Hosting artificial intelligence platforms in financial institutions through cost model based economic and organisational analysis of internal platforms versus managed hyperscale services

Abstract Financial institutions increasingly rely on artificial intelligence to support decision making, automation, fraud detection, and customer engagement. Unlike technology firms, banks are not primarily mandated to develop artificial intelligence platforms as commercial products, but rather to consume AI capabilities reliably and economically in service of regulated financial outcomes. This paper investigates whether internally hosted AI platforms represent a cost-optimal and organisationally sustainable strategy for such institutions when compared with managed hyperscale platforms. A formalised five-year total cost of ownership model is developed and evaluated under conservative assumptions representative of OECD banking environments. Sensitivity analysis is applied to utilisation, labour cost growth, and hardware price dynamics. Within the modelled parameter space and under assumptions intended to reflect OECD banking environments, internally hosted AI platforms show higher five-year total cost of ownership and higher governance overhead than functionally comparable managed hyperscale platforms across most tested scenarios. A hybrid reference architecture and operating model aligned with financial-sector operational resilience and information security regulation is proposed. The findings suggest that disciplined consumption of managed AI platforms, rather than internal replication, is economically favoured under the modelled parameter space and stated assumptions.