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
The paper is about how Lebanese auditors detect fraud in the course of their work and what they advise companies to implement in order to avoid fraudulent acts. For this purpose, various interviews were carded out fro...The paper is about how Lebanese auditors detect fraud in the course of their work and what they advise companies to implement in order to avoid fraudulent acts. For this purpose, various interviews were carded out from different experienced and well-reputed external auditors. This was also for their wide knowledge of all kinds of frauds. Data were taken from primary as well as secondary resources. The paper presents the theoretical and practical aspects. The theoretical part contains the accounting scandals, frauds, auditing processes, and auditor＇s responsibilities and tools for auditors to detect fraud. The practical part consists of case study analysis and detailed research processes.展开更多
This study investigates the relationship between earnings management and financial statements frauds.We examine how earnings management practices;done in the two years before the fraud,impact the likelihood of fraud o...This study investigates the relationship between earnings management and financial statements frauds.We examine how earnings management practices;done in the two years before the fraud,impact the likelihood of fraud occurrence.Moreover,we introduce a new measure for the fraud intensity.Using a sample of 70 fraud and 70 no-fraud firms,we find that firms committing fraud of higher intensity have managed earnings in the two years before the fraud occurrence.This paper contributes to the literature about fraud antecedents because it is the first study measuring the relationship between earnings management and the intensity of the fraud,and it can be also useful for practitioners,because using the analysis of earnings management practices,analysts can foresee and prevent financial statements frauds.展开更多
This study addresses security and ethical challenges in LLM-based Multi-Agent Systems, as exemplified in a blockchain fraud detection case study. Leveraging blockchain’s secure architecture, the framework involves sp...This study addresses security and ethical challenges in LLM-based Multi-Agent Systems, as exemplified in a blockchain fraud detection case study. Leveraging blockchain’s secure architecture, the framework involves specialized LLM Agents—ContractMining, Investigative, Ethics, and PerformanceMonitor, coordinated by a ManagerAgent. Baseline LLM models achieved 30% accuracy with a threshold method and 94% accuracy with a random-forest method. The Claude 3.5-powered LLM system reached an accuracy of 92%. Ethical evaluations revealed biases, highlighting the need for fairness-focused refinements. Our approach aims to develop trustworthy and reliable networks of agents capable of functioning even in adversarial environments. To our knowledge, no existing systems employ ethical LLM agents specifically designed to detect fraud, making this a novel contribution. Future work will focus on refining ethical frameworks, scaling the system, and benchmarking it against traditional methods to establish a robust, adaptable, and ethically grounded solution for blockchain fraud detection.展开更多
The rapid growth of technology impacts all aspects of modern life, including banking and financial transactions. While these industries benefit significantly from technological advancements, they also face challenges ...The rapid growth of technology impacts all aspects of modern life, including banking and financial transactions. While these industries benefit significantly from technological advancements, they also face challenges such as credit card fraud, the most prevalent type of financial fraud. Each year, such fraud leads to billions of dollars in losses for banks, financial institutions, and customers. Although many machine learning (ML) and, more recently, deep learning (DL) solutions have been developed to address this issue, most fail to strike an effective balance between speed and performance. Moreover, the reluctance of financial institutions to disclose their fraud datasets due to reputational risks adds further challenges. This study proposes a predictive model for credit card fraud detection that leverages the unique strengths of Energy-based Restricted Boltzmann Machines (EB-RBM) and Extended Long Short-Term Memory (xLSTM) models. EB-RBM is utilized for its ability to detect new and previously unseen fraudulent patterns, while xLSTM focuses on identifying known fraud types. These models are integrated using an ensemble approach to combine their strengths, achieving a balanced and reliable prediction system. The ensemble employs a bootstrap max-voting mechanism, assigning equal voting rights to EB-RBM and xLSTM, followed by result normalization and aggregation to classify transactions as fraudulent or genuine. The model’s performance is evaluated using metrics such as AUC-ROC, AUC-PR, precision, recall, F1-score, confusion matrix, and elapsed time. Experimental results on a real-world European cardholder dataset demonstrate that the proposed approach effectively balances speed and performance, outperforming recent models in the field.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 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
Fraud of credit cards is a major issue for financial organizations and individuals.As fraudulent actions become more complex,a demand for better fraud detection systems is rising.Deep learning approaches have shown pr...Fraud of credit cards is a major issue for financial organizations and individuals.As fraudulent actions become more complex,a demand for better fraud detection systems is rising.Deep learning approaches have shown promise in several fields,including detecting credit card fraud.However,the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters.This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data,thereby improving fraud detection.Three deep learning models:AutoEncoder(AE),Convolution Neural Network(CNN),and Long Short-Term Memory(LSTM)are proposed to investigate how hyperparameter adjustment impacts the efficacy of deep learning models used to identify credit card fraud.The experiments conducted on a European credit card fraud dataset using different hyperparameters and three deep learning models demonstrate that the proposed models achieve a tradeoff between detection rate and precision,leading these models to be effective in accurately predicting credit card fraud.The results demonstrate that LSTM significantly outperformed AE and CNN in terms of accuracy(99.2%),detection rate(93.3%),and area under the curve(96.3%).These proposed models have surpassed those of existing studies and are expected to make a significant contribution to the field of credit card fraud detection.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
