loading page

Enhancing AI Governance in Financial Industry through IBM watsonx.governance
  • Souva Majumder,
  • Anushree Bhattacharjee,
  • Joseph N. Kozhaya
Souva Majumder

Corresponding Author:[email protected]

Author Profile
Anushree Bhattacharjee
Joseph N. Kozhaya


The integration of AI technologies specially Generative AI into business operations brings forth unprecedented opportunities alongside profound responsibilities. Failure to mitigate risks associated with AI & Generative AI implementation may result in severe consequences such as damaged brand reputation, loss of public trust, and regulatory penalties. In response, the adoption of AI governance has emerged as a critical imperative for enterprises seeking to scale AI initiatives responsibly. At least 80% of the business leaders finds ethical issues a major concerns. 48% Believe decisions made by Generative AI are not sufficiently explainable. 46% Concerned about the safety and ethical aspects of Generative AI. 46% Believe that Generative AI will propagate established biases. 42% Believe Generative AI cannot be trusted. This paper explores the principles of responsible AI, emphasizing transparency, accountability, and the augmentation of human intelligence. Through a technical lens, the intersection of responsible AI, AI ethics, and AI governance is discussed, underscoring the importance of data governance, lifecycle management, and model governance. IBM Watsonx.governance is presented as a unified platform facilitating the operationalization of AI with confidence, risk mitigation, and regulatory compliance. By adhering to principles of lifecycle governance, risk management, and regulatory compliance, this platform empowers organizations to govern generative AI and predictive ML effectively. Built upon ethos of trust, openness, targeting, and empowerment, it serves as a robust toolkit for steering, managing, and monitoring AI activities, thereby fostering trust and integrity in AI systems.
27 Mar 2024Submitted to TechRxiv
30 Mar 2024Published in TechRxiv