Analyzing Credit Risk Management in the Digital Age: Challenges and Solutions
DOI:
https://doi.org/10.71222/ps8sw070Keywords:
credit risk management, artificial intelligence, machine learning, big data, blockchain, regulatory challengesAbstract
This review examines the evolution of credit risk management in the digital age, highlighting the transformative impact of emerging technologies such as big data, artificial intelligence (AI), machine learning (ML), and blockchain. Traditional credit risk management methods, which primarily relied on credit scores and financial statements, are being enhanced by these digital tools, enabling more accurate and real-time assessments of creditworthiness. This paper explores the key challenges faced in digital credit risk management, including data privacy and security concerns, algorithmic bias, and regulatory gaps. Furthermore, it provides insights into the strengths and weaknesses of both traditional and digital approaches, with a focus on how different industries are adopting digital technologies to manage credit risk. Finally, the paper discusses the future landscape of credit risk management, emphasizing the need for robust governance and regulatory frameworks to ensure the ethical and fair use of digital tools in the credit industry.
References
1. H. Nobanee, N. O. D. Ellili, D. Chakraborty, and H. Z. Shanti, “Mapping the fintech revolution: how technology is transforming credit risk management,” Glob. Knowl. Mem. Commun., vol. 10, no. 1108, pp. 49–92, 2024, doi: 10.1108/GKMC-12-2023-0492.
2. N. G. Packin and Y. Lev‐Aretz, “Decentralized credit scoring: Black box 3.0,” Am. Bus. Law J., vol. 61, no. 2, pp. 91–111, 2024, doi: 10.1111/ablj.12240.
3. Z. Tong, Y. Hu, C. Jiang, and Y. Zhang, “User financial credit analysis for blockchain regulation,” Comput. Electr. Eng., vol. 113, p. 109008, 2024, doi: 10.1016/j.compeleceng.2023.109008.
4. R. Damaševičius, N. Bacanin, and A. Nayyar, “Blockchain technology for a trustworthy social credit system: implementation and enforcement perspectives,” Cluster Comput., vol. 28, no. 3, p. 162, 2025, doi: 10.1007/s10586-024-04890-w.
5. M. Ali, A. Razaque, J. Yoo, U. R. Kabievna, A. Moldagulova, S. Ryskhan, and A. Kassymova, “Designing an intelligent scoring system for crediting manufacturers and importers of goods in Industry 4.0,” Logistics, vol. 8, no. 1, p. 33, 2024, doi: 10.3390/logistics8010033.
6. B. Karadag, A. H. Zaim, and A. Akbulut, “Blockchain based KYC model for credit allocation in banking,” IEEE Access, 2024, doi: 10.1109/ACCESS.2024.3410874.
7. H. Chen, N. Wei, L. Wang, W. Mubarak, M. A. Albahar, and Z. A. Shaikh, “The role of blockchain in finance beyond crypto-currency: trust, data management, and automation,” IEEE Access, 2024, doi: 10.1109/ACCESS.2024.3395918.
8. M. Tigges, S. Mestwerdt, S. Tschirner, and R. Mauer, “Who gets the money? A qualitative analysis of fintech lending and credit scoring through the adoption of AI and alternative data,” Technol. Forecast. Soc. Change, vol. 205, p. 123491, 2024, doi: 10.1016/j.techfore.2024.123491.
