为高风险金融场景设计分层降级架构，提升在线推理鲁棒性。
Hierarchical Fallback Architecture for High Risk Online Machine Learning Inference
- 构建分层降级机制应对外部数据源故障
- 在极端压力下仍能保持近实时欺诈检测能力
- 适用于开放银行场景的高可靠性系统设计
开放银行赋能的机器学习应用需应对复杂压力与故障场景。本文提出一种分层降级架构，以提升高风险机器学习应用在金融领域的鲁棒性。定义了在线推理中常见的外部数据提供方故障场景，并详细说明如何应用该架构解决这些问题。最后，通过真实工业案例展示其在使用开放银行数据进行近实时交易欺诈风险评估中的适用性，尤其在极端压力条件下的表现。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。