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Analysis on credit card fraud identification techniques based on KNN and outlier detection

机译:基于KNN和异常值检测的信用卡欺诈识别技术分析

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Popular payment mode accepted both offline and online is credit card that provides cashless transaction. It is easy, convenient and trendy to make payments and other transactions. Credit card fraud is also growing along with the development in technology. It can also be said that economic fraud is drastically increasing in the global communication improvement. It is being recorded every year that the loss due to these fraudulent acts is billions of dollars. These activities are carried out so elegantly so it is similar to genuine transactions. Hence simple pattern related techniques and other less complex methods are really not going to work. Having an efficient method of fraud detection has become a need for all banks in order to minimize chaos and bring order in place. There are several techniques like Machine learning, Genetic Programming, fuzzy logic, sequence alignment, etc are used for detecting credit card fraudulent transactions. Along with these techniques, KNN algorithm and outlier detection methods are implemented to optimize the best solution for the fraud detection problem. These approaches are proved to minimize the false alarm rates and increase the fraud detection rate. Any of these methods can be implemented on bank credit card fraud detection system, to detect and prevent the fraudulent transaction.
机译:热门付款方式接受离线和在线是提供无现金交易的信用卡。付款和其他交易很容易,方便,时尚。信用卡欺诈以及技术的发展也在增长。还可以说,在全球沟通改善方面,经济欺诈造成急剧增加。每年正在记录,由于这些欺诈行为导致的损失是数十亿美元。这些活动如此优雅地进行,因此它类似于真正的交易。因此,简单的模式相关技术和其他复杂的方法实际上不起作用。具有有效的欺诈检测方法已成为所有银行的需求,以便最大限度地减少混乱并将订单带入到位。有几种技术,如机器学习,遗传编程,模糊逻辑,序列对齐等,用于检测信用卡欺诈交易。除了这些技术之外,还实现了KNN算法和异常值检测方法,以优化欺诈检测问题的最佳解决方案。证明这些方法可最大限度地减少误报率并提高欺诈检测率。任何这些方法都可以在银行信用卡欺诈检测系统上实施,以检测和防止欺诈性交易。

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