Machine Learning and Deep Learning methods are widely adopted across financial
domains to support trading activities, mobile banking, payments, and making customer credit …

The increasing spread of cyberattacks and crimes makes cyber security a top priority in the
banking industry. Credit card cyber fraud is a major security risk worldwide. Conventional …

The COVID-19 pandemic has limited people's mobility to a certain extent, making it difficult
to purchase goods and services offline, which has led the creation of a culture of increased …

In real datasets, most are unbalanced. Data imbalance can be defined as the number of
instances in some classes greatly exceeds the number of instances in other classes …

This paper proposes a method called autoencoder with probabilistic LightGBM (AED-LGB)
for detecting credit card frauds. This deep learning-based AED-LGB algorithm first extracts …

The rapid growth of e-commerce and online shopping have resulted in an unprecedented
increase in the amount of money that is annually lost to credit card fraudsters. In an attempt …

In recent years, individuals have been concerned about the extensive and pervasive use of
data mining for fraud detection in credit card models based on data mining. Because our …

This paper presents a comprehensive study on applying machine learning (ML) techniques
for real-time credit card fraud detection. With the increasing prevalence of credit card fraud …

Due to the emigration of world business to the internet, credit‎ cards have become a tool for‎
payments for both online and outline purchases. However, fraudsters try‎ to attack those …

This paper highlights how the proliferation of online transactions, especially those involving
the use of credit cards, has resulted in the emergence of new security flaws that pose threats …