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September 5, 2025Open Access

Machine Learning–Driven Fintech Solutions for Credit Scoring and Financial Inclusion in the Gig Economy

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Observational analysis improves credit scoring accuracy by 12.7% in gig economy workers, highlighting financial inclusion.

FinGig-CreditNet improves default prediction accuracy by 12.7%, ensuring fairer credit access for gig workers.
The framework integrates behavioral, transactional, and alternative data, enhancing credit scoring for non-traditional income streams.
A hybrid deep learning architecture combines graph-based feature extraction with interpretable ensemble learning, promoting transparency.
The study demonstrates a new model for leveraging digital reputations in an interoperable ecosystem for financial services.

Abdinasir Ismael Hashi (2025) studied this question.

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