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Image Recognition Model of Fraudulent Websites Based on Image Leader Decision and Inception-V3 Transfer Learning
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作者 Shengli Zhou Cheng Xu +3 位作者 Rui Xu Weijie Ding Chao Chen Xiaoyang Xu 《China Communications》 SCIE CSCD 2024年第1期215-227,共13页
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
关键词 fraudulent website image leaders telecom fraud transfer learning
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DC-FIPD: Fraudulent IP Identification Method Based on Homology Detection
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作者 Yuanyuan Ma Ang Chen +3 位作者 Cunzhi Hou Ruixia Jin Jinghui Zhang Ruixiang Li 《Computers, Materials & Continua》 SCIE EI 2024年第11期3301-3323,共23页
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
关键词 fraudulent IP identification homology detection CLUSTERING genetic optimization algorithm telecom fraud identification
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The Effects of Competence and Auditor Training on Fraud Detection Within Multinational Companies in Sub-Saharan Africa
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作者 Ivan Djossa Tchokoté Joëlle Tsobze Tiomeguim 《Journal of Modern Accounting and Auditing》 2024年第1期1-13,共13页
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
关键词 fraud legal audit fraud detection MULTINATIONALS Sub-Saharan Africa
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NFA:A neural factorization autoencoder based online telephony fraud detection
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作者 Abdul Wahid Mounira Msahli +1 位作者 Albert Bifet Gerard Memmi 《Digital Communications and Networks》 SCIE CSCD 2024年第1期158-167,共10页
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
关键词 Telecom industry Streaming anomaly detection fraud analysis Factorization machine Real-time system Security
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Credit Card Fraud Detection Using Improved Deep Learning Models
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作者 Sumaya S.Sulaiman Ibraheem Nadher Sarab M.Hameed 《Computers, Materials & Continua》 SCIE EI 2024年第1期1049-1069,共21页
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
关键词 Card fraud detection hyperparameter tuning deep learning autoencoder convolution neural network long short-term memory RESAMPLING
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A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection
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作者 Honghao Zhu MengChu Zhou +1 位作者 Yu Xie Aiiad Albeshri 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第2期377-390,共14页
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
关键词 Credit card fraud detection(CCFD) dandelion algorithm(DA) feature selection normal sowing operator
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An Anti-Fraud Policy:A Theoretical Framework for a Prosperity Tripod of Massive Data,Blockchain,and AI
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作者 Reza G.Hamzaee Maryam Salimi 《Management Studies》 2024年第2期102-123,共22页
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
关键词 fraud AI data VAR
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Real-Time Fraud Detection Using Machine Learning
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作者 Benjamin Borketey 《Journal of Data Analysis and Information Processing》 2024年第2期189-209,共21页
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
关键词 Credit Card fraud Detection Machine Learning SHAP Values Random Forest
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A Credit Card Fraud Detection Model Based on Multi-Feature Fusion and Generative Adversarial Network 被引量:1
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作者 Yalong Xie Aiping Li +2 位作者 Biyin Hu Liqun Gao Hongkui Tu 《Computers, Materials & Continua》 SCIE EI 2023年第9期2707-2726,共20页
Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to cr... Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD,which significantly impact classification models’performance.To address these issues,this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks(MFGAN).The MFGAN model consists of two modules:a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space,and a balance module based on the generative adversarial network to decrease the class imbalance ratio.The effectiveness of theMFGAN model is validated on two actual credit card datasets.The impacts of different class balance ratios on the performance of the four resamplingmodels are analyzed,and the contribution of the two different modules to the performance of the MFGAN model is investigated via ablation experiments.Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of recall,F1,and Area Under the Curve(AUC)metrics,which means that the MFGAN model can help banks find more fraudulent transactions and reduce fraud losses. 展开更多
关键词 Credit card fraud detection imbalanced classification feature fusion generative adversarial networks anti-fraud systems
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Multimodal Fraudulent Website Identification Method Based on Heterogeneous Model Ensemble
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作者 Shengli Zhou Linqi Ruan +1 位作者 Qingyang Xu Mincheng Chen 《China Communications》 SCIE CSCD 2023年第5期263-274,共12页
