In machine learning, the data imbalance imposes challenges to perform data analytics in
almost all areas of real-world research. The raw primary data often suffers from the skewed …

Credit risk has been a widespread and deep penetrating problem for centuries, but not until
various credit derivatives and products were developed and novel technologies began …

Fraud detection has attracted significant attention in financial institutions, especially utilizing
some artificial intelligent methods to automatically detect fraudulent transactions. With the …

In the context of machine learning, an imbalanced classification problem states to a dataset
in which the classes are not evenly distributed. This problem commonly occurs when …

As credit card fraud has caused huge economic losses and harm cardholders seriously,
credit card fraud detection is important and has been paid much attention. An effective …

In recent years, the rapid development of e-commerce exposes great vulnerabilities in
online transactions for fraudsters to exploit. Credit card transactions take a salient role in …

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit
card fraud (CCF). Need for the study: With the advance of technology, the world is …

Companies want to give more and more facilities to their customers. One of these facilities is
the online mode of buying goods. The customers now can buy the required goods online but …

Due to the rise of technology, the possibility of fraud in different areas such as banking has
been increased. Credit card fraud is a crucial problem in banking and its danger is over …

The study aims to identify the frauds committed using a payment card such as credit cards,
debit cards, and also an experiment is performed to find the best suitable algorithm among …