Harnessing AI and Machine Learning for Advanced Fraud and Scam Detection

Abstract

As online transactions surge, fraud has escalated in complexity and scale, requiring sophisticated countermeasures. Traditional rule-based fraud detection systems often fail to adapt to evolving scam tactics. This research explores how Artificial Intelligence (AI), particularly machine learning (ML) models, enhances fraud detection capabilities. By using supervised learning, unsupervised anomaly detection, and natural language processing (NLP) techniques, AI-driven systems can dynamically identify and mitigate fraudulent behaviors. A mixed-methods approach, including data simulations and case study analyses, indicates that ML models outperform traditional systems in precision and recall metrics by substantial margins. The findings suggest that integrating AI into fraud detection not only improves efficiency but also offers a scalable, adaptive defense against emerging fraud patterns.