Credit-Card-Fraud---PCA | credit card fraud where the variables have PCA

Credit-Card-Fraud---PCA is a Jupyter Notebook library. Credit-Card-Fraud---PCA has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

For many banks, retaining high profitable customers is the number one business goal. Banking fraud, however, poses a significant threat to this goal for different banks. In terms of substantial financial losses, trust and credibility, this is a concerning issue to both banks and customers alike. It has been estimated by Nilson Report that by 2020, banking frauds would account for $30 billion worldwide. With the rise in digital payment channels, the number of fraudulent transactions is also increasing in new and different ways. In the banking industry, credit card fraud detection using machine learning is not only a trend but a necessity for them to put proactive monitoring and fraud prevention mechanisms in place. Machine learning is helping these institutions to reduce time-consuming manual reviews, costly chargebacks and fees as well as denials of legitimate transactions. Understanding and Defining Fraud¶.