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The ability of financial institutions to detect fraud, evaluate credit risk, and navigate market volatility with speed and precision is being significantly enhanced by Artificial Intelligence (AI). This advancement is transforming financial risk prevention into a technological revolution. Leveraging advanced machine learning algorithms, AI empowers financial systems to process complex datasets, uncover nuanced risk patterns, and adapt dynamically to evolving threats. As climate change increasingly emerges as a systemic risk, AI plays an indispensable role in evaluating financial impacts, forecasting supply chain disruptions, and shaping climate-resilient investment strategies. However, integrating AI into financial institutions introduces several regulatory challenges, particularly concerning data privacy protection, algorithmic transparency, and accountability. Striking a balance between fostering innovation and adhering to compliance is crucial to ensuring that AI systems operate ethically and fairly. Furthermore, to prevent exacerbating existing financial inequalities or creating systemic vulnerabilities, it is vital to address AI’s inherent limitations. These include data biases, issues with model interpretability, and an overreliance on historical patterns. This chapter emphasizes the necessity of establishing robust regulatory frameworks to oversee AI applications in finance. These frameworks should aim to mitigate risks while simultaneously promoting innovation. By embedding AI into a holistic risk management strategy, financial institutions can not only safeguard their assets and ensure stability but also drive sustainable growth, even in the face of uncertainty.