论文标题
一种新颖的方法来提高使用Bfloat 16在信用卡欺诈检测中训练机器学习算法时提高可伸缩性的方法
A novel approach to increase scalability while training machine learning algorithms using Bfloat 16 in credit card fraud detection
论文作者
论文摘要
如今，随着数字银行业务已成为常态，信用卡的使用已变得非常普遍。随着这一增加，信用卡中的欺诈也对银行和客户都有一个巨大的问题和损失。正常的欺诈检测系统无法检测到欺诈，因为欺诈者以新技术进行欺诈而出现。这创造了使用基于机器学习的软件来检测欺诈的需求。当前，可用的机器学习软件仅着眼于检测欺诈的准确性，但并不关注检测的成本或时间因素。这项研究重点是银行信用卡欺诈检测系统的机器学习可伸缩性。我们已经比较了新提出的技术可用的现有机器学习算法和方法。目的是证明，使用较少的位来训练机器学习算法将导致更可扩展的系统，这将减少时间，并且实施成本也会降低。
The use of credit cards has become quite common these days as digital banking has become the norm. With this increase, fraud in credit cards also has a huge problem and loss to the banks and customers alike. Normal fraud detection systems, are not able to detect the fraud since fraudsters emerge with new techniques to commit fraud. This creates the need to use machine learning-based software to detect frauds. Currently, the machine learning softwares that are available focuses only on the accuracy of detecting frauds but does not focus on the cost or time factors to detect. This research focuses on machine learning scalability for banks' credit card fraud detection systems. We have compared the existing machine learning algorithms and methods that are available with the newly proposed technique. The goal is to prove that using fewer bits for training a machine learning algorithm will result in a more scalable system, that will reduce the time and will also be less costly to implement.
