Network Structure in UK Payment Flows: Evidence on Economic Interdependencies and Implications for Real-Time Measurement

TL;DR

This study uses network analysis of UK payment flows to uncover structural economic relationships, improving real-time forecasting especially during disruptions like COVID-19.

Contribution

It demonstrates that graph-theoretic network features significantly enhance payment flow forecasting and reveal systemic importance of key industries.

Findings

Network features improve forecasting accuracy by 8.8 percentage points.

During COVID-19, network models maintained better performance with +13.8 percentage points.

Network density increased 12.5% over the period, with notable disruption and recovery.

Abstract

Network analysis of inter-industry payment flows reveals structural economic relationships invisible to traditional bilateral measurement approaches, with significant implications for real-time economic monitoring. Analysing 532,346 UK payment records (2017--2024) across 89 industry sectors, we demonstrate that graph-theoretic features which include centrality measures and clustering coefficients improve payment flow forecasting by 8.8 percentage points beyond traditional time-series methods. Critically, network features prove most valuable during economic disruptions: during the COVID-19 pandemic, when traditional forecasting accuracy collapsed (R2} falling from 0.38 to 0.19), network-enhanced models maintained substantially better performance, with network contributions reaching +13.8 percentage points. The analysis identifies Financial Services, Wholesale Trade, and Professional Services as structurally central industries whose network positions indicate systemic importance beyond their transaction volumes. Network density increased 12.5\% over the sample period, with visible disruption during 2020 followed by recovery exceeding pre-pandemic integration levels. These findings suggest payment network monitoring could enhance official statistics production by providing leading indicators of structural economic change and improving nowcasting accuracy during periods when traditional temporal patterns prove unreliable.