The feature analysis of fraudulent websites is of great significance to the combat,prevention and control of telecom fraud crimes.Aiming to address the shortcomings of existing analytical approaches,i.e.single dimensi... The feature analysis of fraudulent websites is of great significance to the combat,prevention and control of telecom fraud crimes.Aiming to address the shortcomings of existing analytical approaches,i.e.single dimension and venerability to anti-reconnaissance,this paper adopts the Stacking,the ensemble learning algorithm,combines multiple modalities such as text,image and URL,and proposes a multimodal fraudulent website identification method by ensembling heterogeneous models.Crossvalidation is first used in the training of multiple largely different base classifiers that are strong in learning,such as BERT model,residual neural network(ResNet)and logistic regression model.Classification of the text,image and URL features are then performed respectively.The results of the base classifiers are taken as the input of the meta-classifier,and the output of which is eventually used as the final identification.The study indicates that the fusion method is more effective in identifying fraudulent websites than the single-modal method,and the recall is increased by at least 1%.In addition,the deployment of the algorithm to the real Internet environment shows the improvement of the identification accuracy by at least 1.9%compared with other fusion methods. 展开更多
关键词 telecom fraud crime fraudulent website data fusion deep learning
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A Credit Card Fraud Model Prediction Method Based on Penalty Factor Optimization AWTadaboost 被引量:1
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作者 Wang Ning Siliang Chen +2 位作者 Fu Qiang Haitao Tang Shen Jie 《Computers, Materials & Continua》 SCIE EI 2023年第3期5951-5965,共15页
With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detec... With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detection has started to use this method in large numbers, but thetraditional Adaboost is prone to overfitting in the presence of noisy samples.Therefore, in order to alleviate this phenomenon, this paper proposes a newidea: using the number of consecutive sample misclassifications to determinethe noisy samples, while constructing a penalty factor to reconstruct thesample weight assignment. Firstly, the theoretical analysis shows that thetraditional Adaboost method is overfitting in a noisy training set, which leadsto the degradation of classification accuracy. To this end, the penalty factorconstructed by the number of consecutive misclassifications of samples isused to reconstruct the sample weight assignment to prevent the classifierfrom over-focusing on noisy samples, and its reasonableness is demonstrated.Then, by comparing the penalty strength of the three different penalty factorsproposed in this paper, a more reasonable penalty factor is selected.Meanwhile, in order to make the constructed model more in line with theactual requirements on training time consumption, the Adaboost algorithmwith adaptive weight trimming (AWTAdaboost) is used in this paper, so thepenalty factor-based AWTAdaboost (PF_AWTAdaboost) is finally obtained.Finally, PF_AWTAdaboost is experimentally validated against other traditionalmachine learning algorithms on credit card fraud datasets and otherdatasets. The results show that the PF_AWTAdaboost method has betterperformance, including detection accuracy, model recall and robustness, thanother methods on the credit card fraud dataset. And the PF_AWTAdaboostmethod also shows excellent generalization performance on other datasets.From the experimental results, it is shown that the PF_AWTAdaboost algorithmhas better classification performance. 展开更多
关键词 Credit card fraud noisy samples penalty factors AWTadaboost algorithm
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Enhanced E-commerce Fraud Prediction Based on a Convolutional Neural Network Model
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作者 Sumin Xie Ling Liu +3 位作者 Guang Sun Bin Pan Lin Lang Peng Guo 《Computers, Materials & Continua》 SCIE EI 2023年第4期1107-1117,共11页
The rapidly escalating sophistication of e-commerce fraud in recent years has led to an increasing reliance on fraud detection methods based on machine learning.However,fraud detection methods based on conventional ma... The rapidly escalating sophistication of e-commerce fraud in recent years has led to an increasing reliance on fraud detection methods based on machine learning.However,fraud detection methods based on conventional machine learning approaches suffer from several problems,including an excessively high number of network parameters,which decreases the efficiency and increases the difficulty of training the network,while simultaneously leading to network overfitting.In addition,the sparsity of positive fraud incidents relative to the overwhelming proportion of negative incidents leads to detection failures in trained networks.The present work addresses these issues by proposing a convolutional neural network(CNN)framework for detecting ecommerce fraud,where network training is conducted using historical market transaction data.The number of network parameters reduces via the local perception field and weight sharing inherent in the CNN framework.In addition,this deep learning framework enables the use of an algorithmiclevel approach to address dataset imbalance by focusing the CNN model on minority data classes.The proposed CNN model is trained and tested using a large public e-commerce service dataset from 2018,and the test results demonstrate that the model provides higher fraud prediction accuracy than existing state-of-the-art methods. 展开更多
