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.

本研究着重研究使用机器学习检测银行信用卡欺诈的可扩展性。我们比较了现有的机器学习算法和方法与新提出的技术，并证明采用较少的比特数来训练机器学习算法将导致一个更具可扩展性，时间和成本更少的系统。