Digital Shields Against Cybercrime: An Analysis of Threat Mitigation, Defense Strategies, and Forensic Solutions in Banking

Sophisticated cyber-attacks have grown in line with the banking sector's digitalization, underscoring the importance of robust security measures to protect financial resources and confidential consumer information. A new and adaptive cybersecurity solution is needed to counter zero-day attacks, phishing attacks, and insider threats, whereas conventional defense mechanisms cannot. By emphasizing the limitations of conventional approaches to addressing emerging cyber threats, this paper provides an in-depth analysis of banking cyber threat mitigation strategies, defense systems, and forensic tools. The Adaptive Cyber Threat Intelligence and Response System (ACTIRS) is a machine learning-based security system that makes financial institutions more resilient to cyberattacks. ACTI-RS uses anomaly detection algorithms to detect suspicious behavior in real time and enhance the accuracy of fraud detection and intrusion prevention features. Its automated forensic analysis module enables rapid incident response, which enhances threat intelligence and reduces response time. Active protection from cyber-attacks is assured by the proposed system, which, through ongoing learning, dynamically adjusts to new attack patterns. It is emulated with real-world banking statistics to evaluate how effectively ACTI-RS identifies threats, prevents fraud, and saves reaction time. Better detection accuracy, reduced false positives, and faster forensic analysis are emphasized compared to security paradigms. The results show that ACTI-RS can offer an adaptable and expandable solution for cyber threat prevention by enhancing banking cybersecurity with threat intelligence powered by machine learning.