Open Banking powered machine learning applications require novel robustness approaches to deal with challenging stress and failure scenarios. In this paper we propose an hierarchical fallback architecture for improving robustness in high risk machine learning applications with a focus in the financial domain. We define generic failure scenarios often found in online inference that depend on external data providers and we describe in detail how to apply the hierarchical fallback architecture to address them. Finally, we offer a real world example of its applicability in the industry for near-real time transactional fraud risk evaluation using Open Banking data and under extreme stress scenarios.

翻译：开放银行驱动的机器学习应用需要新颖的鲁棒性方法来应对具有挑战性的压力与故障场景。本文提出一种分层回退架构，旨在提升高风险机器学习应用（尤其聚焦金融领域）的鲁棒性。我们定义了在线推理中常见的、依赖于外部数据提供商的通用故障场景，并详细描述了如何应用分层回退架构来解决这些问题。最后，我们通过一个工业界的实际案例，展示了该架构在利用开放银行数据进行近实时交易欺诈风险评估时，于极端压力场景下的适用性。