Banks and Firms Urged to Modernize AI Systems

Banks and other firms are being urged to modernize dispute-resolution workflows and AI governance to meet consumer expectations for speed, transparency and empathy while navigating divergent regional rules such as the UK’s FCA/Consumer Duty, heightened U.S. litigation risk, and evolving Canadian guidance. AI and automation can accelerate detection and case handling but introduce risks — algorithmic drift, reduced explainability, erosion of customer trust and new fraud vectors like AI-generated customer-service scams — so human oversight, rigorous vendor vetting and methods to detect drift are essential. Firms should move from siloed tools to interoperable dispute-management platforms that embed transparency, governance, auditability and clear accountability over who builds and operates models, plus legal and funding resilience. The environmental footprint of AI — rising data-center energy and water use, backup-generator emissions and a projected doubling of consumption by 2030 — requires life-cycle-assessment–led optimization and practical measures such as energy-efficient hardware, alternative cooling and energy strategies. Model-efficiency techniques such as transfer learning and fine-tuning frozen pretrained weights can dramatically reduce compute and training energy — in some cases by up to about 90% — and should be prioritized alongside governance and operational safeguards.