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.

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.

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.

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.

Real-Time Fraud Detection Using Machine Learning

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.

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.

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.

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。

基于特征提取和集成学习的个人信用评分方法

在大数据蓬勃发展的今天,信息经济已经深入社会方方面面,个人信用体系建设的重要性越发突出。而传统的信用体系存在覆盖率不足、评价特征维度高、数据孤岛等问题,为了解决以上问题,提出一种基于特征提取和Stacking集成学习的个人信用评分方法(PSL-Stacking)。方法首先利用Pearson和Spearman系数对数据进行初始化分析剔除不相关数据,利用LightGBM算法进行特征选择,减少冗余特征对模型的影响;其次选取XGboost、LightGBM、Random Forest以及Huber回归等算法,利用Stacking集成学习技术构造个人信用评分模型。最后,以某电信数据为研究对象,对该上述模型的个人信用评分能力进行验证。实验结果得出上述模型具有很好的预测能力,能够准确的对用户信用进行评分,有效降低企业遭受金融欺诈、团伙套利等问题的风险。

CTGANBoost:基于CTGAN与Boosting的信贷欺诈检测研究

在金融行业中,信贷欺诈检测是一项重要的工作,能够为银行和消金机构减少大量的经济损失。然而,信贷数据中存在类别不平衡和正负样本特征重叠的问题,导致少数类识别灵敏度低且不同类别数据区分度低。针对这些问题,提出一种面向信贷欺诈检测的CTGANBoost方法。首先,在AdaBoost(Adaptive Boosting)方法的每一轮Boosting迭代中,引入基于类别标签信息约束的CTGAN(Conditional Tabular Generative Adversarial Network)方法学习特征分布,进行少数类数据增强工作;其次,基于CTGAN合成的增强数据集,设计了权重归一化方法,确保在样本加权过程中保持原始数据集的分布特征和相对权重。在3个开源数据集上的实验结果表明,CTGANBoost方法的表现均优于其他主流的信贷欺诈检测方法,AUC值提升了0.5%~2.0%,F1值提升了0.6%~1.8%,验证了CTGANBoost方法的有效性和泛化能力。

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。

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。

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。

基于机器学习的信用卡交易欺诈检测研究综述

机器学习在信用卡交易检测中有其特殊性,面对的环境更为复杂。由于有人的智力介入,战胜信用卡交易欺诈,其挑战性比人脸识别、无人驾驶等工程问题的难度更高,照搬工程学科的机器学习方法往往会失败。综述了2000年以来基于机器学习的信用卡欺诈检测研究历程,辨析了该领域的研究范畴、应用场景、技术流派等相关概念及其联系;解构了机器学习欺诈识别的一般性研究架构,从特征工程、模型算法、评价指标三个环节归纳总结了领域内研究的最新进展;从数据集是否具备标签角度,着重列举了面向欺诈识别的有监督的、无监督和半监督三类主流机器学习模型,讨论了这些模型的出发点、核心思想、求解方法以及优缺点;还分析了强化学习模型模拟欺诈者与机构之间的动态博弈过程;探讨了机器学习面临的海量数据、样本偏斜和概念漂移三大难点问题,并汇集整理了缓解这些问题的最新进展;总结了面向欺诈检测的机器学习研究目前存在的局限、争议和挑战,并为未来的研究方向提供趋势分析与建议。

基于三阶段集成学习的信用卡欺诈检测研究

信用卡欺诈是银行业面临的严峻问题,全球每年因信用卡欺诈造成的损失高达数十亿美元。然而信用卡交易数据存在特征冗余和样本不平衡的问题,这无疑增加了模型对少数欺诈交易的检测难度。针对以上问题,本文提出了三阶段集成学习模型“FS-IFKK-Stacking”:基于FS方法的特征选择、基于IFKK方法的不平衡数据处理和基于Stacking方法的异构模型集成。该模型同时解决了由于特征冗余和样本不均衡性导致的过拟合问题,能够更加准确地检测信用卡欺诈交易。基于Kaggle欧洲信用卡交易数据的实验表明:本文提出的“FS-IFKK-Stacking”模型对信用卡欺诈的检测效果明显优于基于原始样本训练的单类模型:在AUC提升0.44%的同时,对欺诈交易的召回率提升了3.27%。

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.

基于动态集成选择算法的信用卡审批异常检测

针对数据集标签缺失且类别分布极不平衡的信用卡欺诈检测问题,提出一种基于动态集成选择算法的信用卡审批异常检测模型DES-HBOS(Dynamic Ensemble Selection based on Histogram-based Outlier Score)。首先,利用无监督异常检测算法构造训练集客户的伪标签;然后,确定待测客户能力区域,根据Pearson相关系数评估分类器性能;最后,选择一组较优的分类器对待测客户进行集成。在真实信用卡客户数据集上的实验表明,与其他6种经典异常检测模型相比,DES-HBOS的Recall更高,能将更多欺诈客户识别出来。在4个不平衡数据集上进行对比实验,实验结果表明与HBOS相比,DES-HBOS检测异常能力更强。

基于异构信息网络的信贷反欺诈研究

近年来,移动终端设备的数字化程度陡升,信贷行业的欺诈行为呈现出动态发展、行为隐蔽和专业伪装等新特点,海量数据的跨量级增长为传统反欺诈算法的有效性和计算效率都带来了不小的挑战。因此,为了充分学习信贷场景中不同实体间的交互信息,降低算法计算消耗以使其适用于大规模图数据任务,提出了基于异构信息网络的特异群组挖掘算法BKH-(Bron-Kerbosh-H-II),即首先针对源数据中的信贷实体及实体间的关系进行界定和分类,并将不同实体间的相似度作为关系权重,以此构建信贷异构信息网络,对该网络采取了两阶段的基于H图的极大团枚举算法,用于挖掘特异群组,最终通过局部特征工程修正划分得到潜在的欺诈群体,经实验证明,BKH-II在4种评价指标上的准确度分别为NMI=0.983,NRI=0.96,F-score=0.943,Omega=0.95,并表现出了良好的泛化性和较低的计算复杂性。