A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

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.

Credit Card Fraud Detection on Original European Credit Card Holder Dataset Using Ensemble Machine Learning Technique

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.

A Credit Card Fraud Detection Model Based on Multi-Feature Fusion and Generative Adversarial Network

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.

A Credit Card Fraud Model Prediction Method Based on Penalty Factor Optimization AWTadaboost

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.

Credit Card Fraud Detection Using Weighted Support Vector Machine

Credit card fraudulent data is highly imbalanced, and it has presented an overwhelmingly large portion of nonfraudulent transactions and a small portion of fraudulent transactions. The measures used to judge the veracity of the detection algorithms become critical to the deployment of a model that accurately scores fraudulent transactions taking into account case imbalance, and the cost of identifying a case as genuine when, in fact, the case is a fraudulent transaction. In this paper, a new criterion to judge classification algorithms, which considers the cost of misclassification, is proposed, and several undersampling techniques are compared by this new criterion. At the same time, a weighted support vector machine (SVM) algorithm considering the financial cost of misclassification is introduced, proving to be more practical for credit card fraud detection than traditional methodologies. This weighted SVM uses transaction balances as weights for fraudulent transactions, and a uniformed weight for nonfraudulent transactions. The results show this strategy greatly improve performance of credit card fraud detection.

Credit Card Fraud Detection Based on Machine Learning

In recent years,the rapid development of e-commerce exposes great vulnerabilities in online transactions for fraudsters to exploit.Credit card transactions take a salient role in nowadays’online transactions for its obvious advantages including discounts and earning credit card points.So credit card fraudulence has become a target of concern.In order to deal with the situation,credit card fraud detection based on machine learning is been studied recently.Yet,it is difficult to detect fraudulent transactions due to data imbalance(normal and fraudulent transactions),for which Smote algorithm is proposed in order to resolve data imbalance.The assessment of Light Gradient Boosting Machine model which proposed in the paper depends much on datasets collected from clients’daily transactions.Besides,to prove the new model’s superiority in detecting credit card fraudulence,Light Gradient Boosting Machine model is compared with Random Forest and Gradient Boosting Machine algorithm in the experiment.The results indicate that Light Gradient Boosting Machine model has a good performance.The experiment in credit card fraud detection based on Light Gradient Boosting Machine model achieved a total recall rate of 99%in real dataset and fast feedback,which proves the new model’s efficiency in detecting credit card fraudulence.

Dynamic Programming for Estimating Acceptance Probability of Credit Card Products

Banks have many variants of a product which they can offer to their customers. For example, a credit card can have different interest rates. So determining which variants of a product to offer to the new customers and having some indication on acceptance probability will aid with the profit optimisation for the banks. In this paper, the authors look at a model for maximisation of the profit looking at the past information via implementation of the dynamic programming model with elements of Bayesian updating. Numerical results are presented of multiple variants of a credit card product with the model providing the best offer for the maximum profit and acceptance probability. The product chosen is a credit card with different interest rates.

Predicting Credit Card Transaction Fraud Using Machine Learning Algorithms

Credit card fraud is a wide-ranging issue for financial institutions, involving theft and fraud committed using a payment card. In this paper, we explore the application of linear and nonlinear statistical modeling and machine learning models on real credit card transaction data. The models built are supervised fraud models that attempt to identify which transactions are most likely fraudulent. We discuss the processes of data exploration, data cleaning, variable creation, feature selection, model algorithms, and results. Five different supervised models are explored and compared including logistic regression, neural networks, random forest, boosted tree and support vector machines. The boosted tree model shows the best fraud detection result (FDR = 49.83%) for this particular data set. The resulting model can be utilized in a credit card fraud detection system. A similar model development process can be performed in related business domains such as insurance and telecommunications, to avoid or detect fraudulent activity.

SOM approach for clustering customers using credit card transactions

Purpose–The purpose of this paper is to generate customer clusters using self-organizing map(SOM)approach,a machine learning technique with a big data set of credit card consumptions.The authors aim to use the consumption patterns of the customers in a period of three months deducted from the credit card transactions,specifically the consumption categories(e.g.food,entertainment,etc.).Design/methodology/approach–The authors use a big data set of almost 40,000 credit card transactions to cluster customers.To deal with the size of the data set and the eliminated the required parametric assumptions the authors use a machine learning technique,SOMs.The variables used are grouped into three as demographical variables,categorical consumption variables and summary consumption variables.The variables are first converted to factors using principal component analysis.Then,the number of clusters is specified by k-means clustering trials.Then,clustering with SOM is conducted by only including the demographical variables and allvariables.Then,a comparisonis made and the significance of the variablesis examined by analysis of variance.Findings–The appropriate number of clusters is found to be 8 using k-means clusters.Then,the differences in categorical consumption levels are investigated between the clusters.However,they have been found to be insignificant,whereas the summary consumption variables are found to be significant between the clusters,as well as the demographical variables.Originality/value–The originality of the study is to incorporate the credit card consumption variables of customers to cluster the bank customers.The authors use a big data set and dealt with it with a machine learning technique to deduct the consumption patterns to generate the clusters.Credit card transactions generate a vast amount of data to deduce valuable information.It is mainly used to detect fraud in the literature.To the best of the authors’knowledge,consumption patterns obtained from credit card transaction are first used for clustering the customers in this study.

aLMGAN-信用卡欺诈检测方法

针对信用卡交易数据的不平衡重叠问题,提出一种基于生成对抗网络的端到端一类分类方法。提出一种基于PCA和T_SNE的混合数据降维方法,对清洗后的数据进行特征降维;将降维后的数据送入所提出的基于LSTM和aMLP的生成对抗网络(aLMGAN),提出一种基于闵可夫斯基距离(Minkowski distance)的损失函数(Min-loss)代替原始生成对抗网络中的交叉熵损失函数,对正常交易数据进行单类稳定训练,形成一种特殊特征模式,区分不属于该特征的异常数据。通过使用kaggle上两个真实的公共信用卡交易数据集进行实验,验证了aLMGAN算法的有效性。

