Advancements in cybersecurity: Emerging techniques and challenges in fraud detection
Om Sharma, Pitamber Kumar, Ahmad Aftab, Priyanka Sharma · 2025
Traditional fraud detection is reactive, and the increasing sophistication of digital fraud therefore represents a challenge to financial security. Advanced machine learning models combining behavioral analytics and cross-domain threat intelligence are investigated here in order to analyze transactional datasets. Findings are shown that rule-based systems are insufficient against adaptive fraud tactics, with correlations found between multi-vector attack patterns and technological vulnerabilities. Results emphasize the need for proactive, intelligence-driven frameworks that can predict and mitigate sophisticated cyber fraud. The study advocates for dynamic, predictive cybersecurity strategies to neutralize threats before they materialize, ensuring robust fraud prevention in evolving digital landscapes.