AI and ML-based Cybersecurity Enhancement for Financial Institutions
Saurabh Chandra, Hritesh Yadav, S. Aravind, Rakesh Kumar Saini, Upasna Sharma, Siva Koteswara Rao Katta · 2025
Financial institutions operating in the current period of advanced digital threats encounter more complex attacks against their critical financial resources in addition to their sensitive information. Current cybersecurity approaches have shown weak results against advanced persistent threats (APTs) which makes financial institutions require next-generation automated protection solutions. In order to strengthen their cybersecurity systems, finite institutions must integrate AI and ML. Financial entities enhance their immediate security threat detection abilities by using AI-powered automatic threat identification systems. The paper evaluates different ML algorithms like supervised learning and anomaly detection and deep learning models to detect security breaches while spotting unauthorized transactions and new threats. This paper evaluates the reduction of human errors and the accelerated response times along with the scalability which these operational techniques deliver for dynamic cyber threats. Our research includes actual financial cybersecurity framework case studies that show how AI and ML technology applies for risk management as well as fraud prevention assessment. The study demonstrates that financial institutions should implement AI and ML technologies for cybersecurity because they enhance detection while protecting their systems from upcoming cyber threats.