Fairness in AI-Powered Financial Services: Bias in Credit Scoring and Lending
Keywords:
AI, credit scoring, fairness, financial services, lendingAbstract
It is by illegally handling AI in credit scoring and lending within the fintech industry that the financial sector has been presented with efficiency, automation and trans-national access to credit, however, these systems may be subjective to the historical data, lending data, and unfair outcomes of individual or communities. The results of AI-based credit scoring may lead to the discrimination of access to credit and financial exclusion. By 2026, predictive lending algorithms will require AI to focus on the future of balancing prediction accuracy with strict debaising strategies such as adversarial training and disparate impact tracking systematic trust and equitable access to capital. It is a critical conflict that was proposed in the recent study of 2024-2026, and as it is stated, it is the high powered services and the artificial intelligence in credit scoring. Prejudice AI models can lead to the unequal availability of funding, the rise of interest rates or the automatic refusal of claims to the loans of deprived groups in society. This nullifies the trust of the financial institutions and the financial inclusion idea. Besides that, automated decisions cannot be made or contested by the consumer due to the complexity and obscury of most AI models, an issue of transparency and accountability. Fairness in AI-based financial services can only be achieved through a multi-dimensional approach. This involves the use of various and representative data, fairness sensitive algorithms, periodic bias audits and explain ability in decision making. Human analysis is still necessary to analyze areas of high risk lending decisions and correct anomalies. Also, the regulatory and ethical frameworks are important to establish the expectations of responsible use of AI.
References
[1] Darji, R. (2025). AI-driven credit scoring and financial inclusion in India: Opportunities and challenges. Research Review International Journal of Multidisciplinary Studies, 10(2), 45–52.
[2] Gupta, A. (2025). Ethical implications of artificial intelligence in credit scoring: An Indian perspective. Journal of Scientific Advances, 4(1), 12–20.
[3] Gupta, P. (2022). Application of AI-based credit scoring models in rural India. Indian Research Transactions, 3(4), 78–85.
[4] Halder, S., & Deshmukh, R. (2025). Transformation of credit risk assessment through artificial intelligence in Indian banking. International Tax Journal, 6(1), 101–110.
[5] Sejwal, R., & Gupta, M. (2026). Automated decision-making and fairness in Indian credit systems under data protection regulations. In Proceedings of the International Conference on Smart AI Systems (pp. 55–63). Atlantis Press.
[6] Daakshayani, N. S. (2026). Fairness evaluation in machine learning models for lending systems. International Journal of Computer Technology, 8(2), 34–42.
[7] Vakrani, K., Patel, D., & Shah, R. (2026). Comparative analysis of AI-based and traditional credit scoring models in India. Journal of Financial Innovation, 5(3), 89–98.
[8] Mahabub Basha, S., et al. (2025). An Empirical Study on Green Portfolio Management. Indian Journal of Information Sources and Services, 15(3), 444–449.
[9] Kamble, P., & Choudhari, S. (2024). AI-based credit scoring and financial inclusion: Evidence from India. In Proceedings of INCON 2024 (pp. 120–128).
[10] Sambasivan, N., et al. (2021). Re-imagining algorithmic fairness in India and beyond. ACM FAccT Conference, 315–328.
[11] OpenCredit Research Initiative. (2025). Fairness and bias in AI-driven credit scoring systems in India. Whitepaper Series.
[12] Sharma, V., & Singh, R. (2023). Artificial intelligence in Indian banking: Opportunities and challenges. International Journal of Banking Technology, 7(2), 55–63.
[13] Iyer, L. S., & Venkatraman, S. (2022). Role of AI in financial services: Indian fintech perspective. Journal of Financial Services Marketing, 27(4), 210–222.
[14] Reserve Bank of India (RBI). (2023). Report on Trend and Progress of Banking in India. RBI Publications.
[15] NITI Aayog. (2021). Responsible AI for All: Strategy for India. Government of India.
[16] Bhatia, A., & Kumar, S. (2024). Machine learning models in credit risk assessment: Evidence from Indian banks. Indian Journal of Finance, 18(1), 23–35.
[17] Ghosh, S. (2023). Digital lending and AI adoption in India: Risks and regulatory challenges. Economic and Political Weekly, 58(12), 45–52.
[18] Kaur, P., & Kaur, G. (2022). AI-driven financial inclusion in India: A conceptual study. Journal of Emerging Technologies, 5(3), 88–96.
[19] Agarwal, R., & Jain, P. (2023). Algorithmic bias in financial technologies: An Indian perspective. International Journal of Ethics in AI, 2(1), 14–27.
[20] Singh, A., & Mishra, P. (2024). Credit scoring using machine learning techniques in Indian financial institutions. Journal of Data Science and Analytics, 9(2), 101–115.
[21] Mehta, D., & Shah, H. (2023). Explainable AI in banking: Improving transparency in Indian credit systems. Journal of AI Research India, 6(1), 33–42.
[22] Das, S., & Banerjee, A. (2022). Financial inclusion and fintech growth in India: Role of AI. Indian Economic Review, 57(2), 145–160.
[23] Chatterjee, S., & Kar, A. K. (2023). AI adoption in Indian banking sector: Challenges and future directions. Global Business Review, 24(5), 789–805.
[24] PwC India. (2022). AI in Financial Services: India Outlook Report.
[25] Deloitte India. (2023). AI and Risk Management in Banking Sector.
[26] Narayanan, A., & Kapoor, N. (2024). Ethical AI and governance in Indian financial systems. Journal of Business Ethics India, 11(2), 67–79.
[27] Verma, S., & Yadav, R. (2023). Predictive analytics in credit scoring: Indian case study. International Journal of Financial Studies, 11(3), 95–108.
[28] Infosys Knowledge Institute. (2023). AI-powered Banking in India: Trends and Insights.
[29] Joshi, M., & Patil, V. (2022). Bias mitigation techniques in machine learning for Indian datasets. Journal of Computer Science India, 14(2), 56–70.
[30] KPMG India. (2023). Responsible AI in Banking and Financial Services.
