PREDICTIVE AI MODELS AND THEIR RELATIONSHIP TO PROTECTION AGAINST ADVANCED PERSISTENT THREATS TO THE FINANCIAL SYSTEM - Espinoza Villavicencio, Héctor Martín (2026)
The objective was to establish the relationship between artificial intelligence (AI) predictive models and protection against advanced persistent threats (APTs) in the Lima banking system by 2024. A secondary source analysis was conducted on AI predictive models that detect persistent cybersecurity threats such as malware. A survey was administered to a sample of 79 employees from banking institutions in Lima. The study was non-xperimental, quantitative, and cross sectional. The results  showed improved performance of security systems in the banking sector after the application of predictive models in areas such as APT detection, mitigation, and prevention, thus strengthening cybersecurity in a critical context. These findings highlight the usefulness of the models in more accurately predicting attacks targeting financial institutions. The survey revealed that the majority of participants believe that AI predictive models help prevent APTs and optimize problem-solving, indicating a favorable trend toward the implementation of AI-based tools within banks. Implementing predictive AI models strengthens banks’ resilience against APT attacks, as their ability to optimize processes improves resistance and has an effect  on the evolution of protection against APT attacks, giving them adaptability to new threats as they improve and become more sophisticated.