Credit-Card-Fraud-Detection

Using-different-Supervised-Model-for-Undersampled-and-Oversampled-Data

This project is based on supervised machine learning where you will be predicting whether a credit card transaction is original transaction or fraud transaction based on various parameters. This is a classification problem.

Problem Statement

The problem statement chosen for this project is to predict fraudulent credit card transactions with the help of machine learning models.

In this project, we will analyse customer-level data that has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group.

The data set is taken from the Kaggle website and has a total of 2,84,807 transactions; out of these, 492 are fraudulent. Since the data set is highly imbalanced, it needs to be handled before model building.

Business problem overview

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.