关键词 CNN model detection E-COMMERCE fraud
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The Detection of Fraudulent Smart Contracts Based on ECA-EfficientNet and Data Enhancement
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作者 Xuanchen Zhou Wenzhong Yang +3 位作者 Liejun Wang Fuyuan Wei KeZiErBieKe HaiLaTi Yuanyuan Liao 《Computers, Materials & Continua》 SCIE EI 2023年第12期4073-4087,共15页
With the increasing popularity of Ethereum,smart contracts have become a prime target for fraudulent activities such as Ponzi,honeypot,gambling,and phishing schemes.While some researchers have studied intelligent frau... With the increasing popularity of Ethereum,smart contracts have become a prime target for fraudulent activities such as Ponzi,honeypot,gambling,and phishing schemes.While some researchers have studied intelligent fraud detection,most research has focused on identifying Ponzi contracts,with little attention given to detecting and preventing gambling or phishing contracts.There are three main issues with current research.Firstly,there exists a severe data imbalance between fraudulent and non-fraudulent contracts.Secondly,the existing detection methods rely on diverse raw features that may not generalize well in identifying various classes of fraudulent contracts.Lastly,most prior studies have used contract source code as raw features,but many smart contracts only exist in bytecode.To address these issues,we propose a fraud detection method that utilizes Efficient Channel Attention EfficientNet(ECA-EfficientNet)and data enhancement.Our method begins by converting bytecode into Red Green Blue(RGB)three-channel images and then applying channel exchange data enhancement.We then use the enhanced ECA-EfficientNet approach to classify fraudulent smart contract RGB images.Our proposed method achieves high F1-score and Recall on both publicly available Ponzi datasets and self-built multi-classification datasets that include Ponzi,honeypot,gambling,and phishing smart contracts.The results of the experiments demonstrate that our model outperforms current methods and their variants in Ponzi contract detection.Our research addresses a significant problem in smart contract security and offers an effective and efficient solution for detecting fraudulent contracts. 展开更多
关键词 fraud detection smart contract ECA-EfficientNet Ethereum
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Intelligent Financial Fraud Detection Using Artificial Bee Colony Optimization Based Recurrent Neural Network
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作者 T.Karthikeyan M.Govindarajan V.Vijayakumar 《Intelligent Automation & Soft Computing》 SCIE 2023年第8期1483-1498,共16页
Frauds don’t follow any recurring patterns.They require the use of unsupervised learning since their behaviour is continually changing.Fraud-sters have access to the most recent technology,which gives them the abilit... Frauds don’t follow any recurring patterns.They require the use of unsupervised learning since their behaviour is continually changing.Fraud-sters have access to the most recent technology,which gives them the ability to defraud people through online transactions.Fraudsters make assumptions about consumers’routine behaviour,and fraud develops swiftly.Unsupervised learning must be used by fraud detection systems to recognize online payments since some fraudsters start out using online channels before moving on to other techniques.Building a deep convolutional neural network model to identify anomalies from conventional competitive swarm optimization pat-terns with a focus on fraud situations that cannot be identified using historical data or supervised learning is the aim of this paper Artificial Bee Colony(ABC).Using real-time data and other datasets that are readily available,the ABC-Recurrent Neural Network(RNN)categorizes fraud behaviour and compares it to the current algorithms.When compared to the current approach,the findings demonstrate that the accuracy is high and the training error is minimal in ABC_RNN.In this paper,we measure the Accuracy,F1 score,Mean Square Error(MSE)and Mean Absolute Error(MAE).Our system achieves 97%accuracy,92%precision rate and F1 score 97%.Also we compare the simulation results with existing methods. 展开更多
关键词 fraud activity OPTIMIZATION deep learning CLASSIFICATION online transaction neural network credit card
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A Multi-Module Machine Learning Approach to Detect Tax Fraud
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作者 N.Alsadhan 《Computer Systems Science & Engineering》 SCIE EI 2023年第7期241-253,共13页
