Year: 2026
Type: article
Abstract: This paper explores how open banking and algorithmic profiling are reshaping mortgage lending in the UK. Drawing on interviews with lenders, brokers, technology developers and borrowers, it examines how transaction data, behavioural analytics and predictive modelling are being used to assess credit risk. While these technologies promise efficiency and inclusion, they often deepen inequality by embedding opaque and morally charged judgements into lending decisions. Through seven speculative vignettes, the paper illustrates how algorithmic systems may reward conformity, penalize life transitions and categorize borrowers in ways that reinforce social divisions. Using these interviews as a foundation, seven vignettes are developed to capture how data-driven metrics might govern access to housing finance in the near future. As automated infrastructures evolve, they raise urgent questions about fairness, privacy and accountability. This article explores how envisioning mortgage futures through speculative vignettes facilitates an engagement with these emerging questions.
Source: Housing Theory and Society
Institutions: University of Bristol, University of York
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: hybrid
Funder: Nuffield Foundation
OpenAlex ID: W7130495432
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