Credit Beyond Scores: Platform-Native Behavioral Data as Alternative Determinants of Financial Eligibility in the Digital Economy

Abstract

This article explores an innovative paradigm in financial eligibility assessment that moves beyond traditional credit scoring systems. By leveraging platform-native behavioral signals instead of conventional credit histories, financial institutions can expand access to capital for previously underserved populations while maintaining robust risk management. The model analyzes digital footprints, including transaction patterns, earnings consistency, engagement metrics, and professional relationships, to create more holistic creditworthiness profiles. Alternative credit assessment methodologies demonstrate superior predictive power for individuals lacking conventional credit histories, particularly independent contractors, gig economy workers, and young professionals. The technical architecture integrates diverse data streams through sophisticated machine learning algorithms while addressing critical ethical considerations, including bias mitigation, privacy protection, and algorithmic transparency. Implementation results reveal significant financial inclusion benefits alongside strong loan performance metrics, challenging conventional risk assumptions about "credit invisible" populations. This platform-native approach represents a transformative shift in fintech risk modeling that aligns business objectives with broader financial inclusion goals.