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Data-Driven Budget Allocation in Microfinance: A Decision Support System for Resource-Constrained Institutions
Subject area: Science,Engineering and Technology · Area of research: Data-Driven Budget Allocation
Abstract
In the evolving landscape of financial inclusion, microfinance institutions (MFIs) face the challenge of maximizing impact while operating within stringent resource constraints. This paper reviews existing models, methodologies, and technological advances in data-driven budget allocation tailored for MFIs. Emphasizing the integration of Decision Support Systems (DSS), it explores how data analytics, machine learning, and operational research can inform resource prioritization, cost optimization, and outcome-driven planning. The review critically analyzes literature from financial technology, data science, and development finance domains to highlight key frameworks enabling MFIs to shift from intuition-based decisions to evidence-based budget strategies. The paper also assesses the practical applicability, scalability, and limitations of such DSS tools in under-resourced environments. Through a synthesis of empirical studies, case examples, and conceptual models, this review identifies key enablers and barriers to adopting intelligent budget systems, offering a roadmap for future research and implementation. The findings underscore the transformative potential of data-centric budgeting for enhancing transparency, accountability, and efficiency in microfinance operations.
Keywords
Data-Driven Decision Making, Microfinance Institutions (MFIs), Budget Allocation, Decision Support Systems (DSS), Resource Optimization, Financial Technology.
How to cite this paper
@article{1709612,
author = {Okeoghene Elebe, Chikaome Chimara Imediegwu},
title = {Data-Driven Budget Allocation in Microfinance: A Decision Support System for Resource-Constrained Institutions},
journal = {Iconic Research And Engineering Journals},
year = {2020},
volume = {3},
number = {12},
pages = {368-381},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709612.pdf},
abstract = {In the evolving landscape of financial inclusion, microfinance institutions (MFIs) face the challenge of maximizing impact while operating within stringent resource constraints. This paper reviews existing models, methodologies, and technological advances in data-driven budget allocation tailored for MFIs. Emphasizing the integration of Decision Support Systems (DSS), it explores how data analytics, machine learning, and operational research can inform resource prioritization, cost optimization, and outcome-driven planning. The review critically analyzes literature from financial technology, data science, and development finance domains to highlight key frameworks enabling MFIs to shift from intuition-based decisions to evidence-based budget strategies. The paper also assesses the practical applicability, scalability, and limitations of such DSS tools in under-resourced environments. Through a synthesis of empirical studies, case examples, and conceptual models, this review identifies key enablers and barriers to adopting intelligent budget systems, offering a roadmap for future research and implementation. The findings underscore the transformative potential of data-centric budgeting for enhancing transparency, accountability, and efficiency in microfinance operations.},
keywords = {Data-Driven Decision Making, Microfinance Institutions (MFIs), Budget Allocation, Decision Support Systems (DSS), Resource Optimization, Financial Technology.},
month = {June},
}