<![CDATA[Artificial intelligence (AI) is changing how financial institutions manage risk, as traditional methods struggle to keep up with fast-moving threats and growing data complexity. AI technologies like machine learning and natural language processing are now used to improve credit scoring, detect fraud faster, predict market risks, and automate compliance tasks. This study explores how these tools are being applied to make risk management more accurate and efficient. Findings show that using new types of data—such as mobile usage or online behavior—helps assess credit for those without formal histories, AI reduces false fraud alerts significantly, and compliance work becomes faster with automated document reading. Still, challenges remain: some AI models are too difficult to explain to regulators, biased results have raised fairness concerns, and older systems in many banks can’t support AI in real time. These issues highlight the need for clearer models, stronger safeguards, and better technology systems. Institutions are encouraged to train staff on AI oversight, use tools to check for bias, and partner with regulators to safely test new systems before full use.]]>
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