Abstract
Access to credit remains a critical constraint for Micro, Small, and Medium Enterprises (MSMEs) in emerging economies, largely due to information asymmetries and the limitations of traditional collateral-based credit scoring frameworks. Banks and formal financial institutions typically require audited statements, fixed-asset collateral, and long banking histories?criteria that systematically exclude informal but viable MSMEs. This paper proposes an Alternative Data Scoring Framework (ADSF) that leverages mobile usage metadata, digital transaction footprints, behavioural psychometrics, supply-chain analytics, social capital signals, and open banking information to assess creditworthiness. Drawing on global evidence from Sub-Saharan Africa, Asia, and Latin America, the study develops a composite scoring model tailored to emerging markets. The ADSF is conceptualized as a multidimensional risk assessment engine designed to improve predictive accuracy, reduce credit rationing, and expand lenders? ability to serve previously excluded MSMEs. The paper also explores the regulatory and policy implications of alternative data scoring, including issues related to privacy, data protection, algorithmic bias, and consumer rights. The findings suggest that, when embedded within robust regulatory frameworks, alternative data scoring can significantly deepen financial inclusion and unlock new growth for MSMEs.
Keywords
Alternative Credit Scoring; Msmes; Financial Inclusion; Behavioural Economics; Mobile Metadata; Psychometrics; Open Banking; Emerging Markets; Fintech; Credit Risk.
Citations
IRE Journals:
Tolulope A. Shokunbi "Alternative Data Scoring for MSME Lending: A Blueprint for Financial Inclusion" Iconic Research And Engineering Journals Volume 9 Issue 1 2025 Page 1986-1991 https://doi.org/10.64388/IREV9I1-1712949
IEEE:
Tolulope A. Shokunbi
"Alternative Data Scoring for MSME Lending: A Blueprint for Financial Inclusion" Iconic Research And Engineering Journals, 9(1) https://doi.org/10.64388/IREV9I1-1712949
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