Risk Mitigation in AI-Driven Financial Systems

Abstract

Artificial Intelligence (AI) has transformed the financial sector by enhancing decision-making, automating processes, and improving customer experiences. However, the rapid integration of AI-driven solutions has also introduced various risks, including bias, security vulnerabilities, regulatory non-compliance, and ethical concerns. This research paper explores the critical challenges associated with AI-driven financial systems and presents effective risk mitigation strategies. Through empirical experiments and analysis, the study evaluates the impact of different mitigation techniques on risk reduction. The results demonstrate that a combination of robust model governance, adversarial testing, bias detection algorithms, and regulatory alignment can significantly enhance the resilience of AI-powered financial systems. The findings provide valuable insights into safeguarding financial institutions from AI-induced risks while ensuring innovation and efficiency in financial operations.