论文标题
使用人工智能方法在信用卡交易上检测欺诈的方法
Approaches to Fraud Detection on Credit Card Transactions Using Artificial Intelligence Methods
论文作者
论文摘要
信用卡欺诈是世界上几乎所有行业的持续问题，每年为全球经济筹集数百万美元。因此，为了检测该行业中的这些欺诈行为，有许多研究完成或进行程序。这些研究通常使用基于规则或新颖的人工智能方法来找到合格的解决方案。本文的最终目的是使用人工智能和机器学习技术总结最新的欺诈检测方法。总而言之，我们将分类常见问题，例如数据集不平衡的数据集，实时工作场景和功能工程挑战，几乎所有研究工作都会遇到，并确定解决方案的一般方法。发生不平衡的数据集问题是因为合法交易的数量比欺诈性交易的数量高得多，而应用合适的功能工程非常重要，因为从行业获得的功能受到限制，并且应用功能工程方法并改革数据集至关重要。同样，将检测系统调整为实时情况是一个挑战，因为有限时间内的信用卡交易数量很高。此外，我们将讨论评估指标和机器学习方法如何在每项研究之间进行区分。
Credit card fraud is an ongoing problem for almost all industries in the world, and it raises millions of dollars to the global economy each year. Therefore, there is a number of research either completed or proceeding in order to detect these kinds of frauds in the industry. These researches generally use rule-based or novel artificial intelligence approaches to find eligible solutions. The ultimate goal of this paper is to summarize state-of-the-art approaches to fraud detection using artificial intelligence and machine learning techniques. While summarizing, we will categorize the common problems such as imbalanced dataset, real time working scenarios, and feature engineering challenges that almost all research works encounter, and identify general approaches to solve them. The imbalanced dataset problem occurs because the number of legitimate transactions is much higher than the fraudulent ones whereas applying the right feature engineering is substantial as the features obtained from the industries are limited, and applying feature engineering methods and reforming the dataset is crucial. Also, adapting the detection system to real time scenarios is a challenge since the number of credit card transactions in a limited time period is very high. In addition, we will discuss how evaluation metrics and machine learning methods differentiate among each research.
