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

The rapid expansion of the gig economy has generated new challenges for financial inclusion, particularly in credit scoring where traditional models fail to accommodate the fragmented and non-standardized income streams of gig workers. This study introduces FinGig-CreditNet (Financial Inclusion Gig-Credit Neural Framework), a novel machine learning–driven framework that integrates behavioral, transactional, and alternative data to construct adaptive, explainable, and portable credit scores for gig workers. Unlike existing labor credit scoring approaches, FinGig-CreditNet employs a hybrid deep learning architecture combining graph-based feature extraction with interpretable ensemble learning, ensuring transparency and robustness across heterogeneous gig platforms. A multi-layered design enables the aggregation of platform-specific performance signals, psychometric indicators, and mobile payment histories into a unified credit profile. Experimental evaluation on a synthesized multi-platform gig dataset demonstrates that FinGig-CreditNet improves default prediction accuracy by 12.7% and fairness metrics by 9.4% compared to baseline credit scoring models. More importantly, the framework enhances the portability of creditworthiness across platforms, thereby creating an interoperable ecosystem where gig workers can leverage their digital reputation for financial access. The findings highlight FinGig-CreditNet as a scalable solution bridging fintech innovation, machine learning, and social equity, offering both theoretical and policy contributions to the design of inclusive financial infrastructures in the digital labor economy.