Subject-to-group statistical comparison for open banking-type data

A. Svetlošák, M. de Carvalho and R. Calabrese

Journal of the Operational Research Society, 2023, vol. 74, issue 3, 703-718

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.

Date: 2023
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DOI: 10.1080/01605682.2021.1952115

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