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
The aim of this study is to examine the qualities that auditors engaged in detecting potential fraud within multinational corporations in Sub-Saharan Africa should possess.To achieve this goal,a quantitative approach ...The aim of this study is to examine the qualities that auditors engaged in detecting potential fraud within multinational corporations in Sub-Saharan Africa should possess.To achieve this goal,a quantitative approach was used to develop and test a research model based on three theories:agency theory,attribution theory,and cognitive dissonance theory.Responses from a panel of two hundred and nine(209)auditors who conducted a legal audit mission in a Sub-Saharan multinational were analyzed using SmartPLS 3.3.3 software.The results emphasize the crucial importance of auditors’competence and continuous training in fraud detection.However,professional skepticism and time pressure were found to be non-significant in this context.This conclusion provides essential insights for auditors,highlighting the key qualities needed to effectively address fraud detection within multinational corporations in Sub-Saharan Africa.展开更多
The authors’aspiration was to learn-and focus on policy against fraud-leading to the sustainably growing societal illnesses of dishonesty,fraud,pessimism,and divisive issues.The appropriate venue,within the currently...The authors’aspiration was to learn-and focus on policy against fraud-leading to the sustainably growing societal illnesses of dishonesty,fraud,pessimism,and divisive issues.The appropriate venue,within the currently evolving laws and regulations,is proposed to be a three-tier combination of massive data,including data accumulation,transformation,organization,stratification,estimations,data analysis,and blockchain technology,predicted to revolutionize competition and efficiency,which are further suggested to be prerequisites for a more successful creation and implementation of the third element,AI.A currently evolving prosperity tripod is hinging on the three technological legs of the massive data control/management,blockchain tech,and a rapidly growing AI.While briefly incorporating some analysis of the blockchain application,we have analytically focused on the rest-the data and AI-of what we deem to be the prospective prosperity tripod for businesses,markets,and societies,in general,despite the challenges and risks involved in each.Instead of h ypothesizing a predetermined economic model,we are proposing a data-based Vector Autoregression(VAR)methodology for the AI with an application to the fraud and anti-fraud structure and policymaking.Hopefully,the entire attempt would portend some tangible prospective contribution in an achievable positive societal change.展开更多
Credit card fraud remains a significant challenge, with financial losses and consumer protection at stake. This study addresses the need for practical, real-time fraud detection methodologies. Using a Kaggle credit ca...Credit card fraud remains a significant challenge, with financial losses and consumer protection at stake. This study addresses the need for practical, real-time fraud detection methodologies. Using a Kaggle credit card dataset, I tackle class imbalance using the Synthetic Minority Oversampling Technique (SMOTE) to enhance modeling efficiency. I compare several machine learning algorithms, including Logistic Regression, Linear Discriminant Analysis, K-nearest Neighbors, Classification and Regression Tree, Naive Bayes, Support Vector, Random Forest, XGBoost, and Light Gradient-Boosting Machine to classify transactions as fraud or genuine. Rigorous evaluation metrics, such as AUC, PRAUC, F1, KS, Recall, and Precision, identify the Random Forest as the best performer in detecting fraudulent activities. The Random Forest model successfully identifies approximately 92% of transactions scoring 90 and above as fraudulent, equating to a detection rate of over 70% for all fraudulent transactions in the test dataset. Moreover, the model captures more than half of the fraud in each bin of the test dataset. SHAP values provide model explainability, with the SHAP summary plot highlighting the global importance of individual features, such as “V12” and “V14”. SHAP force plots offer local interpretability, revealing the impact of specific features on individual predictions. This study demonstrates the potential of machine learning, particularly the Random Forest model, for real-time credit card fraud detection, offering a promising approach to mitigate financial losses and protect consumers.展开更多
As financial criminal methods become increasingly sophisticated, traditional anti-money laundering and fraud detection approaches face significant challenges. This study focuses on the application technologies and cha...As financial criminal methods become increasingly sophisticated, traditional anti-money laundering and fraud detection approaches face significant challenges. This study focuses on the application technologies and challenges of big data analytics in anti-money laundering and financial fraud detection. The research begins by outlining the evolutionary trends of financial crimes and highlighting the new characteristics of the big data era. Subsequently, it systematically analyzes the application of big data analytics technologies in this field, including machine learning, network analysis, and real-time stream processing. Through case studies, the research demonstrates how these technologies enhance the accuracy and efficiency of anomalous transaction detection. However, the study also identifies challenges faced by big data analytics, such as data quality issues, algorithmic bias, and privacy protection concerns. To address these challenges, the research proposes solutions from both technological and managerial perspectives, including the application of privacy-preserving technologies like federated learning. Finally, the study discusses the development prospects of Regulatory Technology (RegTech), emphasizing the importance of synergy between technological innovation and regulatory policies. This research provides guidance for financial institutions and regulatory bodies in optimizing their anti-money laundering and fraud detection strategies.展开更多
