Abstract
ABSTRACT The integration of Artificial Intelligence (AI) into the financial industry has witnessed significant growth, transitioning from an academic concept to widespread adoption in the industrial sector. This trend has given rise to various AI technologies, presenting both novel opportunities and potential risks within the financial landscape. In light of this development, the present research article aims to investigate the expanding role of AI in the financial sector, focusing on its applications and impact across financial products, channels, and service methodologies. To accomplish this objective, a specific AI algorithm called Support Vector Machine for Regression (SVR) has been selected for analysis. The SVR algorithm is particularly well-suited for small sample learning, making it an appropriate choice for examining trends in Shibor. By employing a combination of theoretical analysis, case studies, and risk assessment, this article contributes to fostering a profound and robust integration of AI and finance. Consequently, it delivers both theoretical insights and practical significance, offering valuable knowledge for industry practitioners and academic researchers alike.
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