Traditional anomaly detection methods often assume that data points are independent or exhibit regularly structured relationships,as in Euclidean data such as time series or image grids.However,real-world data frequen...Traditional anomaly detection methods often assume that data points are independent or exhibit regularly structured relationships,as in Euclidean data such as time series or image grids.However,real-world data frequently involve irregular,interconnected structures,requiring a shift toward non-Euclidean approaches.This study introduces a novel anomaly detection framework designed to handle non-Euclidean data by modeling transactions as graph signals.By leveraging graph convolution filters,we extract meaningful connection strengths that capture relational dependencies often overlooked in traditional methods.Utilizing the Graph Convolutional Networks(GCN)framework,we integrate graph-based embeddings with conventional anomaly detection models,enhancing performance through relational insights.Ourmethod is validated on European credit card transaction data,demonstrating its effectiveness in detecting fraudulent transactions,particularly thosewith subtle patterns that evade traditional,amountbased detection techniques.The results highlight the advantages of incorporating temporal and structural dependencies into fraud detection,showcasing the robustness and applicability of our approach in complex,real-world scenarios.展开更多
Real-time Fraud Detection has always been a challenging task, especially in financial, insurance, and telecom industries. There are mainly three methods, which are rule set, outlier detection and classification to sol...Real-time Fraud Detection has always been a challenging task, especially in financial, insurance, and telecom industries. There are mainly three methods, which are rule set, outlier detection and classification to solve the problem. But those methods have some drawbacks respectively. To overcome these limitations, we propose a new algorithm UAF (Usage Amount Forecast).Firstly, Manhattan distance is used to measure the similarity between fraudulent instances and normal ones. Secondly, UAF gives real-time score which detects the fraud early and reduces as much economic loss as possible. Experiments on various real-world datasets demonstrate the high potential of UAF for processing real-time data and predicting fraudulent users.展开更多
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
The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system,as well as to enforce customer confidence in digital payment systems.Historically,credit ca...The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system,as well as to enforce customer confidence in digital payment systems.Historically,credit card companies have used rulebased approaches to detect fraudulent transactions,but these have proven inadequate due to the complexity of fraud strategies and have been replaced by much more powerful solutions based on machine learning or deep learning algorithms.Despite significant progress,the current approaches to fraud detection suffer from a number of limitations:for example,it is unclear whether some transaction features are more effective than others in discriminating fraudulent transactions,and they often neglect possible correlations among transactions,even though they could reveal illicit behaviour.In this paper,we propose a novel credit card fraud detection(CCFD)method based on a transaction behaviour-based hierarchical gated network.First,we introduce a feature-oriented extraction module capable of identifying key features from original transactions,and such analysis is effective in revealing the behavioural characteristics of fraudsters.Second,we design a transaction-oriented extraction module capable of capturing the correlation between users’historical and current transactional behaviour.Such information is crucial for revealing users’sequential behaviour patterns.Our approach,called transactional-behaviour-based hierarchical gated network model(TbHGN),extracts two types of new transactional features,which are then combined in a feature interaction module to learn the final transactional representations used for CCFD.We have conducted extensive experiments on a real-world credit card transaction dataset with an increase in average F1 between 1.42%and 6.53%and an improvement in average AUC between 0.63%and 2.78%over the state of the art.展开更多
Supply Chain Finance(SCF)is important for improving the effectiveness of supply chain capital operations and reducing the overall management cost of a supply chain.In recent years,with the deep integration of supply c...Supply Chain Finance(SCF)is important for improving the effectiveness of supply chain capital operations and reducing the overall management cost of a supply chain.In recent years,with the deep integration of supply chain and Internet,Big Data,Artificial Intelligence,Internet of Things,Blockchain,etc.,the efficiency of supply chain financial services can be greatly promoted through building more customized risk pricing models and conducting more rigorous investment decision-making processes.However,with the rapid development of new technologies,the SCF data has been massively increased and new financial fraud behaviors or patterns are becoming more covertly scattered among normal ones.The lack of enough capability to handle the big data volumes and mitigate the financial frauds may lead to huge losses in supply chains.In this article,a distributed approach of big data mining is proposed for financial fraud detection in a supply chain,which implements the distributed deep learning model of Convolutional Neural Network(CNN)on big data infrastructure of Apache Spark and Hadoop to speed up the processing of the large dataset in parallel and reduce the processing time significantly.By training and testing on the continually updated SCF dataset,the approach can intelligently and automatically classify the massive data samples and discover the fraudulent financing behaviors,so as to enhance the financial fraud detection with high precision and recall rates,and reduce the losses of frauds in a supply chain.展开更多
