Platform Divergence and Decision-Stage Instability in Tier 1 Banking

Abstract Controlled cross-model testing across Tier 1 banking institutions demonstrates that identical comparative prompts can yield materially different decision-stage recommendations across leading AI systems. Under fixed prompt sequences and repeated runs, we observe: • Deterministic convergence in some systems • Progressive recommendation hardening in others • Explicit winner oscillation in at least one system • Rubric expansion and authority loading across repeated runs No institution exhibited universal disadvantage across the ecosystem. Instead, platform-dependent divergence and run-level instability were observed. This article does not assert bias or systemic targeting. It documents reproducible structural behaviour and examines whether institutions may wish to formalise visibility and assurance over externally generated AI representations when such outputs participate in decision formation.