A novel approach to increase scalability while training machine learning algorithms using Bfloat 16 in credit card fraud detection
This addresses scalability issues for banks and customers in fraud detection, though it appears incremental as it builds on existing methods with a focus on efficiency rather than accuracy.
This research tackles the problem of scalability in credit card fraud detection systems by proposing a technique using Bfloat 16 to train machine learning algorithms with fewer bits, resulting in reduced time and cost for implementation.
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.