natural Science Foundation of China under Grant No.62002103Henan Province Science Foundation for Youths No.222300420058+1 种基金Henan Province Science and Technology Research Project No.232102321064Teacher Education Curriculum Reform Research Priority Project No.2023-JSJYZD-011.
文摘Currently,telecom fraud is expanding from the traditional telephone network to the Internet,and identifying fraudulent IPs is of great significance for reducing Internet telecom fraud and protecting consumer rights.However,existing telecom fraud identification methods based on blacklists,reputation,content and behavioral characteristics have good identification performance in the telephone network,but it is difficult to apply to the Internet where IP(Internet Protocol)addresses change dynamically.To address this issue,we propose a fraudulent IP identification method based on homology detection and DBSCAN(Density-Based Spatial Clustering of Applications with Noise)clustering(DC-FIPD).First,we analyze the aggregation of fraudulent IP geographies and the homology of IP addresses.Next,the collected fraudulent IPs are clustered geographically to obtain the regional distribution of fraudulent IPs.Then,we constructed the fraudulent IP feature set,used the genetic optimization algorithm to determine the weights of the fraudulent IP features,and designed the calculation method of the IP risk value to give the risk value threshold of the fraudulent IP.Finally,the risk value of the target IP is calculated and the IP is identified based on the risk value threshold.Experimental results on a real-world telecom fraud detection dataset show that the DC-FIPD method achieves an average identification accuracy of 86.64%for fraudulent IPs.Additionally,the method records a precision of 86.08%,a recall of 45.24%,and an F1-score of 59.31%,offering a comprehensive evaluation of its performance in fraud detection.These results highlight the DC-FIPD method’s effectiveness in addressing the challenges of fraudulent IP identification.
文摘Understanding the root causes of fraud and learning about the most effective fraud prevention mechanisms are critical in reducing the incidence of financial fraud. Therefore, this study solicits the views of fraud investigators on the existence and effectiveness of fraud prevention mechanisms within Government-Linked Companies （GLCs）. Specifically, effective fraud prevention mechanisms （FPM） should be undertaken by five critical groups, namely the board of directors, audit committees, external auditors, internal auditors, and anti-fraud specialists. A total of 147 questionnaires were distributed to fraud investigators of all public listed GLCs in Malaysia. Out of those, a total of 45 usable responses were received, which represents an effective response rate of 30.6%. In terms of existence, the respondents ranked ＂management review of internal controls＂ and ＂external audits of financial statements＂ as the top-most FPMs. This was followed by other mechanisms such as operational audits, internal audits and internal control review/improvements by departments. Out of the 27 fraud prevention mechanisms, fraud investigators perceiyed surprise audits, fraud hotline, fraud prevention program and training, anti-fraud policy, fraud vulnerability reviews, operational audits, whistle-blowing policy, internal audit or fraud examination department, and, imposing penalty and disciplinary action as more effective than the others. Overall, this study provides important insights to practitioners and organizations in identifying fraud prevention mechanisms that are most effective.
文摘This work is concerned with GSM subscription fraud detection system using some network techniques. Fraud is a hitch around the globe with huge loss of income. Fraud has an effect on the credibility and performance of telecommunication companies. The most difficult problem that faces the industry is the fact that fraud is dynamic, which means that whenever fraudsters feel that they will be detected, they devise other ways to circumvent security measures. In such cases, the perpetrators’ intention is to completely avoid or at least reduce the charges for using the services. Subscription fraud is one of the major types of telecommunication fraud in which a customer obtains an account without intention to pay the bill. Thus at the level of a phone number, all transactions from the number will be fraudulent. In such cases abnormal usage occurs throughout the active period of the account;which is usually used for call selling or intensive self usage. This provides a means for illegal high profit business for fraudsters requiring minimal investment and relatively low risk of getting caught. A system to prevent subscription fraud in GSM telecommunications with high impact on long distance carriers is proposed to detect fraud. The system employs adaptive flexible techniques using advanced data analysis like Artificial Neural Networks (ANN). This study aims at developing a fraud detection model occurrence in GSM Network. The paper also gives analysis of the fraud detection Systems, fraud detection and prevention, fraud prevention methods etc. Fraud affects us all and is of particular concern to those who manage large government and business organisations where the potential losses are greatest. The operation of a mobile network is complex, and fraudsters invest a lot of energy to find and exploit every weakness of the system. A typical example would be subscription fraud, where a fraudster acquires a subscription to the mobile network under a false identity;and start reselling the use of his phone to unscrupulous customers (typically for international calls to distant foreign countries) at rate less than the regular tariff.
