K-Fuse: Credit card fraud detection based on a classification method with a priori class partitioning and a novel feature selection strategy

IF 1.3 4区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Applied Stochastic Models in Business and Industry Pub Date : 2024-05-24 DOI:10.1002/asmb.2868
Mohammed Sabri, Rosanna Verde, Antonio Balzanella
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Abstract

Online transactions have become the dominant and most popular form of online payment in today's digital economy. Due to the growing popularity of e-commerce and the convenience it offers, both consumers and businesses are rapidly adopting online transactions. Notably, credit cards have become one of the most popular and standard online payment methods. However, it should be noted that credit card transactions are not without challenges. In particular, detecting and preventing fraudulent transactions is a major concern of the online payment system. It is difficult to find an effective detection model that can detect the new patterns created by fraudsters, due to the constant evolution of their methods to exploit the vulnerability of current security protocols. These fraud patterns are evolving and may not correspond to existing documented models, leading to a reduction in their identification. In addition, the customer's behavior can affect the model detection as it is susceptible to change based on factors such as economic conditions, trends, and individual circumstances. When consumers deviate from their typical behavior, the model may generate false alerts, thereby reducing its ability to differentiate between legitimate and fraudulent transactions. This article presents a new supervised detection model, called K-Fuse, which introduces an unsupervised phase in order to detect fraud patterns that may correspond to innovative models introduced by fraudsters. K-Fuse is a supervised classification method that fuses three steps consisting of (i) unsupervised clustering to identify hidden patterns of transactions in a dataset, (ii) a novel feature selection criterion based on the unsupervised results, and (iii) supervised classification to exploit the results of clustering and feature selection to predict new transactions as fraudulent or legitimate.

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K-Fuse:基于先验类别划分的分类方法和新型特征选择策略的信用卡欺诈检测
在线交易已成为当今数字经济中最主要、最流行的在线支付方式。由于电子商务的日益普及及其带来的便利,消费者和企业都在迅速采用在线交易。值得注意的是,信用卡已成为最受欢迎的标准在线支付方式之一。然而,应该指出的是,信用卡交易并非没有挑战。特别是,检测和防止欺诈交易是网上支付系统的一个主要问题。由于欺诈者利用当前安全协议漏洞的方法不断演变,因此很难找到一种有效的检测模式来检测欺诈者创造的新模式。这些欺诈模式不断演变,可能与现有的记录模式不符,导致识别率降低。此外,客户的行为也会影响模式检测,因为它很容易受经济状况、趋势和个人情况等因素的影响而发生变化。当消费者偏离其典型行为时,模型可能会产生错误警报,从而降低其区分合法交易和欺诈交易的能力。本文提出了一种新的监督检测模型,称为 K-Fuse,它引入了一个无监督阶段,以检测可能与欺诈者推出的创新模式相对应的欺诈模式。K-Fuse 是一种监督分类方法,它融合了三个步骤,包括(i)无监督聚类,以识别数据集中隐藏的交易模式;(ii)基于无监督结果的新颖特征选择标准;以及(iii)监督分类,利用聚类和特征选择的结果来预测新交易是欺诈还是合法。
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来源期刊
CiteScore
2.70
自引率
0.00%
发文量
67
审稿时长
>12 weeks
期刊介绍: ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process. The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.
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