Bank AI adoption exposes systemic data monopolies and regulatory gaps, urging global, inclusive governance reforms
Original framing: “AI push is putting banks at mercy of tech firms, warns Moody’s” — The Guardian - World
Robust scientific assessment of AI reliability, including adversarial testing, model interpretability, and systemic risk quantification, is essential to prevent cascade failures. Current industry practice often relies on proprietary benchmarks that lack transparency, undermining the scientific rigor needed for critical financial infrastructure.
The push for AI in banking is not merely a technical upgrade but a convergence of data monopolies, regulatory lag, and historical patterns of disruption that together amplify systemic risk.
By integrating indigenous data sovereignty, learning from non‑Western governance experiments, and applying rigorous scientific risk modelling, stakeholders can redesign the AI supply chain to be open, accountable, and inclusive. Embedding marginalised voices through participatory panels and fostering AI literacy creates social capital that counters vendor lock‑in. Trickster insights expose the paradox of promised efficiency turning into dependency, urging policymakers to enact anti‑monopoly and data‑sovereignty safeguards. Together, these coordinated actions can transform AI from a vulnerability into a catalyst for a more resilient, equitable financial ecosystem.