Forecasting inclusive futures: Fintech, capability expansion, and livelihood pathways in urban Indian slums – A mixed-methods analysis

Rigorous evidence forecasting fintech's poverty alleviation mechanisms in low-income settings remains scarce. Integrating Sen's Capability Approach and the Sustainable Livelihoods Framework, this study analyzes fintech's influence on productive credit use, assets, and capabilities in Indian slums. A mixed-methods design (N = 600 survey, 40 interviews) combines Structural Equation Modeling (SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA). SEM confirms fintech adoption strongly predicts productive credit use (β = 0.68, p < 0.001), fostering assets (β = 0.72) and capabilities (β = 0.75), improving livelihoods (β = 0.70); policy support significantly moderates (β = 0.30, p = 0.002) the initial fintech-credit link. Complementing SEM's net effects, fsQCA reveals two sufficient, equifinal pathways to improved livelihoods: (1) high fintech adoption with robust policy support (Consistency: 0.87, Coverage: 0.42), demonstrating policy synergy, and (2) moderate fintech usage compensated by strong social networks and prior assets (Consistency: 0.82, Coverage: 0.31), highlighting community compensation mechanisms. Qualitative data illuminate crucial roles of trust, digital literacy, mentorship, and gender dynamics, a finding quantitatively substantiated by a multi-group analysis revealing that women are significantly more effective at leveraging social networks, while revealing risks such as digital exclusion and potential debt traps. Findings forecast that successful fintech deployment for poverty reduction necessitates synergistic socio-technical ecosystems—combining technology with enabling policies, community structures, and proactive mitigation of negative externalities—thereby informing strategies for inclusive, sustainable social change.