基于逻辑回归的个人信用评分卡模型研究

构建有效的个人信用风险评价系统,用以应对潜在的个人信贷风险,这对金融行业和社会公众皆有重要的现实意义。文章首先对数据进行清洗、预处理,然后通过WOE编码分箱、IV值进行变量筛选,构建了逻辑回归模型并基于逻辑回归模型建立了个人信用评分卡模型,该模型可辅助决策者制定合理的授信政策、定价策略以及其他相关业务运营策略。

基于注意力机制优化的WGAN-BiLSTM信用卡欺诈检测方法

信用卡欺诈是银行操作风险的主要来源之一,对信用卡诈骗交易进行准确的检测对于减少银行经济损失具有重要意义。针对信用卡欺诈检测中存在的数据类别不平衡和数据漂移的问题,提出一种基于注意力机制优化的WGAN-BiLSTM信用卡欺诈检测方法。首先引入Wasserstein距离改进生成对抗网络(GAN),将信用卡数据输入至WGAN(Wasserstein GAN)中,在生成器和判别器相互博弈训练下,得到符合目标分布的欺诈样本;然后,构建结合注意力机制的双向长短期记忆(BiLSTM)网络,在正反两个方向上提取信用卡数据的长时依赖关系;最后,通过Softmax层输出分类结果。在欧洲持卡人数据集上的实验结果表明,所提方法能有效提升信用卡欺诈检测效果。

基于XGBoost与LR融合模型的信用卡欺诈检测

随着银行卡业务的不断发展,各种各样的信用卡欺诈方式已经给金融机构带来严重的威胁,使得信用卡欺诈检测成为一个十分紧迫的任务。为解决此问题,提出一种XGBoost与LR融合模型。该模型首先运用XGBoost算法自动进行特征组合和离散化,然后将新构造的特征向量运用在逻辑回归LR模型上,通过XGBoost与LR融合模型进行分类预测。实验结果表明,与经典传统算法相比,提出的XGBoost与LR融合模型具有更好的欺诈检测性能,提高了信用卡欺诈检测的准确率。

涉第三方支付平台信用卡冒用行为的司法定性

随着电商平台成为人们日常生活的组成部分,涉第三方支付平台侵财案件时有发生。此类案件主要有两种类型:一是行为人通过诈骗的方式获得他人的第三方支付平台账号密码,再以他人的身份登录该平台实施侵财行为;二是行为人虽未通过违法或犯罪的方式获取他人第三方支付平台账号密码,但仍以他人的名义登录该平台实施侵财行为。在实务界,在第三方支付平台冒用他人信用卡行为被认定为冒用型信用卡盗窃罪或诈骗罪。文章认为,随着第三方支付平台的发展,实务界逐步承认平台用户被骗的可能性;在第三方支付平台上冒用他人信用卡的行为并不因第三方支付平台的介入而影响信用卡诈骗罪的认定,只是由于行为人最终是否掌控财产而存在犯罪形态的差异。

A Brief Study of Consuming Credit in US and Its Influences on Economy

A serious global financial crisis broke out in 2008 and its negative impact became increasingly felt in China,and continuingly showed its huge destruction power on real economy,which makes us try to figure out and introspect what's the matter with economy.This essay will research the of consuming credit in US and its influences on economy by studying on American's consuming habits and its highly developed financial tools,and analyze the process of the chain effect appear.

基于XGBoost机器学习模型的信用评分卡与基于逻辑回归模型的对比

分别基于逻辑回归模型和XGBoost机器学习模型构建了信用评分卡,比较了两种模型在个人信用评分上的表现,指出XGBoost机器学习模型在“AUC、KS、F1和Accuracy值”上表现更加优秀.首先,从数据的包容性、可解释性以及模型的准确性方面对两个模型进行了对比;其次,使用住房贷款违约风险预测的竞赛数据,分别构建了基于逻辑回归模型和XGBoost机器学习模型的信用评分卡,并使用了AUC、KS、F1和Accuracy来评估这两个模型的分类效果和预测准确程度;最后,通过对比两个模型的评估结果,分析了XGBoost机器学习模型相较于逻辑回归模型更加优秀的原因.结论指出:XGBoost机器学习模型在测试集上的AUC、KS、F1和Accuracy值比逻辑回归模型分别提升了19.9%、17.5%、15.4%和11.9%,其原因在于XGBoost机器学习模型纳入了更多的维度信息、更加科学的缺失值处理方式以及考虑了正则化项的算法原理.

Intelligent Financial Fraud Detection Using Artificial Bee Colony Optimization Based Recurrent Neural Network

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.

基于VAE-GWO-LightGBM的信用卡欺诈检测方法

针对信用卡欺诈检测中样本数据规模大、计算复杂程度高、数据分布极度不平衡等问题,提出一种结合变分自编码器(VAE)、灰狼算法(GWO)和轻量级梯度提升机(LightGBM)的信用卡欺诈检测(VAE-GWO-LightGBM)方法.对原始数据进行预处理,考虑到数据的不平衡性,采用VAE处理训练样本的数据不平衡问题.在扩充后的平衡数据集上,利用LightGBM作为分类器,并通过智能优化算法(GWO算法)对分类器参数进行寻优,进而获得最优分类器,提高欺诈检测性能.最后在信用卡欺诈数据和其他不平衡数据集上进行对比实验验证.结果表明,基于VAE-GWO-LightGBM的融合模型从整体上大大提高了信用卡欺诈检测的识别率.