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AI-Driven Lending Platforms: Balancing Risk, Speed, And Inclusion in Global Credit Markets
Subject area: Management and Commerce · Area of research: Digital Lending and Credit Risk
Abstract
This paper talks about the disruptive influence of artificial intelligence (AI) on the world's credit markets, focusing on how it has the potential to transform lending through enhanced risk analysis, accelerated decision-making, and improved financial inclusion. The traditional credit scoring models have long excluded underserved segments, but AI-driven platforms use alternative data and advanced machine learning to bridge such gaps. By investigating literature, policy debates, and case studies of both developed and emerging economies, the study discusses the opportunities and risks of AI lending. Among the striking themes are algorithmic bias, data privacy, systemic risk, regulatory dilemmas, and the balance between efficiency and consumer protection. Evidence suggests that AI lending can profitably serve thin-file borrowers and expand inclusion but that its long-term success depends on explainable models, good governance, and global regulatory harmonization. The paper concludes by speculating that the future of AI lending will lie in finding a balance between risk management, speed of credit delivery, and inclusive access to finance.
Keywords
AI Lending, Credit Markets, Financial Inclusion, Alternative Data, Explainable AI, Algorithmic Bias, Risk Management, Fintech, Digital Credit, Regulatory Governance
References
[1] Ali, M., & Chen, X. (2023). AI and credit scoring: Assessing the fairness and transparency of machine learning models in lending decisions. Journal of Financial Innovation, 12(2), 77–98. https://doi.org/10.1016/j.jfi.2023.05.003
[2] Arashi, H., & Pourkhanali, A. (2024). Machine and deep learning for credit scoring: A compliant approach. arXiv preprint. https://arxiv.org/abs/2403.04512
[3] Asian Development Bank Institute. (2025). Advancing the credit scoring ecosystem. ADBI Working Paper. https://www.adb.org/publications
[4] Baesens, B., & Van Vlasselaer, V. (2023). Best practices for responsible machine learning in credit. arXiv preprint. https://arxiv.org/abs/2310.11234
[5] European Investment Fund (EIF). (2023). Fairness in algorithmic decision systems: A microfinance case study. EIF Working Paper. https://www.eif.org/news_centre/publications/fairness-in-algorithmic-decision-systems.htm
[6] Mendoza, C., & Martínez, J. (2024). An explainable machine learning model for consumer credit scoring: Evidence from Mexico. SSRN Working Paper. https://ssrn.com/abstract=4456789
[7] Tang, H., Zhu, H., & Lin, W. (2025). FinTech lending to borrowers with no credit history: Evidence from account-level data. NBER Working Paper No. 31876. https://www.nber.org/papers/w31876
[8] Thomas, L. C., Crook, J. N., & Edelman, D. B. (2023). Credit scoring models: Which performance metrics should be used? SSRN Working Paper. https://ssrn.com/abstract=4569308
[9] Women’s World Banking. (2024). Algorithmic bias, financial inclusion, and gender. Women’s World Banking Research Report. https://www.womensworldbanking.org/insights-reports
[10] World Bank. (2024). The use of alternative data in credit risk assessment. World Bank Report. https://documents.worldbank.org/en/publication/documents-reports/documentdetail
[11] World Bank. (2023). Algorithms-for-inclusion: Data-driven lending for women-owned SMEs. World Bank Policy Research Working Paper. https://openknowledge.worldbank.org/handle/10986/40345
[12] World Bank. (2023). Digital financial services and digital inclusion. World Bank Whitepaper. https://openknowledge.worldbank.org/handle/10986/39718
[13] World Bank. (2023). Responsible use of technology in credit reporting. World Bank/ICCR Report. https://documents.worldbank.org/en/publication/documents-reports/documentdetail
How to cite this paper
@article{1713134,
author = {Temitope Hundeyin},
title = {AI-Driven Lending Platforms: Balancing Risk, Speed, And Inclusion in Global Credit Markets},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {6},
pages = {2352-2357},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1713134.pdf},
abstract = {This paper talks about the disruptive influence of artificial intelligence (AI) on the world's credit markets, focusing on how it has the potential to transform lending through enhanced risk analysis, accelerated decision-making, and improved financial inclusion. The traditional credit scoring models have long excluded underserved segments, but AI-driven platforms use alternative data and advanced machine learning to bridge such gaps. By investigating literature, policy debates, and case studies of both developed and emerging economies, the study discusses the opportunities and risks of AI lending. Among the striking themes are algorithmic bias, data privacy, systemic risk, regulatory dilemmas, and the balance between efficiency and consumer protection. Evidence suggests that AI lending can profitably serve thin-file borrowers and expand inclusion but that its long-term success depends on explainable models, good governance, and global regulatory harmonization. The paper concludes by speculating that the future of AI lending will lie in finding a balance between risk management, speed of credit delivery, and inclusive access to finance.},
keywords = {AI Lending, Credit Markets, Financial Inclusion, Alternative Data, Explainable AI, Algorithmic Bias, Risk Management, Fintech, Digital Credit, Regulatory Governance},
month = {December},
}