Home / Current Issue / Paper 1708888
Designing Inclusive and Scalable Credit Delivery Systems Using AI-Powered Lending Models for Underserved Markets
Subject area: Science,Engineering and Technology · Area of research: Financial Inclusion
Abstract
This paper explores the transformative role of artificial intelligence in designing inclusive and scalable credit delivery systems for underserved populations. In many emerging markets, access to credit remains a persistent barrier to economic empowerment due to structural, informational, and socio-economic limitations. Traditional lending frameworks often fail to accommodate individuals and small enterprises lacking formal financial histories, thereby perpetuating cycles of exclusion. The study examines how AI-driven models?leveraging alternative data sources such as mobile usage, digital payments, and utility records?can generate more equitable credit profiles and enhance risk assessment accuracy. It further outlines the application of machine learning techniques across the credit lifecycle, from onboarding and credit scoring to disbursement and repayment monitoring. Emphasis is placed on fairness, ethical deployment, and regulatory compliance, highlighting strategies to mitigate algorithmic bias and foster transparency. The paper also discusses the role of multistakeholder collaboration in building institutional trust and scaling AI-powered lending platforms responsibly. Ultimately, it argues that the integration of inclusive design principles with advanced AI methodologies can reshape financial systems to serve marginalized communities better and contribute to broader financial inclusion and economic resilience.
Keywords
Financial Inclusion, AI-Powered Lending, Alternative Credit Scoring, Machine Learning in Finance, Ethical AI Deployment, Scalable Credit Systems
How to cite this paper
@article{1708888,
author = {Sharon Nwani, Olayinka Abiola-Adams, Bisayo Oluwatosin Otokiti, Jeffrey Chidera Ogeawuchi},
title = {Designing Inclusive and Scalable Credit Delivery Systems Using AI-Powered Lending Models for Underserved Markets},
journal = {Iconic Research And Engineering Journals},
year = {2020},
volume = {4},
number = {1},
pages = {212-221},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708888.pdf},
abstract = {This paper explores the transformative role of artificial intelligence in designing inclusive and scalable credit delivery systems for underserved populations. In many emerging markets, access to credit remains a persistent barrier to economic empowerment due to structural, informational, and socio-economic limitations. Traditional lending frameworks often fail to accommodate individuals and small enterprises lacking formal financial histories, thereby perpetuating cycles of exclusion. The study examines how AI-driven models?leveraging alternative data sources such as mobile usage, digital payments, and utility records?can generate more equitable credit profiles and enhance risk assessment accuracy. It further outlines the application of machine learning techniques across the credit lifecycle, from onboarding and credit scoring to disbursement and repayment monitoring. Emphasis is placed on fairness, ethical deployment, and regulatory compliance, highlighting strategies to mitigate algorithmic bias and foster transparency. The paper also discusses the role of multistakeholder collaboration in building institutional trust and scaling AI-powered lending platforms responsibly. Ultimately, it argues that the integration of inclusive design principles with advanced AI methodologies can reshape financial systems to serve marginalized communities better and contribute to broader financial inclusion and economic resilience.},
keywords = {Financial Inclusion, AI-Powered Lending, Alternative Credit Scoring, Machine Learning in Finance, Ethical AI Deployment, Scalable Credit Systems},
month = {July},
}