AI Hallucination in Financial Systems

This chapter examined AI hallucination, where artificial intelligence systems generated false, misleading, or non factual outputs, and analysed its impact on the reliability and governance of financial systems during 2015 to 2025. The study focused on key financial applications such as credit risk assessment, fraud detection, algorithmic trading, and investment forecasting, where such errors strongly affected decision making. A Systematic Literature Review was conducted using the PRISMA framework, drawing studies from Scopus, Web of Science, and Google Scholar to identify major patterns, causes, and governance related challenges of AI hallucination. The findings indicated that hallucinations mainly arose from poor data quality, limited contextual understanding within models, and excessive dependence on automated systems without sufficient human supervision. The chapter concluded that AI hallucination was not merely a technical issue but a broader governance challenge, emphasizing the importance of explainable AI, robust internal control mechanisms, ethical auditing practices.