AI is increasingly becoming an important catalyst for improving the efficiency and efficacy of the financial services industry. For this paper, we consider institutions that provide banking services, insurance services, payment services, and investment services as part of the financial services industry (FSI). The recent success of generative AI and predictive AI in the last few years generated enormous interest in deploying AI across FSI use cases. However, because it is highly regulated and lacks open-source datasets, there are not enough published resources on the production challenges, failures, and reasons behind them. Because of this, there is a growing technical debt regarding how AI is deployed in the FSIs. In addition to this, due to a lack of interdisciplinary skills in AI and the associated business risks, traditional risk managers and auditors struggle to create risk frameworks for AI deployments. In this paper, we will review the AI technical debt (TD) in FSIs and do an empirical study about the risks involved.