Abstract

Open banking (OB) creates an opportunity for financial institutions to offer more personalised services by better differentiating between a specific customer (reference subject) and similar customers (comparison group). We propose the time-varying comparative mean value as a statistical method that learns about the dynamics governing how the response of a reference subject differs from that of a comparison group, defined via covariate truncation. The proposed model can be regarded as a time-varying truncated covariate regression model of which a smooth version is devised by resorting to local polynomial regression. The simulation study suggests that our estimators accurately recover the true time-varying comparative mean value in a variety of scenarios. We showcase our methods using OB-type data from a financial service provider in the UK, with the dataset containing detailed information on customers’ accounts across 70 UK financial institutions. By contrasting a specific customer against similar customers, our method offers interesting diagnostics that can be used by financial institutions to recommend personalised services.

摘要

开放银行 (OB) 通过更好地区分特定客户（参考对象）和类似客户（比较组），为金融机构创造了提供更个性化服务的机会). 我们建议将时变比较平均值作为一种统计方法，了解控制参考对象的反应与比较组的反应如何不同的动态，通过协变量截断定义。所提出的模型可以被视为时变截断协变量回归模型，其平滑版本是通过求助于局部多项式回归设计的。模拟研究表明，我们的估计器可以在各种情况下准确地恢复真实的时变比较平均值。我们使用来自英国金融服务提供商的 OB 类型数据展示了我们的方法，该数据集包含 70 家英国金融机构客户账户的详细信息。通过将特定客户与类似客户进行对比，