These systems can fail unexpectedly in a variety of different ways. Notably, applications Open Banking powered machine learning applications require novel that rely on online inference are subject to their inability robustness approaches to deal with challenging stress and failure to keep up with the expected operating procedures while, now scenarios. In this paper we propose an hierarchical fallback architecture additionally, having to make tedious computational tasks for these for improving robustness in high risk machine learning AI/ML applications, typically resulting in timeouts, infrastructure applications with a focus in the financial domain. We define generic outages and, often, failures in external dependencies such as third failure scenarios often found in online inference that depend on party data providers (external API calls) [7]. When the underlying external data providers and we describe in detail how to apply the machine learning applications are presented with strong robustness hierarchical fallback architecture to address them. Finally, we offer requirements, fallback or fall-over strategies are needed to keep a real world example of its applicability in the industry for near-real operations running, even in the event of unexpected failures. In time transactional fraud risk evaluation using Open Banking data finance, specifically applications that require real time risk mitigation and under extreme stress scenarios.