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
Some case studies are presented ranging from geological fakes and frauds,homicides and one environmental forensic case.Fakes may be true geological materials such as created fossils or gems and precious stones,or wher...Some case studies are presented ranging from geological fakes and frauds,homicides and one environmental forensic case.Fakes may be true geological materials such as created fossils or gems and precious stones,or where geological methods are used to analyse fakes,such as the stones or ceramics used in making archaeological or art forgeries(e.g.,mineral pigments in paintings).Fakes have also been created for reasons of academic rivalry,career advancement and religious belief.Fraud commonly involves over-stated claims of ore content associated with mining and the oil and gas industry.The range of geological fakes,the uses of geological methods in detecting fakes,and the extent of fraud in the mining sector are all extensive and sometimes incredible.The homicide is case presented to demonstrate how the types of geological investigation described in the rest of this volume may be applied.We include an environmental forensic case for similar reasons,to show that forensic geology may be applied to more than homicides and fakery.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
Non-technical losses(NTL)of electric power are a serious problem for electric distribution companies.The solution determines the cost,stability,reliability,and quality of the supplied electricity.The widespread use of...Non-technical losses(NTL)of electric power are a serious problem for electric distribution companies.The solution determines the cost,stability,reliability,and quality of the supplied electricity.The widespread use of advanced metering infrastructure(AMI)and Smart Grid allows all participants in the distribution grid to store and track electricity consumption.During the research,a machine learning model is developed that allows analyzing and predicting the probability of NTL for each consumer of the distribution grid based on daily electricity consumption readings.This model is an ensemble meta-algorithm(stacking)that generalizes the algorithms of random forest,LightGBM,and a homogeneous ensemble of artificial neural networks.The best accuracy of the proposed meta-algorithm in comparison to basic classifiers is experimentally confirmed on the test sample.Such a model,due to good accuracy indicators(ROC-AUC-0.88),can be used as a methodological basis for a decision support system,the purpose of which is to form a sample of suspected NTL sources.The use of such a sample will allow the top management of electric distribution companies to increase the efficiency of raids by performers,making them targeted and accurate,which should contribute to the fight against NTL and the sustainable development of the electric power industry.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
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
Email communication plays a crucial role in both personal and professional contexts;however,it is frequently compromised by the ongoing challenge of spam,which detracts from productivity and introduces considerable se...Email communication plays a crucial role in both personal and professional contexts;however,it is frequently compromised by the ongoing challenge of spam,which detracts from productivity and introduces considerable security risks.Current spam detection techniques often struggle to keep pace with the evolving tactics employed by spammers,resulting in user dissatisfaction and potential data breaches.To address this issue,we introduce the Divide and Conquer-Generative Adversarial Network Squeeze and Excitation-Based Framework(DaC-GANSAEBF),an innovative deep-learning model designed to identify spam emails.This framework incorporates cutting-edge technologies,such as Generative Adversarial Networks(GAN),Squeeze and Excitation(SAE)modules,and a newly formulated Light Dual Attention(LDA)mechanism,which effectively utilizes both global and local attention to discern intricate patterns within textual data.This approach significantly improves efficiency and accuracy by segmenting scanned email content into smaller,independently evaluated components.The model underwent training and validation using four publicly available benchmark datasets,achieving an impressive average accuracy of 98.87%,outperforming leading methods in the field.These findings underscore the resilience and scalability of DaC-GANSAEBF,positioning it as a viable solution for contemporary spam detection systems.The framework can be easily integrated into existing technologies to enhance user security and reduce the risks associated with spam.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
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
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
With the increased accessibility of global trade information,transaction fraud has become a major worry in global banking and commerce security.The incidence and magnitude of transaction fraud are increasing daily,res...With the increased accessibility of global trade information,transaction fraud has become a major worry in global banking and commerce security.The incidence and magnitude of transaction fraud are increasing daily,resulting in significant financial losses for both customers and financial professionals.With improvements in data mining and machine learning in computer science,the capacity to detect transaction fraud is becoming increasingly attainable.The primary goal of this research is to undertake a comparative examination of cutting-edge machine-learning algorithms developed to detect credit card fraud.The research looks at the efficacy of these machine learning algorithms using a publicly available dataset of credit card transactions performed by European cardholders in 2023,comprising around 550,000 records.The study uses this dataset to assess the performance of well-established machine learning models,measuring their accuracy,recall,and F1 score.In addition,the study includes a confusion matrix for all models to aid in evaluation and training time duration.Machin learning models,including Logistic regression,random forest,extra trees,and LGBM,achieve high accuracy and precision in the credit card fraud detection dataset,with a reported accuracy,recall,and F1 score of 1.00 for both classes.展开更多