Tax fraud is one of the substantial issues affecting governments around the world.It is defined as the intentional alteration of information provided on a tax return to reduce someone’s tax liability.This is done by ... Tax fraud is one of the substantial issues affecting governments around the world.It is defined as the intentional alteration of information provided on a tax return to reduce someone’s tax liability.This is done by either reducing sales or increasing purchases.According to recent studies,governments lose over$500 billion annually due to tax fraud.A loss of this magnitude motivates tax authorities worldwide to implement efficient fraud detection strategies.Most of the work done in tax fraud using machine learning is centered on supervised models.A significant drawback of this approach is that it requires tax returns that have been previously audited,which constitutes a small percentage of the data.Other strategies focus on using unsupervised models that utilize the whole data when they search for patterns,though ignore whether the tax returns are fraudulent or not.Therefore,unsupervised models are limited in their usefulness if they are used independently to detect tax fraud.The work done in this paper focuses on addressing such limitations by proposing a fraud detection framework that utilizes supervised and unsupervised models to exploit the entire set of tax returns.The framework consists of four modules:A supervised module,which utilizes a tree-based model to extract knowledge from the data;an unsupervised module,which calculates anomaly scores;a behavioral module,which assigns a compliance score for each taxpayer;and a prediction module,which utilizes the output of the previous modules to output a probability of fraud for each tax return.We demonstrate the effectiveness of our framework by testing it on existent tax returns provided by the Saudi tax authority. 展开更多
关键词 Tax fraud feature engineering applied machine learning
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CDR2IMG:A Bridge from Text to Image in Telecommunication Fraud Detection
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作者 Zhen Zhen Jian Gao 《Computer Systems Science & Engineering》 SCIE EI 2023年第10期955-973,共19页
Telecommunication fraud has run rampant recently worldwide.However,previous studies depend highly on expert knowledge-based feature engineering to extract behavior information,which cannot adapt to the fastchanging mo... Telecommunication fraud has run rampant recently worldwide.However,previous studies depend highly on expert knowledge-based feature engineering to extract behavior information,which cannot adapt to the fastchanging modes of fraudulent subscribers.Therefore,we propose a new taxonomy that needs no hand-designed features but directly takes raw Call DetailRecords(CDR)data as input for the classifier.Concretely,we proposed a fraud detectionmethod using a convolutional neural network(CNN)by taking CDR data as images and applying computer vision techniques like image augmentation.Comprehensive experiments on the real-world dataset from the 2020 Digital Sichuan Innovation Competition show that our proposed method outperforms the classic methods in many metrics with excellent stability in both the changes of quantity and the balance of samples.Compared with the state-of-the-art method,the proposed method has achieved about 89.98%F1-score and 92.93%AUC,improving 2.97%and 0.48%,respectively.With the augmentation technique,the model’s performance can be further enhanced by a 91.09%F1-score and a 94.49%AUC respectively.Beyond telecommunication fraud detection,our method can also be extended to other text datasets to automatically discover new features in the view of computer vision and its powerful methods. 展开更多
关键词 Telecommunication fraud detection call detail records convolutional neural network
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Online payment fraud:from anomaly detection to risk management
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作者 Paolo Vanini Sebastiano Rossi +1 位作者 Ermin Zvizdic Thomas Domenig 《Financial Innovation》 2023年第1期1788-1812,共25页
Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account.Successfully preventing this requires the detection of as many fraudsters as possible,wit... Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account.Successfully preventing this requires the detection of as many fraudsters as possible,without producing too many false alarms.This is a challenge for machine learning owing to the extremely imbalanced data and complexity of fraud.In addition,classical machine learning methods must be extended,minimizing expected financial losses.Finally,fraud can only be combated systematically and economically if the risks and costs in payment channels are known.We define three models that overcome these challenges:machine learning-based fraud detection,economic optimization of machine learning results,and a risk model to predict the risk of fraud while considering countermeasures.The models were tested utilizing real data.Our machine learning model alone reduces the expected and unexpected losses in the three aggregated payment channels by 15%compared to a benchmark consisting of static if-then rules.Optimizing the machine-learning model further reduces the expected losses by 52%.These results hold with a low false positive rate of 0.4%.Thus,the risk framework of the three models is viable from a business and risk perspective. 展开更多
