Artificial intelligence systems for assessing creditworthiness
DOI:
https://doi.org/10.5281/zenodo.15648565Keywords:
Artificial Intelligence, Creditworthiness, Machine Learning, Credit Risk, Financial Technology, Alternative Data, AI Models, Credit Scoring, Financial Institutions, Credit Data Analytics.Abstract
This paper explores the application of Artificial Intelligence (AI) systems in assessing creditworthiness. AI-based
models leverage large datasets, machine learning, and advanced analytics to evaluate the credit risk of individuals
and organizations more accurately and efficiently than traditional methods. By incorporating alternative data sources, AI
systems can provide deeper insights into the financial behavior of borrowers, enhancing the decision-making process for
credit institutions. This study examines the effectiveness, advantages, and challenges of using AI in credit assessments
and highlights its potential to revolutionize the financial sector.
References
Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias: There’s software used across the country to
predict future criminals. And it’s biased against blacks. ProPublica.
Binns, R. (2018). Artificial intelligence in financial services: Disrupting credit scoring models. Journal of Financial
Technology, 3(1), 42-58.
Brynjolfsson, E., & McAfee, A. (2017). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant
Technologies. W.W. Norton & Company.
O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown
Publishing.
Zhao, X., Zhang, J., & Wang, L. (2019). Alternative data in credit scoring: A new frontier in financial inclusion. Journal
of Banking & Finance, 101, 1-14.
Rajan, R. G., & Zingales, L. (2003). The great reversals: The politics of financial development in the 20th century.
Journal of Financial Economics, 69(1), 5-50.
Xu, Y., & Li, T. (2020). Data privacy and protection in AI-powered credit scoring systems. International Journal of Data
Privacy and Security, 14(2), 98-112.
OECD. (2021). The Impact of Artificial Intelligence on the Financial Sector. OECD Publishing.
World Bank. (2020). Fintech and the Future of Finance. World Bank Group.
SHAP Documentation. (2023). Shapley Additive explanations.
Zest AI. (2022). Case Studies in AI-based Lending.
Usmonov, B. (2024). Evaluation of Capital Assets in Companies of Uzbekistan. Miasto Przyszłości, 52, 21-24.
Aharon, D. Y., Ali, S., & Naved, M. (2023). Too big to fail: The aftermath of Silicon Valley Bank (SVB) collapse and its
impact on financial markets. Research in International Business and Finance, 66, 102036.
Silicon Valley Bank balance sheet by FDIC. https://www.fdic.gov/system/files/2024-08/10539-6-30-2024-balancesheet.
Analysis: Silicon Valley Bank’s Bankruptcy
https://latribune.lazardfreresgestion.fr/en/analysis-silicon-valley-bank/
Usmonov, B. (2024). Evaluation of Efficiency of Capital Management in Joint-Stock Companies in the Chemical Sector:
The Case of Uzbekistan. In Development of International Entrepreneurship Based on Corporate Accounting and
Reporting According to IFRS (Vol. 33, pp. 67-74). Emerald Publishing Limited.
Burkhanov, A. U., Tursunov, B. O., Usmonov, B., & Mamayusupova, S. U. (2024). Assessment of Financial Security
of Joint-Stock Companies: The Case of Uzbekistan. In Development of International Entrepreneurship Based on
Corporate Accounting and Reporting According to IFRS: Part B (pp. 205-213). Emerald Publishing Limited.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 YASHIL IQTISODIYOT VA TARAQQIYOT
This work is licensed under a Creative Commons Attribution 4.0 International License.