The rapid advancement of artificial intelligence (AI) technology has significantly impacted the financial services industry in recent years, with AI products leading to increased productivity and efficiency in banking institutions. While AI offers numerous benefits such as error reduction and data processing capabilities, it also poses unique risks and challenges that highlight the need for a robust AI governance framework in banking institutions. Anil Sood, leading the AI Governance practice at EY Canada, emphasizes the importance of establishing effective governance practices to manage these risks effectively.
Banks face external risks when implementing AI applications, including regulatory risks related to global regulations, local regulations, and international standards. The EU AI Act and various state legislation in the U.S. have set standards for the appropriate use of AI, while organizations like the OECD provide guidelines for best practices. Additionally, banks must address adversarial threats such as cybersecurity issues and potential risks associated with third-party AI solution providers impacting the security of AI applications. Misuse, perceived bias, or breaches in data privacy in AI systems can also lead to reputational damage for banks, especially for customer-facing applications.
Establishing a centralized AI governance framework is crucial for effectively managing the diverse risks associated with AI, including data risk, model risk, and cybersecurity risk. Without cohesive guidelines and alignment across control functions, the effectiveness of AI governance may be compromised, leading to potential errors or liabilities being overlooked. The broad distribution of AI governance responsibilities across various departments can create ambiguity regarding accountability for negative outcomes, hindering the decision-making and rectification process.
The increasing complexity of AI models, especially with the introduction of large language models (LLMs), presents new governance challenges for banking institutions. Chatbots and deep learning models deployed in credit adjudication processes require transparency, accountability, and explainability to ensure compliance with regulations and detect potential biases. The lack of explainability in AI decision-making processes can hinder banks from providing necessary information to clients, potentially leading to legal complications or discriminatory practices.
In conclusion, as AI continues to be embraced by banking institutions, the establishment of a robust AI governance framework is essential for effectively managing risks, ensuring compliance, and maintaining trust with clients. The evolving landscape of AI risk underscores the need for comprehensive governance practices and centralized frameworks to mitigate disparities and enhance transparency and accountability. An effective AI governance framework not only protects institutions from external risks but also fosters a reputation as a trustworthy and innovative financial service provider in the digital era.