In recent years,the rapid development of e-commerce exposes great vulnerabilities in online transactions for fraudsters to exploit.Credit card transactions take a salient role in nowadays’online transactions for its ...In recent years,the rapid development of e-commerce exposes great vulnerabilities in online transactions for fraudsters to exploit.Credit card transactions take a salient role in nowadays’online transactions for its obvious advantages including discounts and earning credit card points.So credit card fraudulence has become a target of concern.In order to deal with the situation,credit card fraud detection based on machine learning is been studied recently.Yet,it is difficult to detect fraudulent transactions due to data imbalance(normal and fraudulent transactions),for which Smote algorithm is proposed in order to resolve data imbalance.The assessment of Light Gradient Boosting Machine model which proposed in the paper depends much on datasets collected from clients’daily transactions.Besides,to prove the new model’s superiority in detecting credit card fraudulence,Light Gradient Boosting Machine model is compared with Random Forest and Gradient Boosting Machine algorithm in the experiment.The results indicate that Light Gradient Boosting Machine model has a good performance.The experiment in credit card fraud detection based on Light Gradient Boosting Machine model achieved a total recall rate of 99%in real dataset and fast feedback,which proves the new model’s efficiency in detecting credit card fraudulence.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
Credit card fraudulent data is highly imbalanced, and it has presented an overwhelmingly large portion of nonfraudulent transactions and a small portion of fraudulent transactions. The measures used to judge the verac...Credit card fraudulent data is highly imbalanced, and it has presented an overwhelmingly large portion of nonfraudulent transactions and a small portion of fraudulent transactions. The measures used to judge the veracity of the detection algorithms become critical to the deployment of a model that accurately scores fraudulent transactions taking into account case imbalance, and the cost of identifying a case as genuine when, in fact, the case is a fraudulent transaction. In this paper, a new criterion to judge classification algorithms, which considers the cost of misclassification, is proposed, and several undersampling techniques are compared by this new criterion. At the same time, a weighted support vector machine (SVM) algorithm considering the financial cost of misclassification is introduced, proving to be more practical for credit card fraud detection than traditional methodologies. This weighted SVM uses transaction balances as weights for fraudulent transactions, and a uniformed weight for nonfraudulent transactions. The results show this strategy greatly improve performance of credit card fraud detection.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
A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all...A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.展开更多
To evaluate the applicability of the M-score model in the Chinese capital market,this research observed 190 financial fraud samples punished by the China Securities Regulatory Commission(CSRC)in the years from 2014 to...To evaluate the applicability of the M-score model in the Chinese capital market,this research observed 190 financial fraud samples punished by the China Securities Regulatory Commission(CSRC)in the years from 2014 to 2018.The test results indicate that two types of errors are high,which means that the applicability of the M-score is unacceptable.Therefore,in this paper,a 9-index model is constructed by Wald's backward stepwise regression method,and the optimal threshold is set by the Beneish expected cost method(ECM).The accuracy of the modified M-score is significantly improved,especially the Type I error rate of is reduced from 70.37%to 19.75%.The receiver operating characteristic(ROC)curve test also proves the superior identification effect of the modified M-score applied in the Chinese market.Finally,variables such as current ratio,fixed asset index,and equity concentration in the modified model could represent the fraud characteristics of Chinese listed companies.展开更多
The proliferation of internet communication channels has increased telecom fraud,causing billions of euros in losses for customers and the industry each year.Fraudsters constantly find new ways to engage in illegal ac...The proliferation of internet communication channels has increased telecom fraud,causing billions of euros in losses for customers and the industry each year.Fraudsters constantly find new ways to engage in illegal activity on the network.To reduce these losses,a new fraud detection approach is required.Telecom fraud detection involves identifying a small number of fraudulent calls from a vast amount of call traffic.Developing an effective strategy to combat fraud has become challenging.Although much effort has been made to detect fraud,most existing methods are designed for batch processing,not real-time detection.To solve this problem,we propose an online fraud detection model using a Neural Factorization Autoencoder(NFA),which analyzes customer calling patterns to detect fraudulent calls.The model employs Neural Factorization Machines(NFM)and an Autoencoder(AE)to model calling patterns and a memory module to adapt to changing customer behaviour.We evaluate our approach on a large dataset of real-world call detail records and compare it with several state-of-the-art methods.Our results show that our approach outperforms the baselines,with an AUC of 91.06%,a TPR of 91.89%,an FPR of 14.76%,and an F1-score of 95.45%.These results demonstrate the effectiveness of our approach in detecting fraud in real-time and suggest that it can be a valuable tool for preventing fraud in telecommunications networks.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
基金supported by the National Research Foundation of Korea(NRF)funded by the Korea government(RS-2023-00249743)Additionally,this research was supported by the Global-Learning&Academic Research Institution for Master’s,PhD Students,and Postdocs(LAMP)Program of the National Research Foundation of Korea(NRF)grant funded by the Ministry of Education(RS-2024-00443714)This research was also supported by the“Research Base Construction Fund Support Program”funded by Jeonbuk National University in 2025.