The 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
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
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
Decentralized Finance(DeFi)is a system of financial products and services built and delivered through smart contracts on various blockchains.In recent years,DeFi has gained popularity and market capitalization.However...Decentralized Finance(DeFi)is a system of financial products and services built and delivered through smart contracts on various blockchains.In recent years,DeFi has gained popularity and market capitalization.However,it has also been connected to crime,particularly various types of securities violations.The lack of Know Your Customer requirements in DeFi poses challenges for governments trying to mitigate potential offenses.This study aims to determine whether this problem is suited to a machine learning approach,namely,whether we can identify DeFi projects potentially engaging in securities violations based on their tokens’smart contract code.We adapted prior works on detecting specific types of securities violations across Ethereum by building classifiers based on features extracted from DeFi projects’tokens’smart contract code(specifically,opcode-based features).Our final model was a random forest model that achieved an 80%F-1 score against a baseline of 50%.Notably,we further explored the code-based features that are the most important to our model’s performance in more detail by analyzing tokens’Solidity code and conducting cosine similarity analyses.We found that one element of the code that our opcode-based features can capture is the implementation of the SafeMath library,although this does not account for the entirety of our features.Another contribution of our study is a new dataset,comprising(a)a verified ground truth dataset for tokens involved in securities violations and(b)a set of legitimate tokens from a reputable DeFi aggregator.This paper further discusses the potential use of a model like ours by prosecutors in enforcement efforts and connects it to a wider legal context.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
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Some case studies are presented ranging from geological fakes and frauds,homicides and one environmental forensic case.Fakes may be true geological materials such as created fossils or gems and precious stones,or where geological methods are used to analyse fakes,such as the stones or ceramics used in making archaeological or art forgeries(e.g.,mineral pigments in paintings).Fakes have also been created for reasons of academic rivalry,career advancement and religious belief.Fraud commonly involves over-stated claims of ore content associated with mining and the oil and gas industry.The range of geological fakes,the uses of geological methods in detecting fakes,and the extent of fraud in the mining sector are all extensive and sometimes incredible.The homicide is case presented to demonstrate how the types of geological investigation described in the rest of this volume may be applied.We include an environmental forensic case for similar reasons,to show that forensic geology may be applied to more than homicides and fakery.
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.
文摘Non-technical losses(NTL)of electric power are a serious problem for electric distribution companies.The solution determines the cost,stability,reliability,and quality of the supplied electricity.The widespread use of advanced metering infrastructure(AMI)and Smart Grid allows all participants in the distribution grid to store and track electricity consumption.During the research,a machine learning model is developed that allows analyzing and predicting the probability of NTL for each consumer of the distribution grid based on daily electricity consumption readings.This model is an ensemble meta-algorithm(stacking)that generalizes the algorithms of random forest,LightGBM,and a homogeneous ensemble of artificial neural networks.The best accuracy of the proposed meta-algorithm in comparison to basic classifiers is experimentally confirmed on the test sample.Such a model,due to good accuracy indicators(ROC-AUC-0.88),can be used as a methodological basis for a decision support system,the purpose of which is to form a sample of suspected NTL sources.The use of such a sample will allow the top management of electric distribution companies to increase the efficiency of raids by performers,making them targeted and accurate,which should contribute to the fight against NTL and the sustainable development of the electric power industry.
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.
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funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.(GPIP:71-829-2024).
文摘Email communication plays a crucial role in both personal and professional contexts;however,it is frequently compromised by the ongoing challenge of spam,which detracts from productivity and introduces considerable security risks.Current spam detection techniques often struggle to keep pace with the evolving tactics employed by spammers,resulting in user dissatisfaction and potential data breaches.To address this issue,we introduce the Divide and Conquer-Generative Adversarial Network Squeeze and Excitation-Based Framework(DaC-GANSAEBF),an innovative deep-learning model designed to identify spam emails.This framework incorporates cutting-edge technologies,such as Generative Adversarial Networks(GAN),Squeeze and Excitation(SAE)modules,and a newly formulated Light Dual Attention(LDA)mechanism,which effectively utilizes both global and local attention to discern intricate patterns within textual data.This approach significantly improves efficiency and accuracy by segmenting scanned email content into smaller,independently evaluated components.The model underwent training and validation using four publicly available benchmark datasets,achieving an impressive average accuracy of 98.87%,outperforming leading methods in the field.These findings underscore the resilience and scalability of DaC-GANSAEBF,positioning it as a viable solution for contemporary spam detection systems.The framework can be easily integrated into existing technologies to enhance user security and reduce the risks associated with spam.
