AI and Data Governance: A New Era for Banks
Evolving to a Modern Data and AI Governance Framework
Abstract
With its ability to analyze vast amounts of data, identify patterns, and make intelligent predictions, AI has rapidly been adopted across the banking value chain—especially in operations, risk management, product development, and customer service. From improved accuracy in payment fraud detection to loan underwriting, to providing personalized products and services, to enriching client service, AI has proved itself to be a transformative technology - and is fueling investment in data platforms.
However, alongside these opportunities are challenges and considerations that come with AI adoption. Ethical concerns, data privacy, and the impact on the workforce are just a few of the complex issues that banks must grapple with as they integrate AI into their operations. As banks look to take advantage of powerful new artificial intelligence (AI) capabilities, they need to modernize and expand their existing data governance frameworks and practices to accommodate the unique needs and risks of AI development. On the one hand, they need to incorporate modern technologies and processes to enable them to scale AI development. On the other hand, it is crucial that they proactively address the evolving concerns of regulators to implement responsible AI. Both of these objectives should be incorporated in an integrated enterprise data and AI governance framework.
It is important for banks to quickly understand their current AI risk exposure and identify a go‐forward plan to remediate the risks and define how AI governance will be instituted in the future. In addition to addressing regulatory concerns, another key step in preparing for an AI future is to update legacy data architectures and utilize modern cloud‐based tools and platforms to help accelerate AI development and automate data governance. To achieve this, Celent presents nine actionable next steps.
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