Enhancing Credit Scoring with Alternative Data and Machine Learning for Financial Inclusion

Abstract

Introduction: This review article explored advancements in credit appraisal through machine learning techniques and alternative data sources, staying focused on their implications for financial inclusion and risk assessment.
Traditional credit scoring models, which relied heavily on linear methods and credit history, often excluded individuals in developing economies and those with limited credit records.
Methods: This gap underscored the need for innovative approaches leveraging non-traditional data such as psychometrics, email activity, and digital footprints. The research design encompassed a comprehensive analysis of 36 articles examining case studies from several countries covering applications in microfinance, agricultural credit, and fintech-driven solutions. Methodologically, the studies applied neural networks, support vector machines, and ensemble learning to enhance predictive accuracy over logistic regression and linear discriminant analysis.
Results: Findings consistently demonstrated that machine learning models outperform traditional approaches, especially in volatile environments and for underserved populations. Including alternative data significantly improved credit access, enabling financial institutions to extend services to high-risk or previously unbanked individuals.
Conclusions: Implications for practice highlighted the transformative role of fintech in democratising credit, while theoretical contributions emphasised the evolving nature of credit risk modelling. This synthesis advocated for further interdisciplinary collaboration to refine non-traditional credit models and addressed ongoing barriers to global financial inclusion.