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

Research output: Contribution to journalArticlepeer-review

Abstract

Open banking (OB) creates an opportunity for financial institutions to oer 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.
Original languageEnglish
JournalJournal of the Operational Research Society
Early online date23 Aug 2021
DOIs
Publication statusE-pub ahead of print - 23 Aug 2021

Keywords

  • f-barycenter
  • financial advice
  • nonparametric regression
  • open banking
  • personal finances
  • statistical planning
  • truncated regression

Fingerprint

Dive into the research topics of 'Subject-to-group statistical comparison for open banking-type data'. Together they form a unique fingerprint.

Cite this