Proceedings of the 8th International Conference on Applied Research in Business, Management and Economics
Year: 2025
DOI:
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Exploring How Risk Assessment Algorithms in Digital Lending Apps Affect Financial Inclusion for Traditionally Underserved Populations in Developing Countries
Philipp Goetzinger
ABSTRACT:
This study investigates how AI-driven risk assessment algorithms in digital lending apps influence financial inclusion in developing countries, with a specific focus on India, Kenya, and Nigeria. Financial inclusion is essential for socioeconomic development, yet billions globally remain underserved by traditional financial institutions. The research uses a mixed-method approach, combining quantitative analysis of survey data with qualitative insights from case studies and expert interviews. Findings indicate that AI algorithms improve credit access for underserved populations by analyzing alternative data sources like mobile usage and transaction history. However, concerns persist regarding algorithmic bias, transparency, and data privacy. The study identifies that socioeconomic factors significantly impact perceptions of fairness, with higher-income groups reporting greater trust in AI-driven assessments. While AI models show potential for reducing default rates and enhancing loan accessibility, their success hinges on ethical implementation and robust regulatory frameworks. The research concludes with recommendations for improving algorithmic transparency, enhancing financial literacy, and fostering collaboration among stakeholders to promote inclusive, secure, and fair access to credit in developing nations.
keywords: AI-Driven Risk Assessment, Digital Lending Apps, Financial Inclusion, Developing Countries, Algorithmic Bias, Credit Scoring, Fintech; Kenya, Nigeria, India,Financial Technology