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 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.

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.

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.

Profitable credit card business empirical analysis of factors

Since 1995, major domestic commercial banks are beginning to have a variety of credit cards issued. However, at present, China＇s relatively low profitability of the credit card business, it accounts for a smaller proportion of total bank income. By means of credit card revenue／cost structure analysis, the authors found spending and overdraft balances affecting credit card business, an important factor in profitability. At the same time, combined with a commercial bank＇s existing statistical data, using SPSS software correlation and regression analysis, the authors found that the key to improve the bank card revenue is to raise China＇s commercial banks, credit card revolving credit utilization, and expand the scale of overdraft balances.

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.

建行信用卡系统全栈国产化改造研究

信用卡业务是业务逻辑最复杂的银行业务之一,其对可用性、可靠性、处理性能要求较高。从技术发展角度来看,多技术栈融合的新型IT架构,符合云计算资源池化的趋势。从业务角度来看,为满足不同的业务需求,同样存在多技术栈融合架构的诉求。然而,多计算架构并非简单实现一个全新的技术栈即可,需要解决架构改造与设计、系统验证、兼容性以及故障切换等一系列问题。面对上述挑战,该文面向金融IT系统高并发、高性能以及高可用需求,介绍了建行面向金融行业的高性能、高可用和高安全可靠的x86、ARM双平台混合架构系统中的设计与思考。通过一系列的代码迁移、应用迁移以及系统垂直优化技术,实现高性能、高可用和高安全的诉求,并在建行得到了大规模、长时间的真实系统验证。

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.

个人信用卡信用风险评价体系与模型研究

分析了国内个人信用卡信用评价的现状和不足;探讨了建立个人信用指标体系的原则和方法;建立了一套包括个人还贷能力和还贷意愿共2大类15个指标的个人信用卡信用风险评价指标体系,并设计了负债情况等3项具有双向影响作用的指标;运用隶属度原理和层次分析法,确定了各类指标的评分函数和权重;建立了个人信用卡信用风险评价模型.确定了划分信用等级的两个阈值,解决了以往信用分级缺乏依据的问题.

基于Spring MVC及MyBatis的Web应用框架研究

基于EJB等重量级的Web应用框架存在很多问题,如性能差、复杂度高、代码复用率低等,提出了一种B/S结构与C/S结构相结合,采用Spring MVC设计模式和MyBatis为基础的Web应用框架,并对该框架的结构、组成等内容进行分析和研究。以TOPCard信用卡业务系统为应用实例,说明Spring MVC和MyBatis在Web系统中的应用。通过实验结果分析,基于Spring MVC及MyBatis的Web应用框架研究,可以解决性能差、复杂度高、代码复用率低等问题。

论消费信用卡透支风险的法律监管制度建设

消费信用卡给发卡行带来新的利润增长点的同时 ,也对其造成一定的风险。风险背后的深层原因是制度的缺陷。我国信用卡业务的制度缺陷主要体现在银行对持卡人拖欠透支款缺乏有效催款措施、健全的个人信用制度、信用卡担保保证制度以及完善、统一的消费信贷法规。要改变这一现状 ,促进信用卡的健康发展 ,我国应在上述几个方面加强建设步伐 ,逐步建立一个有法可依的消费信贷法律制度体系。

基于ADO的信用卡收银系统的研究与实现

通过面向对象的分析方法,研究并实现了信用卡收银系统;该系统采用Delphi编程,通过ADO接口,利用磁卡实现了收银系统的强大功能;它有利于商业的智能化管理,提高企业的经济效益和社会效率。

基于BBC与价值链风险分析的农户信用评价指标体系探析

主要针对"农民工返乡创业"这一社会现象,金融机构对其提供资金支持时,对农户信用进行评价为研究目的。通过对现有农户信用评价的研究分析,发现在构建农户信用评价指标体系时,所采用的方法主要以"5C"或"4C"分析法为模型,方法比较单一,所以,提出了基于价值链风险分析与平衡积分卡方法的农户信用评价指标体系。以个人平衡积分卡为框架,通过对农产品价值链风险分析,找出影响农户偿还贷款的潜在因素,根据这些因素来选取评价指标,构建农户信用评价指标体系。新的农户信用评价指标体系与传统的评价体系相比,更为全面、合理。

遗传算法在信用卡审批中的应用

论述了遗传算法在信用卡审批一类问题中应用的工作原理。对具体采用的 GABIL系统应用方法和流程做了详细描述。针对信用卡审批问题实施并运行了该系统 ,说明了遗传算法在信用卡审批问题中应用的可行性 ,并进一步讨论了该系统存在的问题及其有待改进的方面。

信用卡发展中的问题与对策思考

信用卡作为现代社会一种先进的金融结算工具,从它诞生之日起就给人们带来便捷。信用卡在我国经过二十多年的发展,已被广大群众所接受。但信用卡发展中存在的问题也日益显露,主要表现为:商业银行盲目追求信用卡的发行规模,存在套现和诈骗现象,信用卡的不良贷款率上升,睡眠卡比例过高等。对此,政府有关部门应加大对信用卡产业的扶持,推动个人征信体系的完善,商业银行应该加强自身建设,强化内部风险管理,为信用卡产业健康、稳定地发展创造良好的内外部环境,并使之成为我国商业银行一个新的利润增长点。

商洛市行政事业单位公务卡制度改革的路径思考

自2009年底商洛市推行公务卡结算制度改革以来,行政事业单位在财政授权支付业务,保障资金安全,公务支出透明度,加强公共财政管理与监督,方便预算单位用款,预防防腐体系建设等方面取得了一定成效,但在实施的过程中仍存在一些问题。因此需要进一步深化公务卡结算制度改革。

我国银行卡市场发展状况与对策研究

银行卡是具有存款、取款、转账结算、消费等功能的信用支付工具。我国银行卡市场经过二十几年的发展,取得了重大成就。本文从我国银行卡市场的发展状况入手,分析了发展中存在的问题,在此基础上提出了加快我国银行卡市场快速健康发展的对策建议。

我国单用途预付卡信用治理:逻辑、体系与机制

预付消费本质上是一种商业信用交易,单用途预付卡是预付消费的重要载体。近年来,在新冠肺炎疫情反复冲击和经济持续下行的大背景下,单用途预付卡风险不断暴露,其治理面临严重挑战。以单用途预付卡交易的本质为逻辑起点,分析其商业信用交易的模式和特征,构建单用途预付卡信用治理的目标和框架体系,并系统总结已有的治理实践与创新,设计单用途预付卡的信用治理机制,提出相应的政策建议。