🤖 AI Summary
To address inference failure in high-risk real-time fraud detection under open banking—caused by overreliance on external data—this paper proposes the first hierarchical fallback paradigm tailored for financial real-time machine learning inference. Our method embeds fault tolerance across the entire inference pipeline via (i) fine-grained fault classification, (ii) multi-level policy routing, (iii) lightweight backup model ensembles, and (iv) coordinated scheduling with Open Banking APIs, enabling dynamic degradation and semantically consistent fallback decisions. Evaluated on a production anti-fraud system, it achieves 99.98% service availability under extreme load, reduces inference latency volatility by 76%, and maintains ≥92% consistency in risk assessment. This work is the first to systematically define, implement, and empirically validate a layered fallback architecture for financial real-time ML inference, establishing a scalable, fault-tolerant framework for high-reliability online machine learning systems.
📝 Abstract
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.