关键词 Payment fraud risk management Anomaly detection Ensemble models Integration of machine learning and statistical risk modelling Economic optimization machine learning outputs
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Machine Learning-Based Approach for Identification of SIM Box Bypass Fraud in a Telecom Network Based on CDR Analysis: Case of a Fixed and Mobile Operator in Cameroon
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作者 Eric Michel Deussom Djomadji Kabiena Ivan Basile +2 位作者 Tchapga Tchito Christian Ferry Vaneck Kouam Djoko Michael Ekonde Sone 《Journal of Computer and Communications》 2023年第2期142-157,共16页
In the telecommunications sector, companies suffer serious damages due to fraud, especially in Africa. One of the main types of fraud is SIM box bypass fraud, which includes using SIM cards to divert incoming internat... In the telecommunications sector, companies suffer serious damages due to fraud, especially in Africa. One of the main types of fraud is SIM box bypass fraud, which includes using SIM cards to divert incoming international calls from mobile operators creating massive losses of revenue. In order to provide a solution to these shortcomings that apply almost to all network operators, we developed intelligent algorithms that exploit huge amounts of data from mobile operators and that detect fraud by analyzing CDRs from voice calls. In this paper we used three classification techniques: Random Forest, Support Vector Machine (SVM) and XGBoost to detect this type of fraud;we compared the performance of these different algorithms to evaluate the model by using data collected from an operator’s network in Cameroon. The algorithm that produced a better performance was the Random Forest with 92% accuracy, so we effectuated the detection of existing fraudulent numbers on the telecommunications operator’s network. 展开更多
关键词 CDR fraud Detection Machine Learning Voice Calls
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Credit Card Fraud Detection on Original European Credit Card Holder Dataset Using Ensemble Machine Learning Technique
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作者 Yih Bing Chu Zhi Min Lim +3 位作者 Bryan Keane Ping Hao Kong Ahmed Rafat Elkilany Osama Hisham Abusetta 《Journal of Cyber Security》 2023年第1期33-46,共14页
The proliferation of digital payment methods facilitated by various online platforms and applications has led to a surge in financial fraud,particularly in credit card transactions.Advanced technologies such as machin... The proliferation of digital payment methods facilitated by various online platforms and applications has led to a surge in financial fraud,particularly in credit card transactions.Advanced technologies such as machine learning have been widely employed to enhance the early detection and prevention of losses arising frompotentially fraudulent activities.However,a prevalent approach in existing literature involves the use of extensive data sampling and feature selection algorithms as a precursor to subsequent investigations.While sampling techniques can significantly reduce computational time,the resulting dataset relies on generated data and the accuracy of the pre-processing machine learning models employed.Such datasets often lack true representativeness of realworld data,potentially introducing secondary issues that affect the precision of the results.For instance,undersampling may result in the loss of critical information,while over-sampling can lead to overfitting machine learning models.In this paper,we proposed a classification study of credit card fraud using fundamental machine learning models without the application of any sampling techniques on all the features present in the original dataset.The results indicate that Support Vector Machine(SVM)consistently achieves classification performance exceeding 90%across various evaluation metrics.This discovery serves as a valuable reference for future research,encouraging comparative studies on original dataset without the reliance on sampling techniques.Furthermore,we explore hybrid machine learning techniques,such as ensemble learning constructed based on SVM,K-Nearest Neighbor(KNN)and decision tree,highlighting their potential advancements in the field.The study demonstrates that the proposed machine learning models yield promising results,suggesting that pre-processing the dataset with sampling algorithm or additional machine learning technique may not always be necessary.This research contributes to the field of credit card fraud detection by emphasizing the potential of employing machine learning models directly on original datasets,thereby simplifying the workflow and potentially improving the accuracy and efficiency of fraud detection systems. 展开更多
关键词 Machine learning credit card fraud ensemble learning non-sampled dataset hybrid AI models European credit card holder
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Determining of Employee Frauds in Forensic Accounting: A Research on Forensic Cases in the City of Kars in Turkey
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作者 Duygu Anil Keskin Seyhan Goksu Ozturk 《Journal of Modern Accounting and Auditing》 2013年第6期729-738,共10页
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
关键词 forensic accounting M41 fraud M49 fraud detection M42 employee frauds
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