The lack of access to finance is a major issue for young entrepreneurs, especially those who have poor credit scores, lack financial statements and have no collateral. Artificial intelligence (AI) based credit scoring systems have been seen as a potential solution to credit assessment using alternative data, but there are concerns about fairness, transparency, accountability and algorithmic exclusion. This study aims to understand the impact of AI-powered credit scoring on financial justice for young entrepreneurs in Dhaka City, focusing on perceptions of financial access, AI-driven lending opportunities, ethical concerns, and responsible decision-making practices. The study uses a qualitative research approach and thematic analysis to explore the experiences and perspectives of entrepreneurs on traditional credit assessment systems and AI-powered lending mechanisms. The results show that AI-powered credit scoring is seen as a potential means to financial inclusion, as it can include alternative data like digital transaction records and business activity data. But there are also important issues of algorithmic bias, data quality, privacy, transparency, and the risk of leaving out people with small digital footprints, participants note. The study also shows that in the context of AI-driven lending, financial justice encompasses not merely the opportunity to obtain a loan, but also procedural fairness, transparency or explain ability, accountability, and the ability to object to or challenge automated decisions. The study adds to the literature on responsible AI and financial inclusion by offering a socio-technical approach that combines technological innovation and human-centered governance. The results indicate that AI-based credit scoring can contribute to inclusive entrepreneurial finance if applied in an ethically guided, transparent, and accountable manner with proper human supervision.

REFERENCES

[1].Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012

[2].Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671–732. https://doi.org/10.15779/Z38BG31

[3].Berg, T., Burg, V., Gombović, A., & Puri, M. (2020). On the rise of fintechs: Credit scoring using digital footprints. The Review of Financial Studies, 33(7), 2845–2897. https://doi.org/10.1093/rfs/hhz099

[4].Fuster, A., Goldsmith-Pinkham, P., Ramadorai, T., & Walther, A. (2022). Predictably unequal? The effects of machine learning on credit markets. The Journal of Finance, 77(1), 5–47. https://doi.org/10.1111/jofi.13090

[5].Hand, D. J., & Henley, W. E. (1997). Statistical classification methods in consumer credit scoring: A review. Journal of the Royal Statistical Society: Series A (Statistics in Society), 160(3), 523–541. https://doi.org/10.1111/1467-985X.00078

[6].Lessmann, S., Baesens, B., Seow, H. V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research, 247(1), 124–136. https://doi.org/10.1016/j.ejor.2015.05.030

[7].Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), Article 115. https://doi.org/10.1145/3457607

[8].Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x

[9].Thomas, L. C., Crook, J. N., & Edelman, D. B. (2017). Credit scoring and its applications (2nd ed.). Society for Industrial and Applied Mathematics