With the increase of credit card usage, the volume of credit card misuse also has significantly increased. As a result, financial organizations are working hard on developing and deploying credit card fraud detection methods, in order to adapt to ever-evolving, increasingly sophisticated defrauding strategies and identifying illicit transactions as quickly as possible to protect themselves and their customers. Compounding on the complex nature of such adverse strategies, credit card fraudulent activities are rare events compared to the number of legitimate transactions. Hence, the challenge to develop fraud detection that are accurate and efficient is substantially intensified and, as a consequence, credit card fraud detection has lately become a very active area of research. In this work, we provide a survey of current techniques most relevant to the problem of credit card fraud detection. We carry out our survey in two main parts. In the first part,we focus on studies utilizing classical machine learning models, which mostly employ traditional transnational features to make fraud predictions. These models typically rely on some static physical characteristics, such as what the user knows (knowledge-based method), or what he/she has access to (object-based method). In the second part of our survey, we review more advanced techniques of user authentication, which use behavioral biometrics to identify an individual based on his/her unique behavior while he/she is interacting with his/her electronic devices. These approaches rely on how people behave (instead of what they do), which cannot be easily forged. By providing an overview of current approaches and the results reported in the literature, this survey aims to drive the future research agenda for the community in order to develop more accurate, reliable and scalable models of credit card fraud detection.
翻译:随着信用卡使用量的增加,滥用信用卡的数量也大大增加,因此,金融机构正在努力制定和采用信用卡欺诈检测方法,以适应不断演变的、日益复杂的欺诈策略,并尽快查明非法交易,以保护自己和客户。由于这些不利战略的复杂性,信用卡欺诈活动与合法交易的数量相比是罕见的。因此,开发准确和高效的欺诈检测的挑战大大加剧,因此,信用卡欺诈检测最近已成为一个非常活跃的研究领域。在这项工作中,我们对与信用卡欺诈检测问题最相关的当前技术进行了调查。我们在两个主要部分进行了调查。在第一部分,我们侧重于利用传统的机器学习模型进行研究,这些模型大多使用传统的跨国特征来作出欺诈预测。这些模型通常依赖一些静态的物理特征,例如用户了解(知识型方法),或他/她能够轻易地获得(基于虚拟的方法)的准确性欺诈检测。在我们调查的第二部分,我们审查与信用卡欺诈检测问题最为相关的现有技术。我们通过更先进的用户行为方法来进行这种方法,同时利用他独特的生物鉴别方法来进行个人识别。这些用户行为方式的精确性做法是使用一种独特的生物鉴别方法。