Journal Press India^®

Author Details ( * ) denotes Corresponding author

This research paper aims to investigate and compare conventional creditworthiness assessment methods with emerging AI-based approaches in the context of lending and financial decision-making. The conventional methods typically rely on historical financial data, credit scores, and collateral evaluation, while AI-based methods leverage machine learning algorithms to analyze alternative data sources and behavioral patterns. The paper will explore the advantages and limitations of each approach, considering factors such as accuracy, transparency, bias, scalability, and regulatory compliance. Additionally, the study will discuss the ethical implications and potential societal impacts of transitioning towards AI-based creditworthiness assessment methods. Through a comprehensive analysis, this research aims to provide insights into the evolution of credit risk assessment practices and inform stakeholders about the implications of adopting AI in lending decisions.

Keywords

Conventional Creditworthiness; Credit score; AI-based creditworthiness; PICS; Underserved; Alternate data

1. Abdou, H. A. & Pointon, J. (2011). Credit scoring, statistical techniques and evaluation criteria: A review of the literature. Intelligent Systems in Accounting Finance & Management, 18(2–3), 59–88. Retrieved from https://doi.org/10.1002/isaf.325
2. Abdou, H. A., Tsafack, M. D. D., Ntim, C. G., & Baker, R. D. (2016). Predicting creditworthiness in retail banking with limited scoring data. Knowledge-Based Systems, 103, 89–103. Retrieved from https://doi.org/10.1016/j.knosys.2016.03.023
3. Addy, N. W. A., Ajayi-Nifise, N. a. O., Bello, N. B. G., Tula, N. S. T., Odeyemi, N. O., & Falaiye, N. T. (2024). AI in credit scoring: A comprehensive review of models and predictive analytics. Global Journal of Engineering and Technology Advances, 18(2), 118–129. Retrieved from https://doi.org/10.30574/gjeta.2024.18.2.0029
4. Amarnadh, V. & Moparthi, N. R. (2023). Comprehensive review of different artificial intelligence-based methods for credit risk assessment in data science. Intelligent Decision Technologies, 17(4), 1265–1282. Retrieved from https://doi.org/10.3233/idt-230190
5. Aniceto, M. C., Barboza, F. & Kimura, H. (2020). Machine learning predictivity applied to consumer creditworthiness. Future Business Journal, 6(1). Retrieved from https://doi.org/10.1186/s43093-020-00041-w
6. Arya, S., Eckel, C., & Wichman, C. (2011). Anatomy of the credit score. Journal of Economic Behavior & Organization, 95, 175–185. Retrieved from https://doi.org/10.1 016/j.jebo.2011.05.005
7. Bary, E. (2018, October 29). How artificial intelligence could replace credit scores and reshape how we get loans. Marketwatch. Retrieved from https://www.marketwatch.com /story/ai-based-credit-scores-will-soon-give-one-billion-people-access-to-banking-services-2018-10-09
8. Bature, H. J., Wisdom, D. D., Dufuwa, T. T., & Ayetuoma, I. O. (2023). Credit default prediction system using machine learning. COMPUTOLOGY: Journal of Applied Computer Science and Intelligent Technologies, 3(1), 51–60. Retrieved from https://doi.org/10.17492/computology.v3i1.2305
9. Chatterjee, S., Corbae, D., Dempsey, K. & Ríos-Rull, J. (2023). A quantitative theory of the credit score. Econometrica, 91(5), 1803–1840. Retrieved from https://doi.org/10.39 82/ecta18771
10. CRIF High Mark. (n.d.). CRIF high mark: India’s full-service credit company. Retrieved from https://www.crifhighmark.com/about-us
11. Djeundje, V. B., Crook, J., Calabrese, R. & Hamid, M. (2020). Enhancing credit scoring with alternative data. Expert Systems with Applications, 163, 113766. Retrieved from https://doi.org/10.1016/j.eswa.2020.113766
12. Experian opens credit bureau in India. (n.d.). Retrieved from https://www.experianplc.c om/newsroom/press-releases/2010/12-08-2010
13. Fogarty, D. J. (2012). Using genetic algorithms for credit scoring system maintenance functions. International Journal of Artificial Intelligence & Applications, 3(6), 1–8. Retrieved from https://doi.org/10.5121/ijaia.2012.3601
