AI-Driven Fraud Detection: Leveraging Machine Learning for Scam Identification

Fraud detection has become one of the main challenges in cybersecurity, financial security, and online shopping. Traditional rule-based fraud detection systems are no longer sufficient to struggle against ever-evolving fraud methods. Machine learning (ML) provides an adaptive, data-driven approach for detecting fraudulent activity in real time by learning patterns and detecting outliers. This article explores the main ML approaches in fraud detection: supervised learning (logistic regression, decision trees, gradient boosting, and deep learning), unsupervised learning (clustering, autoencoders, and isolation forests), and hybrid models. It also mentions the issues related to the source of the data, feature engineering, challenges such as data imbalance and concept drift, and future research directions such as explainable AI, adversarial machine learning defenses, and real-time fraud detection using Edge AI. The result indicates that fraud detection based on AI greatly enhances accuracy, scalability, and adaptability and is, hence, a critical tool in modern-day cybersecurity