Authors: vijaya Sawant
Abstract: Fraud detection has become a critical area of concern across industries, with increasing volumes of online transactions and evolving cyber threats. Machine Learning (ML) models play a vital role in identifying fraudulent activities. This study explores a comparative analysis of supervised and unsupervised learning approaches in fraud detection. Supervised models, relying on labeled data, offer high accuracy, while unsupervised models excel in anomaly detection, capable of identifying previously unseen fraud patterns. This paper discusses their applications, advantages, challenges, and suggests hybrid approaches to optimize fraud detection systems.
