AI-DRIVEN CYBERSECURITY THREAT IDENTIFICATION IN FINANCIAL INSTITUTIONS USING MACHINE LEARNING APPROACHES
T MUTHAIAH, RANGU MANASA · International journal of engineering science and advanced technology. · 2024
The increasing interconnectedness of digital assets is leading to an unparalleled surge in cyber attacks. Investments in artificial intelligence-based solutions are necessary if financial institutions are to recognise these dangers and safeguard their assets. When examining intricate financial security risks that are dynamic and often unpredictable, machine learning is a potent tool. Through the use of artificial intelligence (AI) technology, such as automated reasoning systems, natural language processing, and algorithms, banks may enhance their comprehension of possible hazards and establish more effective data controls. This study proposes a machine learning technique to identify cyber security concerns in financial institutions using artificial intelligence. Algorithms for machine learning are always being enhanced to find data abnormalities that might point to a security risk. With this strategy, financial institutions may use custom-made models that provide actionable insights into both internal and external threats to detect and fight against harmful assaults.