Hierarchical Fallback Architecture for High Risk Online Machine Learning Inference
This addresses reliability issues for financial institutions using Open Banking data, though it appears incremental as it adapts existing fallback concepts to a specific domain.
The paper tackles robustness challenges in high-risk online machine learning inference by proposing a hierarchical fallback architecture, demonstrating its applicability in real-world financial fraud detection under extreme stress scenarios.
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.