Year: 2025
Type: article
Abstract: Fraud detection in Software as a Service (SaaS) platforms has garnered significant attention in light of the growing complexity of cybercrimes and the growing need to protect sensitive user data. Although traditional methods of fraud detection are largely based on rule-based systems, machine learning (ML) has emerged as a more effective option due to its ability to detect complex and dynamic patterns of fraud. This paper performs a literature review for the period 2015-2024, examining the use of various ML techniques in fraud detection in SaaS environments. Early research focused on basic classifiers like decision trees and logistic regression, gradually moving towards ensemble methods and feature engineering to achieve higher accuracy.
Source: Universal Research Reports
Institutions: Texas A&M University – Kingsville, Sharda University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408779119