Open banking enables individual customers to own their banking data, which provides fundamental support for the boosting of a new ecosystem of data marketplaces and financial services. In the near future, it is foreseeable to have decentralized data ownership in the finance sector using federated learning. This is a just-in-time technology that can learn intelligent models in a decentralized training manner. The most attractive aspect of federated learning is its ability to decompose model training into a centralized server and distributed nodes without collecting private data. This kind of decomposed learning framework has great potential to protect users' privacy and sensitive data. Therefore, federated learning combines naturally with an open banking data marketplaces. This chapter will discuss the possible challenges for applying federated learning in the context of open banking, and the corresponding solutions have been explored as well.
翻译:开放银行使个人客户能够拥有自己的银行数据,这为推动数据市场和金融服务的新生态系统提供了基本支持。在不远的将来,可以预见的是,利用联合学习将数据所有权分散在金融部门。这是一个即时技术,可以以分散的培训方式学习智能模型。联合银行学习最有吸引力的方面是,它能够将模式培训分解成一个中央服务器和分布式节点,而不收集私人数据。这种分解的学习框架在保护用户隐私和敏感数据方面有很大的潜力。因此,联合学习自然与开放银行数据市场相结合。本章将讨论在开放银行业务中应用联合学习的可能挑战,并探讨相应的解决方案。