基金supported by the National Social Science Fund of China(23BGL272)。
文摘The fraudulent website image is a vital information carrier for telecom fraud.The efficient and precise recognition of fraudulent website images is critical to combating and dealing with fraudulent websites.Current research on image recognition of fraudulent websites is mainly carried out at the level of image feature extraction and similarity study,which have such disadvantages as difficulty in obtaining image data,insufficient image analysis,and single identification types.This study develops a model based on the entropy method for image leader decision and Inception-v3 transfer learning to address these disadvantages.The data processing part of the model uses a breadth search crawler to capture the image data.Then,the information in the images is evaluated with the entropy method,image weights are assigned,and the image leader is selected.In model training and prediction,the transfer learning of the Inception-v3 model is introduced into image recognition of fraudulent websites.Using selected image leaders to train the model,multiple types of fraudulent websites are identified with high accuracy.The experiment proves that this model has a superior accuracy in recognizing images on fraudulent websites compared to other current models.
基金funded by the National Natural Science Foundation of China under Grant No.62002103Henan Province Science Foundation for Youths No.222300420058+1 种基金Henan Province Science and Technology Research Project No.232102321064Teacher Education Curriculum Reform Research Priority Project No.2023-JSJYZD-011.
文摘Currently,telecom fraud is expanding from the traditional telephone network to the Internet,and identifying fraudulent IPs is of great significance for reducing Internet telecom fraud and protecting consumer rights.However,existing telecom fraud identification methods based on blacklists,reputation,content and behavioral characteristics have good identification performance in the telephone network,but it is difficult to apply to the Internet where IP(Internet Protocol)addresses change dynamically.To address this issue,we propose a fraudulent IP identification method based on homology detection and DBSCAN(Density-Based Spatial Clustering of Applications with Noise)clustering(DC-FIPD).First,we analyze the aggregation of fraudulent IP geographies and the homology of IP addresses.Next,the collected fraudulent IPs are clustered geographically to obtain the regional distribution of fraudulent IPs.Then,we constructed the fraudulent IP feature set,used the genetic optimization algorithm to determine the weights of the fraudulent IP features,and designed the calculation method of the IP risk value to give the risk value threshold of the fraudulent IP.Finally,the risk value of the target IP is calculated and the IP is identified based on the risk value threshold.Experimental results on a real-world telecom fraud detection dataset show that the DC-FIPD method achieves an average identification accuracy of 86.64%for fraudulent IPs.Additionally,the method records a precision of 86.08%,a recall of 45.24%,and an F1-score of 59.31%,offering a comprehensive evaluation of its performance in fraud detection.These results highlight the DC-FIPD method’s effectiveness in addressing the challenges of fraudulent IP identification.
文摘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With the increased accessibility of global trade information,transaction fraud has become a major worry in global banking and commerce security.The incidence and magnitude of transaction fraud are increasing daily,resulting in significant financial losses for both customers and financial professionals.With improvements in data mining and machine learning in computer science,the capacity to detect transaction fraud is becoming increasingly attainable.The primary goal of this research is to undertake a comparative examination of cutting-edge machine-learning algorithms developed to detect credit card fraud.The research looks at the efficacy of these machine learning algorithms using a publicly available dataset of credit card transactions performed by European cardholders in 2023,comprising around 550,000 records.The study uses this dataset to assess the performance of well-established machine learning models,measuring their accuracy,recall,and F1 score.In addition,the study includes a confusion matrix for all models to aid in evaluation and training time duration.Machin learning models,including Logistic regression,random forest,extra trees,and LGBM,achieve high accuracy and precision in the credit card fraud detection dataset,with a reported accuracy,recall,and F1 score of 1.00 for both classes.
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.
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.
基金funded by the UK EPSRC grant EP/S022503/1 that supports the Centre for Doctoral Training in Cybersecurity at UCL.
文摘Decentralized Finance(DeFi)is a system of financial products and services built and delivered through smart contracts on various blockchains.In recent years,DeFi has gained popularity and market capitalization.However,it has also been connected to crime,particularly various types of securities violations.The lack of Know Your Customer requirements in DeFi poses challenges for governments trying to mitigate potential offenses.This study aims to determine whether this problem is suited to a machine learning approach,namely,whether we can identify DeFi projects potentially engaging in securities violations based on their tokens’smart contract code.We adapted prior works on detecting specific types of securities violations across Ethereum by building classifiers based on features extracted from DeFi projects’tokens’smart contract code(specifically,opcode-based features).Our final model was a random forest model that achieved an 80%F-1 score against a baseline of 50%.Notably,we further explored the code-based features that are the most important to our model’s performance in more detail by analyzing tokens’Solidity code and conducting cosine similarity analyses.We found that one element of the code that our opcode-based features can capture is the implementation of the SafeMath library,although this does not account for the entirety of our features.Another contribution of our study is a new dataset,comprising(a)a verified ground truth dataset for tokens involved in securities violations and(b)a set of legitimate tokens from a reputable DeFi aggregator.This paper further discusses the potential use of a model like ours by prosecutors in enforcement efforts and connects it to a wider legal context.
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.