文摘With the increased accessibility of global trade information,transaction fraud has become a major worry in global banking and commerce security.The incidence and magnitude of transaction fraud are increasing daily,resulting in significant financial losses for both customers and financial professionals.With improvements in data mining and machine learning in computer science,the capacity to detect transaction fraud is becoming increasingly attainable.The primary goal of this research is to undertake a comparative examination of cutting-edge machine-learning algorithms developed to detect credit card fraud.The research looks at the efficacy of these machine learning algorithms using a publicly available dataset of credit card transactions performed by European cardholders in 2023,comprising around 550,000 records.The study uses this dataset to assess the performance of well-established machine learning models,measuring their accuracy,recall,and F1 score.In addition,the study includes a confusion matrix for all models to aid in evaluation and training time duration.Machin learning models,including Logistic regression,random forest,extra trees,and LGBM,achieve high accuracy and precision in the credit card fraud detection dataset,with a reported accuracy,recall,and F1 score of 1.00 for both classes.
文摘The 2011 Global Economic Crime Survey instituted by PricewaterhouseCoopers （PwC） confirms the economic crime in Malaysia to be on the increase and, therefore, requires immediate attention to stem the tides. In anticipation of the challenges occasioned due to a shift from the modified cash basis to the accrual basis of accounting, the Malaysian State＇s determination to move from a developing nation to a developed nation, and to be ranked among the first 10 in 2020, this paper presents the need for forensic accountant and auditor capability （i.e., mindset and skills） on forensic accountant and auditor competence （i.e., task performance fraud risk assessment （TPFRA）） in the Malaysian public sector. It also draws the attention of the users of public sector accountants and auditors to the understanding of fraud mechanisms and how to deal with fraudsters. The population of this study comprised the accountants and auditors in the office of the Accountant General and Auditor General of Malaysia. The objective of this paper is to investigate the competence requirements of accountants and auditors in the effective and efficient utilization of capability requirements, which have the potentials to usher in the best global practices in fighting fraud in the Malaysian public sector.
文摘Auditing standards require external auditors to provide reasonable assurance that the financial statements are free from material misstatements, either due to fraud or error. Inability of the external auditors to detect the material misstatements, particularly fraud, may expose them to litigation. The present study aims to examine the effect of personality factors （i.e., neuroticism, extraversion, conscientiousness, openness to experience and agreeableness） on the external auditors＇ ability to detect the likelihood of fraud. An experimental approach is adopted by sending case materials to audit partners and audit managers attached to auditing firms operating in Malaysia. The result shows that personality does not have a positive effect on the external auditors＇ ability to detect the likelihood of fraud.
基金the National Natural Science Foundation of China(No.61931019).
文摘With increasingly rampant telephone fraud activities,the social impact and economic losses caused to China have increased dramatically.Precise,convenient,and efficient fraudulent phone call recognition has become a challenge since telephone fraud became more varied and covert.To deal with this problem,many researchers have extracted some statistical features of telephone fraud behavior and proposed some machine learning algorithms on the field of fraudulent phone call recognition.In this paper,the calling detail records are utilized to construct a classifier for fraudulent phone call recognition.Meantime,a deep learning approach based on convolutional neural network(CNN)is proposed for better features learning and compared with the existing state-of-the-art machine learning algorithms.It learns phone number and call behavior features of telephone fraud,and improves the accuracy of classification.The evaluation results show that the proposed algorithm outperforms competitive algorithms.
