Predicting Youth Financial Inclusion: A Machine LearningClassification Approach Using Access, Quality, and Demographic Determinants
DOI:
https://doi.org/10.14419/6wszea16Published
08-12-2025Keywords:
Access and Quality Dimensions; CHAID Model; Financial Inclusion; Logistic Regression; Socio-economic DeterminantsAbstract
Financial inclusion has become a cornerstone of inclusive growth in India, yet its outreach among youth remains uneven and underexplored. This study measures the determinants of financial inclusion among the youth of Kutch district by applying both Logistic Regression and Chi-square Automatic Interaction Detection (CHAID) models. The analysis is based on primary data collected from 410 respondents across diverse demographic and occupational backgrounds. The logistic regression model achieved an overall prediction accuracy of 90.2% and a Nagelkerke R² value of 0.805, signifying strong explanatory power. The findings reveal that Access and Quality dimensions—derived from the Reserve Bank of India’s Financial Inclusion framework—are the most significant predictors of inclusion. Occupation and income also exhibit meaningful influence, indicating that employment and financial capability enhance formal participation. The CHAID model further validates these results, uncovering hierarchical relationships between access, quality, and socio-economic variables. The combined interpretation of both models provides a comprehensive view of youth inclusion behavior and highlights that accessibility, service quality, and economic opportunity jointly determine inclusion outcomes. The study concludes with policy recommendations aimed at strengthening digital availability, improving service reliability, and promoting youth employment to accelerate financial inclusion in semi-urban and rural India.
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