文摘Traditional anomaly detection methods often assume that data points are independent or exhibit regularly structured relationships,as in Euclidean data such as time series or image grids.However,real-world data frequently involve irregular,interconnected structures,requiring a shift toward non-Euclidean approaches.This study introduces a novel anomaly detection framework designed to handle non-Euclidean data by modeling transactions as graph signals.By leveraging graph convolution filters,we extract meaningful connection strengths that capture relational dependencies often overlooked in traditional methods.Utilizing the Graph Convolutional Networks(GCN)framework,we integrate graph-based embeddings with conventional anomaly detection models,enhancing performance through relational insights.Ourmethod is validated on European credit card transaction data,demonstrating its effectiveness in detecting fraudulent transactions,particularly thosewith subtle patterns that evade traditional,amountbased detection techniques.The results highlight the advantages of incorporating temporal and structural dependencies into fraud detection,showcasing the robustness and applicability of our approach in complex,real-world scenarios.
基金This paper is supported by the National Natural Science Foundation of China (61272515), and National Science & Technology Pillar Program (2015BAH03F02).
文摘Real-time Fraud Detection has always been a challenging task, especially in financial, insurance, and telecom industries. There are mainly three methods, which are rule set, outlier detection and classification to solve the problem. But those methods have some drawbacks respectively. To overcome these limitations, we propose a new algorithm UAF (Usage Amount Forecast).Firstly, Manhattan distance is used to measure the similarity between fraudulent instances and normal ones. Secondly, UAF gives real-time score which detects the fraud early and reduces as much economic loss as possible. Experiments on various real-world datasets demonstrate the high potential of UAF for processing real-time data and predicting fraudulent users.
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.
基金supported in part by the National Natural Science Foundation of China(61972241)the Natural Science Foundation of Shanghai(24ZR1427500,22ZR1427100)+1 种基金the Key Projects of Natural Science Research in Anhui Higher Education Institutions(2022AH051909)Bengbu University 2021 High-Level Scientific Research and Cultivation Project(2021pyxm04).
文摘The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system,as well as to enforce customer confidence in digital payment systems.Historically,credit card companies have used rulebased approaches to detect fraudulent transactions,but these have proven inadequate due to the complexity of fraud strategies and have been replaced by much more powerful solutions based on machine learning or deep learning algorithms.Despite significant progress,the current approaches to fraud detection suffer from a number of limitations:for example,it is unclear whether some transaction features are more effective than others in discriminating fraudulent transactions,and they often neglect possible correlations among transactions,even though they could reveal illicit behaviour.In this paper,we propose a novel credit card fraud detection(CCFD)method based on a transaction behaviour-based hierarchical gated network.First,we introduce a feature-oriented extraction module capable of identifying key features from original transactions,and such analysis is effective in revealing the behavioural characteristics of fraudsters.Second,we design a transaction-oriented extraction module capable of capturing the correlation between users’historical and current transactional behaviour.Such information is crucial for revealing users’sequential behaviour patterns.Our approach,called transactional-behaviour-based hierarchical gated network model(TbHGN),extracts two types of new transactional features,which are then combined in a feature interaction module to learn the final transactional representations used for CCFD.We have conducted extensive experiments on a real-world credit card transaction dataset with an increase in average F1 between 1.42%and 6.53%and an improvement in average AUC between 0.63%and 2.78%over the state of the art.
基金This research work is supported by Hunan Provincial Education Science 13th Five-Year Plan(Grant No.XJK016BXX001,Zhou,H.,http://jyt.hunan.gov.cn/jyt/sjyt/jky/index.html)Social Science Foundation of Hunan Province(Grant No.17YBA049,Zhou,H.,https://sk.rednet.cn/channel/7862.html)The work is also supported by Open Foundation for University Innovation Platform from Hunan Province,China(Grand No.18K103,Sun,G.,http://kxjsc.gov.hnedu.cn/).
文摘Supply Chain Finance(SCF)is important for improving the effectiveness of supply chain capital operations and reducing the overall management cost of a supply chain.In recent years,with the deep integration of supply chain and Internet,Big Data,Artificial Intelligence,Internet of Things,Blockchain,etc.,the efficiency of supply chain financial services can be greatly promoted through building more customized risk pricing models and conducting more rigorous investment decision-making processes.However,with the rapid development of new technologies,the SCF data has been massively increased and new financial fraud behaviors or patterns are becoming more covertly scattered among normal ones.The lack of enough capability to handle the big data volumes and mitigate the financial frauds may lead to huge losses in supply chains.In this article,a distributed approach of big data mining is proposed for financial fraud detection in a supply chain,which implements the distributed deep learning model of Convolutional Neural Network(CNN)on big data infrastructure of Apache Spark and Hadoop to speed up the processing of the large dataset in parallel and reduce the processing time significantly.By training and testing on the continually updated SCF dataset,the approach can intelligently and automatically classify the massive data samples and discover the fraudulent financing behaviors,so as to enhance the financial fraud detection with high precision and recall rates,and reduce the losses of frauds in a supply chain.
