Blockchain Technology in Mortgage Banking: A Solution to Housing Affordability and Security Concerns in the U.S.

OLAYINKA ODUTOLA1, OYINDAMOLA IWALEHIN2, ELIZABETH MODUPE DOPEMU3
123Independent Researcher
DOI: http://doi.org/10.37502/IJSMR.2021.4811

Abstract

This study explored the integration of blockchain technology into mortgage banking operations to address key issues such as housing affordability, process efficiency, and security concerns in the U.S. The research investigated how blockchain could streamline mortgage processes, ensure transparency, and mitigate risks associated with traditional banking systems. It drew on insights from the challenges faced by Nigerian mortgage banks, particularly the impact of political instability on financial markets. By leveraging the potential of blockchain to enhance operational transparency and efficiency, the study aimed to provide a comprehensive understanding of its benefits and limitations. This research contributed to the broader discourse on technological innovations in financial services and offered actionable recommendations for improving mortgage banking systems through advanced technological solutions.

Keywords: blockchain technology, mortgage banking, housing affordability, financial transparency, U.S. mortgage market

References

  • Laroiya, C., Saxena, D., & Komalavalli, C. (2020). Applications of blockchain technology. In Handbook of research on blockchain technology (pp. 213-243). Academic Press.
  • Odutola, A. A. (2021). Modeling the intricate association between sustainable service quality and supply chain performance with mediating role of blockchain technology in America. International Journal of Multidisciplinary Research and Studies, 4(07), 17. https://doi.org/10.5281/zenodo.12788813
  • Ravindran, U., & Vamsi, P. R. (2021, August). A secure blockchain-based finance application. In Proceedings of the 2021 Thirteenth International Conference on Contemporary Computing (pp. 99-109).
  • Uzowuru, I. M., Odutola, O., Adetoro, A., Moromoke, O. A., & Rajani, P. (2021). Optimized machine learning models for predictive analysis: AI-driven analytical tools for enhanced credit risk assessment. Journal of Financial Data Science, 7(1), 112-128