Forensic accounting gained importance due to increasing number of financial frauds and scams. This new area in accounting encompasses accounting, auditing, and investigative skills, thus emerged to detect frauds. They...Forensic accounting gained importance due to increasing number of financial frauds and scams. This new area in accounting encompasses accounting, auditing, and investigative skills, thus emerged to detect frauds. They involve themselves in different areas like employee-related frauds, settlement and arbitrations, etc.. A forensic accountant has a financial sixth sense. Despite the fact that forensic accounting can bridge the gap between conventional accounting and auditing, this profession has not been able to gain the needed momentum due to some hassles. This paper tries to shed light on the theoretical concept, nature, practice, need, role of forensic accounting in preventing fraud, and the practical difficulties faced by forensic accountants. The study is based on information collected from interviewing practicing forensic accounting in India during 2011-12. The paper was able to assess the importance and rising scope of forensic accounting as a job. It also understood the practical difficulties they faced like lack of organized databases in Indian scenario which makes it difficult to access all needed information. Expectation level of the clients is very high and at times even unreasonable. This paper fulfills an identified need to study the important rising field of forensic accounting in India.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
Information analysis of high dimensional data was carried out through similarity measure application. High dimensional data were considered as the a typical structure. Additionally, overlapped and non-overlapped data ...Information analysis of high dimensional data was carried out through similarity measure application. High dimensional data were considered as the a typical structure. Additionally, overlapped and non-overlapped data were introduced, and similarity measure analysis was also illustrated and compared with conventional similarity measure. As a result, overlapped data comparison was possible to present similarity with conventional similarity measure. Non-overlapped data similarity analysis provided the clue to solve the similarity of high dimensional data. Considering high dimensional data analysis was designed with consideration of neighborhoods information. Conservative and strict solutions were proposed. Proposed similarity measure was applied to express financial fraud among multi dimensional datasets. In illustrative example, financial fraud similarity with respect to age, gender, qualification and job was presented. And with the proposed similarity measure, high dimensional personal data were calculated to evaluate how similar to the financial fraud. Calculation results show that the actual fraud has rather high similarity measure compared to the average, from minimal 0.0609 to maximal 0.1667.展开更多
Background:In recent years,blockchain technology has attracted considerable attention.It records cryptographic transactions in a public ledger that is difficult to alter and compromise because of the distributed conse...Background:In recent years,blockchain technology has attracted considerable attention.It records cryptographic transactions in a public ledger that is difficult to alter and compromise because of the distributed consensus.As a result,blockchain is believed to resist fraud and hacking.Results:This work explores the types of fraud and malicious activities that can be prevented by blockchain technology and identifies attacks to which blockchain remains vulnerable.Conclusions:This study recommends appropriate defensive measures and calls for further research into the techniques for fighting malicious activities related to blockchains.展开更多
In order to strengthen their security issues,electrical companies devote particular efforts to developing and enhancing their fraud detection techniques that cope with the information and communication technologies in...In order to strengthen their security issues,electrical companies devote particular efforts to developing and enhancing their fraud detection techniques that cope with the information and communication technologies integration in smart grid fields.Having been treated earlier by several researchers,various detection schemes adapted from attack models that benefit from the smart grid topologies weaknesses,aiming primarily to the identification of suspicious incoming hazards.Wireless meshes have been extensively used in smart grid communication architectures due to their facility,lightness of conception and low cost installation;however,the communicated packets are still exposed to be intercepted maliciously in order either to falsify pertinent information like the smart meter readings,or to inject false data instead,aiming at electricity theft during the communication phase.For this reason,this paper initiates a novel method based on RSA cryptographic algorithm to detect electricity fraud in smart grid.This new method consists of generating two different cryptograms of one electricity measurement before sending,after which the recipient is used to find the same value after decrypting the two cyphers in a normal case.Otherwise,a fraudulent manipulation could occur during the transmission stage.The presented method allows us to kill two birds with one stone.First,satisfactory outcomes are shown:the algorithm accuracy reaches 100%,from one hand,and the privacy is protected thanks to the cryptology concept on the other hand.展开更多
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.
