Hybrid Naïve Bayes Models for Scam Detection: Comparative Insights From Email and Financial Fraud

Online scams continue to escalate in scale and sophistication, ranging from deceptive phishing emails to complex financial fraud schemes. These evolving threats have surpassed the capabilities of traditional detection systems, creating an urgent demand for scalable, real-time, and interpretable machine learning solutions. This study revisits the Naïve Bayes algorithm—often underestimated in modern cybersecurity—as a core building block for effective scam detection. By looking at two specific case studies on phishing emails and financial fraud detection, we look into the math behind Naïve Bayes and talk about its flaws, such as the assumption that features are independent, problems with high-dimensional data, and extreme class imbalance. We apply advanced feature engineering techniques and introduce hybrid model architectures that integrate Naïve Bayes with deep learning and ensemble methods. Our empirical results reveal that a strategically optimized Naïve Bayes model can deliver competitive accuracy, while maintaining transparency and computational efficiency—key attributes for real-world fraud prevention systems. By bridging theory and application, this research challenges the notion that simplicity implies ineffectiveness. It demonstrates that Naïve Bayes, particularly when hybridized, remains a powerful and adaptable tool in the fight against evolving cyber threats, contributing to the design of robust and responsive cybersecurity frameworks.