AI and machine learning as tools for financial inclusion: challenges and opportunities in credit scoring

Tambari Faith Nuka 1, * and Amos Abidemi Ogunola 2

1 Department of Business Administration, Earl G. Graves School of Business and Management, Morgan State University, USA.
2 Econometrics and Quantitative Economics, Department of Agricultural and Applied Economics, University of Georgia. USA.
 
Research Article
International Journal of Science and Research Archive, 2024, 13(02), 1052–1067.
Article DOI: 10.30574/ijsra.2024.13.2.2258
Publication history: 
Received on 09 October 2024; revised on 16 November 2024; accepted on 18 November 2024
 
Abstract: 
Financial inclusion remains a pressing global challenge, with millions of underserved individuals excluded from traditional credit systems due to systemic biases and outdated evaluation models. Artificial Intelligence [AI] and Machine Learning [ML] have emerged as transformative tools for addressing these inequities, offering opportunities to redefine how creditworthiness is assessed. By leveraging the predictive power of AI and ML, financial institutions can expand access to credit, improve fairness, and reduce disparities in underserved communities. This paper begins by exploring the broad potential of AI and ML in financial inclusion, highlighting their ability to process vast datasets and uncover patterns that traditional methods overlook. It then delves into the specific role of ML in identifying and reducing biases in credit scoring. ML algorithms, when designed with fairness in mind, can detect discriminatory patterns, enabling financial institutions to implement corrective measures and create more inclusive systems. The discussion narrows to examine the importance of diverse datasets in ensuring equitable outcomes. By incorporating non-traditional data points—such as rent payments, utility bills, and employment history—AI systems can provide a more holistic view of creditworthiness, particularly for individuals marginalized by conventional models. Finally, the ethical considerations of using AI in credit scoring are addressed, focusing on the need for transparency, accountability, and safeguards against algorithmic discrimination. This paper argues that responsible implementation of AI and ML, combined with robust regulatory frameworks, is essential to balance innovation with fairness. By embracing these principles, the financial industry can harness AI as a powerful enabler of financial inclusion, ultimately creating a more equitable credit ecosystem for underserved communities.
 
Keywords: 
AI; ML; Financial Inclusion; Credit Scoring Equity; Algorithmic Bias; Ethical AI Practices
 
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