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• Graph-theoretic features including centrality measures and clustering coefficients improve payment flow forecasting by 8.8 percentage points beyond traditional time-series methods.result 90%
• 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.result 90%
• Financial Services, Wholesale Trade, and Professional Services are structurally central industries whose network positions indicate systemic importance beyond their transaction volumes.result 85%
• Network density increased 12.5% over the sample period (2017-2024), with visible disruption during 2020 followed by recovery exceeding pre-pandemic integration levels.result 90%
• Network analysis of inter-industry payment flows reveals structural economic relationships invisible to traditional bilateral measurement approaches.method 85%
• 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.market 80%
• The analysis is based on 532,346 UK payment records (2017-2024) across 89 industry sectors.method 95%
• Network features prove most valuable during economic disruptions.result 85%

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