文摘In recent years,the rapid development of e-commerce exposes great vulnerabilities in online transactions for fraudsters to exploit.Credit card transactions take a salient role in nowadays’online transactions for its obvious advantages including discounts and earning credit card points.So credit card fraudulence has become a target of concern.In order to deal with the situation,credit card fraud detection based on machine learning is been studied recently.Yet,it is difficult to detect fraudulent transactions due to data imbalance(normal and fraudulent transactions),for which Smote algorithm is proposed in order to resolve data imbalance.The assessment of Light Gradient Boosting Machine model which proposed in the paper depends much on datasets collected from clients’daily transactions.Besides,to prove the new model’s superiority in detecting credit card fraudulence,Light Gradient Boosting Machine model is compared with Random Forest and Gradient Boosting Machine algorithm in the experiment.The results indicate that Light Gradient Boosting Machine model has a good performance.The experiment in credit card fraud detection based on Light Gradient Boosting Machine model achieved a total recall rate of 99%in real dataset and fast feedback,which proves the new model’s efficiency in detecting credit card fraudulence.
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Credit card fraudulent data is highly imbalanced, and it has presented an overwhelmingly large portion of nonfraudulent transactions and a small portion of fraudulent transactions. The measures used to judge the veracity of the detection algorithms become critical to the deployment of a model that accurately scores fraudulent transactions taking into account case imbalance, and the cost of identifying a case as genuine when, in fact, the case is a fraudulent transaction. In this paper, a new criterion to judge classification algorithms, which considers the cost of misclassification, is proposed, and several undersampling techniques are compared by this new criterion. At the same time, a weighted support vector machine (SVM) algorithm considering the financial cost of misclassification is introduced, proving to be more practical for credit card fraud detection than traditional methodologies. This weighted SVM uses transaction balances as weights for fraudulent transactions, and a uniformed weight for nonfraudulent transactions. The results show this strategy greatly improve performance of credit card fraud detection.
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 Institutional Fund Projects(IFPIP-1481-611-1443)the Key Projects of Natural Science Research in Anhui Higher Education Institutions(2022AH051909)+1 种基金the Provincial Quality Project of Colleges and Universities in Anhui Province(2022sdxx020,2022xqhz044)Bengbu University 2021 High-Level Scientific Research and Cultivation Project(2021pyxm04)。
文摘A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.
文摘To evaluate the applicability of the M-score model in the Chinese capital market,this research observed 190 financial fraud samples punished by the China Securities Regulatory Commission(CSRC)in the years from 2014 to 2018.The test results indicate that two types of errors are high,which means that the applicability of the M-score is unacceptable.Therefore,in this paper,a 9-index model is constructed by Wald's backward stepwise regression method,and the optimal threshold is set by the Beneish expected cost method(ECM).The accuracy of the modified M-score is significantly improved,especially the Type I error rate of is reduced from 70.37%to 19.75%.The receiver operating characteristic(ROC)curve test also proves the superior identification effect of the modified M-score applied in the Chinese market.Finally,variables such as current ratio,fixed asset index,and equity concentration in the modified model could represent the fraud characteristics of Chinese listed companies.
基金This research work has been conducted in cooperation with members of DETSI project supported by BPI France and Pays de Loire and Auvergne Rhone Alpes.
文摘The proliferation of internet communication channels has increased telecom fraud,causing billions of euros in losses for customers and the industry each year.Fraudsters constantly find new ways to engage in illegal activity on the network.To reduce these losses,a new fraud detection approach is required.Telecom fraud detection involves identifying a small number of fraudulent calls from a vast amount of call traffic.Developing an effective strategy to combat fraud has become challenging.Although much effort has been made to detect fraud,most existing methods are designed for batch processing,not real-time detection.To solve this problem,we propose an online fraud detection model using a Neural Factorization Autoencoder(NFA),which analyzes customer calling patterns to detect fraudulent calls.The model employs Neural Factorization Machines(NFM)and an Autoencoder(AE)to model calling patterns and a memory module to adapt to changing customer behaviour.We evaluate our approach on a large dataset of real-world call detail records and compare it with several state-of-the-art methods.Our results show that our approach outperforms the baselines,with an AUC of 91.06%,a TPR of 91.89%,an FPR of 14.76%,and an F1-score of 95.45%.These results demonstrate the effectiveness of our approach in detecting fraud in real-time and suggest that it can be a valuable tool for preventing fraud in telecommunications networks.
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.
