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Machine Learning in Social Finance: Facilitating Inclusive Finance Approaches
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Author(s): Silvio Andrae (Independent Researcher, Germany)
Copyright: 2025
Pages: 30
Source title:
AI Strategies for Social Entrepreneurship and Sustainable Economic Development
Source Author(s)/Editor(s): Poshan Yu (Soochow University, China & European Business Institute, Luxembourg & Australian Studies Centre, Shanghai University, China), Steve K.M. Wong (Big Data Ideal Lab, China & Belt and Road Blockchain Association, China)and Akhilesh Chandra Prabhakar (University of Technology, Papua New Guinea)
DOI: 10.4018/979-8-3693-6392-8.ch002
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Abstract
Machine learning (ML) has emerged as a transformative tool in the field of social finance, particularly in enhancing microfinance institutions' (MFIs) ability to address the financial needs of disadvantaged populations. This paper explores the potential of ML to promote inclusive finance by leveraging alternative data sources and advanced algorithms for credit scoring, customer segmentation, and risk assessment. Practical examples demonstrate how ML technologies can optimize operational efficiency, improve loan accessibility, and maintain fairness in decision-making processes. However, the study also highlights ethical challenges, including data bias, transparency issues, and the risk of algorithmic discrimination. By combining hard and soft information, ML has the potential to complement human judgment, enabling a balanced approach to financial inclusion. Furthermore, the paper emphasizes the need for a socially responsible implementation of AI, ensuring that these innovations contribute to the well-being and resilience of marginalized communities. The findings suggest a path forward for integrating ML into microfinance while addressing critical ethical and practical considerations.
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