Home / Current Issue / Paper 1712949
Alternative Data Scoring for MSME Lending: A Blueprint for Financial Inclusion
Subject area: Management and Commerce · Area of research: MSME Finance
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.
References
[1] Ayyagari, M., Demirgüç-Kunt, A., & Maksimovic, V. (2016). Are innovative firms more credit constrained? Journal of Financial Economics, 120(1), 45–67.
[2] Basel Committee on Banking Supervision. (2020). Sound practices: Implications of fintech developments for banks and bank supervisors. Bank for International Settlements.
[3] Björkegren, D., & Grissen, D. (2019). Behavior revealed in mobile phone usage predicts credit repayment. The World Bank Economic Review, 33(3), 534–558.
[4] Central Bank of Nigeria. (2023). Open Banking Framework for Nigeria.
[5] de Mel, S., McKenzie, D., & Woodruff, C. (2019). Business training and psychometric testing for female micro-entrepreneurs. Journal of Development Economics, 136, 99–118.
[6] GSMA. (2022). Digital financial services usage and credit behavior in Sub-Saharan Africa.
[7] International Finance Corporation. (2022). MSME Finance Gap: Assessment of the Shortfalls and Opportunities in Financing Micro, Small, and Medium Enterprises.
[8] LenddoEFL. (2018). Psychometric scoring for credit assessment: Global evidence. LenddoEFL Research Report.
[9] Stiglitz, J. E., & Weiss, A. (1981). Credit rationing in markets with imperfect information. American Economic Review, 71(3), 393–410.
[10] World Bank. (2023). Enterprise Survey Dashboard: Nigeria.
How to cite this paper
@article{1712949,
author = {Tolulope A. Shokunbi},
title = {Alternative Data Scoring for MSME Lending: A Blueprint for Financial Inclusion},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {1},
pages = {1986-1991},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712949.pdf},
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.},
month = {July},
doi = {https://doi.org/10.64388/IREV9I1-1712949}
}