文摘The paper is about how Lebanese auditors detect fraud in the course of their work and what they advise companies to implement in order to avoid fraudulent acts. For this purpose, various interviews were carded out from different experienced and well-reputed external auditors. This was also for their wide knowledge of all kinds of frauds. Data were taken from primary as well as secondary resources. The paper presents the theoretical and practical aspects. The theoretical part contains the accounting scandals, frauds, auditing processes, and auditor＇s responsibilities and tools for auditors to detect fraud. The practical part consists of case study analysis and detailed research processes.
文摘This study investigates the relationship between earnings management and financial statements frauds.We examine how earnings management practices;done in the two years before the fraud,impact the likelihood of fraud occurrence.Moreover,we introduce a new measure for the fraud intensity.Using a sample of 70 fraud and 70 no-fraud firms,we find that firms committing fraud of higher intensity have managed earnings in the two years before the fraud occurrence.This paper contributes to the literature about fraud antecedents because it is the first study measuring the relationship between earnings management and the intensity of the fraud,and it can be also useful for practitioners,because using the analysis of earnings management practices,analysts can foresee and prevent financial statements frauds.
文摘This study addresses security and ethical challenges in LLM-based Multi-Agent Systems, as exemplified in a blockchain fraud detection case study. Leveraging blockchain’s secure architecture, the framework involves specialized LLM Agents—ContractMining, Investigative, Ethics, and PerformanceMonitor, coordinated by a ManagerAgent. Baseline LLM models achieved 30% accuracy with a threshold method and 94% accuracy with a random-forest method. The Claude 3.5-powered LLM system reached an accuracy of 92%. Ethical evaluations revealed biases, highlighting the need for fairness-focused refinements. Our approach aims to develop trustworthy and reliable networks of agents capable of functioning even in adversarial environments. To our knowledge, no existing systems employ ethical LLM agents specifically designed to detect fraud, making this a novel contribution. Future work will focus on refining ethical frameworks, scaling the system, and benchmarking it against traditional methods to establish a robust, adaptable, and ethically grounded solution for blockchain fraud detection.
文摘The rapid growth of technology impacts all aspects of modern life, including banking and financial transactions. While these industries benefit significantly from technological advancements, they also face challenges such as credit card fraud, the most prevalent type of financial fraud. Each year, such fraud leads to billions of dollars in losses for banks, financial institutions, and customers. Although many machine learning (ML) and, more recently, deep learning (DL) solutions have been developed to address this issue, most fail to strike an effective balance between speed and performance. Moreover, the reluctance of financial institutions to disclose their fraud datasets due to reputational risks adds further challenges. This study proposes a predictive model for credit card fraud detection that leverages the unique strengths of Energy-based Restricted Boltzmann Machines (EB-RBM) and Extended Long Short-Term Memory (xLSTM) models. EB-RBM is utilized for its ability to detect new and previously unseen fraudulent patterns, while xLSTM focuses on identifying known fraud types. These models are integrated using an ensemble approach to combine their strengths, achieving a balanced and reliable prediction system. The ensemble employs a bootstrap max-voting mechanism, assigning equal voting rights to EB-RBM and xLSTM, followed by result normalization and aggregation to classify transactions as fraudulent or genuine. The model’s performance is evaluated using metrics such as AUC-ROC, AUC-PR, precision, recall, F1-score, confusion matrix, and elapsed time. Experimental results on a real-world European cardholder dataset demonstrate that the proposed approach effectively balances speed and performance, outperforming recent models in the field.
