Artificial Intelligence-Based Cyber Security Threat Identification in Financial Institutions Using Machine Learning Approach
Keywords:
insights, internal, external, malicious, advancementsAbstract
As digital assets become increasingly interconnected, the frequency and sophistication of cyber threats are escalating. Financial institutions need to invest in AI-based solutions to effectively identify and counteract these threats, thus protecting their assets. Machine learning has become a pivotal tool for examining intricate and evolving financial security threats, which are often unpredictable. By employing AI technologies such as natural language processing, advanced algorithms, and automated reasoning systems, banks can better understand potential risks and enhance their data control measures. This paper introduces an AI-based method for detecting cyber security threats within financial institutions, utilizing a machine learning approach. Ongoing advancements in machine learning algorithms improve their capability to detect data anomalies that may indicate security threats. This strategy enables financial firms to proactively identify and defend against malicious activities through customized models that provide actionable insights into both internal and external risks.
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