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 …

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 …

Although the most widely studied datasets in fraud-detection systems belong to the banking
sector, the aviation industry is susceptible to fraud activities that seriously harm airline …

The use of credit cards has recently increased, creating an essential need for credit card
assessment methods to minimize potential risks. This study investigates the utilization of …

Aim: The main aim of this research study is to detect fraudulent service enrollment websites
using the Novel Random Forest algorithm and compare its accuracy with the XGBoost …

Abstract Machine learning is an area that helps to predict outcomes more accurately. It was
utilized in different domains such as banking, healthcare, education, etc. Among all the …

The goal of the proposed work is improving prediction accuracy in drift detection using
Logistic Regression compared with modified light gradient boost model. The collection of 40 …

Online credit card fraud (CCF) is an ongoing problem and with a recent surge of merchants
moving their businesses online, it is crucial to identify fraudulent transactions before they …

Online credit card fraud is an ongoing problem and with the recent COVID-19 pandemic,
there has been a surge of merchants moving their businesses online. It is therefore crucial to …