MITIGATING THREATS IN MODERN BANKING: THREAT MODELING AND ATTACK PREVENTION WITH AI AND MACHINE LEARNING - Cheemakurthi, Sai Krishna Manohar; Gunnam, Vinodh; Kilaru, Naresh Babu (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2022)
The world of banking today can be regarded as a sphere that experiences higher levels of threats, which are complex and more effective, meaning that financial data has to be protected more accurately. This paper aims to review several threat modeling approaches to attack prevention in today's banking relationship with the presence of AI and machine learning. Explaining the plan of action in detail by referring to the simulation reports and actual times, the appropriateness of these technologies in identifying threats, and prediction and prevention of the threats that may occur is well illustrated. AI and machine learning make the threat detection process faster and more accurate; thus, one can take preventive measures that help prevent cyber-attacks. This report also discusses the issues concerned with using AI in security, where, among others, there are the privacy issues with data used in AI, how to integrate AI in security, and the fact that updates for AI technologies that handle security are always needed because threats can change often. Some recommendations regarding solutions for these challenges are presented concerning the current experience and possible further developments in the field of R&D. The conclusions suggest that AI and machine learning solutions should be employed to improve the defense of banks from cyber threats, as well as maintain customers' confidence in the digital environment. Therefore, integrating these advanced technologies will make it easier for the banks to counter cyber criminals and better protect their vital infrastructure.