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 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.
文摘Fraud of credit cards is a major issue for financial organizations and individuals.As fraudulent actions become more complex,a demand for better fraud detection systems is rising.Deep learning approaches have shown promise in several fields,including detecting credit card fraud.However,the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters.This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data,thereby improving fraud detection.Three deep learning models:AutoEncoder(AE),Convolution Neural Network(CNN),and Long Short-Term Memory(LSTM)are proposed to investigate how hyperparameter adjustment impacts the efficacy of deep learning models used to identify credit card fraud.The experiments conducted on a European credit card fraud dataset using different hyperparameters and three deep learning models demonstrate that the proposed models achieve a tradeoff between detection rate and precision,leading these models to be effective in accurately predicting credit card fraud.The results demonstrate that LSTM significantly outperformed AE and CNN in terms of accuracy(99.2%),detection rate(93.3%),and area under the curve(96.3%).These proposed models have surpassed those of existing studies and are expected to make a significant contribution to the field of credit card fraud detection.
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.
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The aim of this study is to examine the qualities that auditors engaged in detecting potential fraud within multinational corporations in Sub-Saharan Africa should possess.To achieve this goal,a quantitative approach was used to develop and test a research model based on three theories:agency theory,attribution theory,and cognitive dissonance theory.Responses from a panel of two hundred and nine(209)auditors who conducted a legal audit mission in a Sub-Saharan multinational were analyzed using SmartPLS 3.3.3 software.The results emphasize the crucial importance of auditors’competence and continuous training in fraud detection.However,professional skepticism and time pressure were found to be non-significant in this context.This conclusion provides essential insights for auditors,highlighting the key qualities needed to effectively address fraud detection within multinational corporations in Sub-Saharan Africa.
文摘The authors’aspiration was to learn-and focus on policy against fraud-leading to the sustainably growing societal illnesses of dishonesty,fraud,pessimism,and divisive issues.The appropriate venue,within the currently evolving laws and regulations,is proposed to be a three-tier combination of massive data,including data accumulation,transformation,organization,stratification,estimations,data analysis,and blockchain technology,predicted to revolutionize competition and efficiency,which are further suggested to be prerequisites for a more successful creation and implementation of the third element,AI.A currently evolving prosperity tripod is hinging on the three technological legs of the massive data control/management,blockchain tech,and a rapidly growing AI.While briefly incorporating some analysis of the blockchain application,we have analytically focused on the rest-the data and AI-of what we deem to be the prospective prosperity tripod for businesses,markets,and societies,in general,despite the challenges and risks involved in each.Instead of h ypothesizing a predetermined economic model,we are proposing a data-based Vector Autoregression(VAR)methodology for the AI with an application to the fraud and anti-fraud structure and policymaking.Hopefully,the entire attempt would portend some tangible prospective contribution in an achievable positive societal change.
文摘Credit card fraud remains a significant challenge, with financial losses and consumer protection at stake. This study addresses the need for practical, real-time fraud detection methodologies. Using a Kaggle credit card dataset, I tackle class imbalance using the Synthetic Minority Oversampling Technique (SMOTE) to enhance modeling efficiency. I compare several machine learning algorithms, including Logistic Regression, Linear Discriminant Analysis, K-nearest Neighbors, Classification and Regression Tree, Naive Bayes, Support Vector, Random Forest, XGBoost, and Light Gradient-Boosting Machine to classify transactions as fraud or genuine. Rigorous evaluation metrics, such as AUC, PRAUC, F1, KS, Recall, and Precision, identify the Random Forest as the best performer in detecting fraudulent activities. The Random Forest model successfully identifies approximately 92% of transactions scoring 90 and above as fraudulent, equating to a detection rate of over 70% for all fraudulent transactions in the test dataset. Moreover, the model captures more than half of the fraud in each bin of the test dataset. SHAP values provide model explainability, with the SHAP summary plot highlighting the global importance of individual features, such as “V12” and “V14”. SHAP force plots offer local interpretability, revealing the impact of specific features on individual predictions. This study demonstrates the potential of machine learning, particularly the Random Forest model, for real-time credit card fraud detection, offering a promising approach to mitigate financial losses and protect consumers.