14. Hurley, M. & Adebayo, J. (2016). Credit scoring in the era of big data. Yale Journal of Law and Technology, 18, 148. Retrieved from https://yjolt.org/sites/default/files/ hurley_18yjolt136_jz_proofedits_final_7aug16_clean_0.pdf
15. Kamalloo, E. & Abadeh, M. S. (2010). An artificial immune system for extracting fuzzy rules in credit scoring. IEEE Congress on Evolutionary Computation, 1–8. Retrieved from https://doi.org/10.1109/cec.2010.5586346
16. Khandani, A. E., Kim, A. J. & Lo, A. W. (2010). Consumer credit-risk models via machine-learning algorithms. Journal of Banking and Finance, 34(11), 2767–2787.
17. Kreiswirth, B., Schoenrock, P., & Singh, P. (2017). Using alternative data to evaluate creditworthiness. Consumer Financial Protection Bureau. Retrieved from https://busin ess.cch.com/bfld/CFPBBlog-Usingalternativedatatoevaluatecreditworthines s.pdf
18. Kumar, S. & Sharma, T. (2021). The growth in credit to priority sector by commercial banks in India: an inter-state analysis. PRAGATI Journal of Indian Economy, 8(1), 1–19. Retrieved from https://doi.org/10.17492/jpi.pragati.v8i1.812101
19. Malhotra, S., Dhanya, K. A., Prathibha, R. M. S. & Mohanty, P. (2025). AI-driven credit assessment in banks and non-banking finance companies (NBFCs) in India. In Advances in finance, accounting, and economics book series (pp. 275–292). Retrieved from https://doi.org/10.4018/979-8-3693-8186-1.ch011
20. Mpofu, T. & Mukoser, M. (2014). Credit scoring techniques: A survey. International Journal of Science and Research (IJSR), 3(8). Retrieved from https://www.academia.edu /download/113243648/MDIwMTUyNDM_.pdf
21. Muñoz-Cancino, R., Bravo, C., Ríos, S. A., & Graña, M. (2023). On the dynamics of credit history and social interaction features, and their impact on creditworthiness assessment performance. Expert Systems with Applications, 218, 119599. Retrieved from https://doi.org/10.1016/j.eswa.2023.119599
22. Patil, A. (2024). The role of AI: Transforming the landscape of Indian financial services. MUDRA Journal of Finance and Accounting, 11(2), 37–58. Retrieved from https://doi.org/10.17492/jpi.mudra.v11i2.1122403
23. Prince, A. E., & Schwarcz, D. (2019). Proxy discrimination in the age of artificial intelligence and big data. Iowa L. Review, 105, 1257. Retrieved from https://heinon line.org/HOL/LandingPage?handle=hein.journals/ilr105&div=35&id=&page=
24. Ramakrishnan, R., Rohella, P., Mimani, S., Jiwani, N. & Logeshwaran, J. (2024). employing AI and ML in risk assessment for lending for assessing credit worthiness. In International Conference on Knowledge Engineering and Communication Systems (ICKECS) (pp. 561–566). Retrieved from https://doi.org/10.1109/icdt61202.2024.10489 313
25. Sharma, S., Gola, K. R. & Ujjawal, N. (2023). Artificial intelligence- Applications and uses in the banking industry in India - A systematic literature review. International Journal of Management Issues and Research., 12(1), 115–124. Retrieved from https://doi.org/10.69711/sharda.ijmir.v12i1.2311
26. Shruti. (2020). NPA - in the context of Indian banking sector. International Journal of Management Issues and Research., 9(2), 120–124. Retrieved from https://doi.org/10.69 711/sharda.ijmir.v9i2.2008
27. Shukla, S. K., Mishra, A. & Pandey, D. (2023). Intelligent Credit: Unleashing the power of artificial intelligence in evaluating credit worthiness (1st ed., Vol. 1).
28. Sury, M. M. (2016). Rural credit co-operatives: significance, challenges and the road ahead. MANTHAN Journal of Commerce and Management, 3(2), 61-83. Retrieved from https://doi.org/10.17492/manthan.v3i2.7876
29. Taylor, W. F. (2018). The ECOA and disparate impact theory: A historical perspective. Journal of Law and Policy, 26(2), 575.
30. TRANSUNION CIBIL Company History. (n.d.). Retrieved from https://www.transunion cibil.com/about-us/company-history
31. What is a credit score? | Consumer Financial Protection Bureau. (2023, August 28). Consumer Financial Protection Bureau. Retrieved from https://www.consumerfinance. gov/ask-cfpb/what-is-a-credit-score-en-315/
32. Who we are | About us | Equifax. (n.d.). Retrieved from https://www.equifax.co.in/about-equifax/who-we-are/