AI-Driven Credit Scoring Systems and Financial Inclusion in Emerging Markets

• Author(s): Iboro Akpan Essien ; Geraldine Chika Nwokocha ; Eseoghene Daniel Erigha ; Ehimah Obuse ; Ayorinde Olayiwola Akindemowo
• Paper ID: 1710189
• Page: 517-534
• Published Date: 31-05-2019
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 2 Issue 11 May-2019

Abstract

Artificial Intelligence (AI)-driven credit scoring systems are rapidly transforming the financial landscape in emerging markets, offering promising solutions to address long-standing challenges of financial exclusion. Traditional credit assessment models, which rely heavily on formal credit histories and collateral, often fail to accommodate low-income individuals, informal workers, and micro-entrepreneurs who lack access to formal banking systems. AI-powered credit scoring leverages alternative data sources such as mobile phone usage, digital payment histories, utility bills, social media activity, and psychometric profiles to evaluate creditworthiness. By applying machine learning algorithms and predictive analytics, these systems can identify credit risks with greater speed, accuracy, and inclusiveness than conventional models. This explores the role of AI-driven credit scoring in promoting financial inclusion in emerging markets. It examines the technological foundations of these systems, highlighting how alternative data and AI techniques such as neural networks and decision trees are used to create dynamic, adaptive credit models. This also analyzes the key opportunities these systems present, including expanded credit access for underserved populations, reduced loan processing times, and the development of personalized credit products suited to diverse financial needs. However, this also addresses significant risks and challenges, including concerns over data privacy, algorithmic bias, lack of transparency in AI decision-making, and regulatory gaps in emerging markets. To mitigate these risks, this recommends best practices such as ethical AI guidelines, fairness audits, robust data governance, and explainable AI tools. Finally, it outlines future directions, including cross-sector collaboration, investment in digital literacy, and the creation of global standards for responsible AI credit scoring. This concludes that while AI-powered credit scoring systems offer substantial potential to foster financial inclusion, their success depends on balancing innovation with fairness, accountability, and regulatory oversight to ensure equitable and sustainable financial access in emerging markets.

Keywords

AI-driven, Credit scoring systems, Financial inclusion, Emerging Markets

Citations

IRE Journals:
Iboro Akpan Essien , Geraldine Chika Nwokocha , Eseoghene Daniel Erigha , Ehimah Obuse , Ayorinde Olayiwola Akindemowo "AI-Driven Credit Scoring Systems and Financial Inclusion in Emerging Markets" Iconic Research And Engineering Journals Volume 2 Issue 11 2019 Page 517-534

IEEE:
Iboro Akpan Essien , Geraldine Chika Nwokocha , Eseoghene Daniel Erigha , Ehimah Obuse , Ayorinde Olayiwola Akindemowo "AI-Driven Credit Scoring Systems and Financial Inclusion in Emerging Markets" Iconic Research And Engineering Journals, 2(11)