文摘As financial criminal methods become increasingly sophisticated, traditional anti-money laundering and fraud detection approaches face significant challenges. This study focuses on the application technologies and challenges of big data analytics in anti-money laundering and financial fraud detection. The research begins by outlining the evolutionary trends of financial crimes and highlighting the new characteristics of the big data era. Subsequently, it systematically analyzes the application of big data analytics technologies in this field, including machine learning, network analysis, and real-time stream processing. Through case studies, the research demonstrates how these technologies enhance the accuracy and efficiency of anomalous transaction detection. However, the study also identifies challenges faced by big data analytics, such as data quality issues, algorithmic bias, and privacy protection concerns. To address these challenges, the research proposes solutions from both technological and managerial perspectives, including the application of privacy-preserving technologies like federated learning. Finally, the study discusses the development prospects of Regulatory Technology (RegTech), emphasizing the importance of synergy between technological innovation and regulatory policies. This research provides guidance for financial institutions and regulatory bodies in optimizing their anti-money laundering and fraud detection strategies.
文摘Forensic accounting gained importance due to increasing number of financial frauds and scams. This new area in accounting encompasses accounting, auditing, and investigative skills, thus emerged to detect frauds. They involve themselves in different areas like employee-related frauds, settlement and arbitrations, etc.. A forensic accountant has a financial sixth sense. Despite the fact that forensic accounting can bridge the gap between conventional accounting and auditing, this profession has not been able to gain the needed momentum due to some hassles. This paper tries to shed light on the theoretical concept, nature, practice, need, role of forensic accounting in preventing fraud, and the practical difficulties faced by forensic accountants. The study is based on information collected from interviewing practicing forensic accounting in India during 2011-12. The paper was able to assess the importance and rising scope of forensic accounting as a job. It also understood the practical difficulties they faced like lack of organized databases in Indian scenario which makes it difficult to access all needed information. Expectation level of the clients is very high and at times even unreasonable. This paper fulfills an identified need to study the important rising field of forensic accounting in India.
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.
基金Project(RDF 11-02-03)supported by the Research Development Fund of XJTLU,China
文摘Information analysis of high dimensional data was carried out through similarity measure application. High dimensional data were considered as the a typical structure. Additionally, overlapped and non-overlapped data were introduced, and similarity measure analysis was also illustrated and compared with conventional similarity measure. As a result, overlapped data comparison was possible to present similarity with conventional similarity measure. Non-overlapped data similarity analysis provided the clue to solve the similarity of high dimensional data. Considering high dimensional data analysis was designed with consideration of neighborhoods information. Conservative and strict solutions were proposed. Proposed similarity measure was applied to express financial fraud among multi dimensional datasets. In illustrative example, financial fraud similarity with respect to age, gender, qualification and job was presented. And with the proposed similarity measure, high dimensional personal data were calculated to evaluate how similar to the financial fraud. Calculation results show that the actual fraud has rather high similarity measure compared to the average, from minimal 0.0609 to maximal 0.1667.
文摘Background:In recent years,blockchain technology has attracted considerable attention.It records cryptographic transactions in a public ledger that is difficult to alter and compromise because of the distributed consensus.As a result,blockchain is believed to resist fraud and hacking.Results:This work explores the types of fraud and malicious activities that can be prevented by blockchain technology and identifies attacks to which blockchain remains vulnerable.Conclusions:This study recommends appropriate defensive measures and calls for further research into the techniques for fighting malicious activities related to blockchains.
文摘In order to strengthen their security issues,electrical companies devote particular efforts to developing and enhancing their fraud detection techniques that cope with the information and communication technologies integration in smart grid fields.Having been treated earlier by several researchers,various detection schemes adapted from attack models that benefit from the smart grid topologies weaknesses,aiming primarily to the identification of suspicious incoming hazards.Wireless meshes have been extensively used in smart grid communication architectures due to their facility,lightness of conception and low cost installation;however,the communicated packets are still exposed to be intercepted maliciously in order either to falsify pertinent information like the smart meter readings,or to inject false data instead,aiming at electricity theft during the communication phase.For this reason,this paper initiates a novel method based on RSA cryptographic algorithm to detect electricity fraud in smart grid.This new method consists of generating two different cryptograms of one electricity measurement before sending,after which the recipient is used to find the same value after decrypting the two cyphers in a normal case.Otherwise,a fraudulent manipulation could occur during the transmission stage.The presented method allows us to kill two birds with one stone.First,satisfactory outcomes are shown:the algorithm accuracy reaches 100%,from one hand,and the privacy is protected thanks to the cryptology concept on the other hand.
