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1711761 Vol 9 · Issue 5 Download Paper

Integrating Technology for Enhanced Audit Efficiency and Risk Management in Real Estate Banking

Solomon Odoh

Subject area: Management and Commerce  ·  Area of research: Tech-Enabled Audit Management

DOI: https://doi.org/10.64388/IREV9I5-1711761-2004

Abstract

The meeting point of technology and real estate banking has radically changed audit processes and risk management structures. This paper evaluates the combination of developing technologies (including artificial intelligence (AI), machine learning (ML), blockchain, robotic process automation (RPA), and cloud computing) into real estate audit operations within banking institutions.Through review of current applications, case studies, and industry shifts, this research shows how technology acceptance improves audit performance, improves risk review accuracy, and strengthens legal requirements. Banking is expected to produce a yearly capacity of USD 200 billion to USD 340 billion of added economic value from AI uptake, mostly from increased productivity. The findings show that integrated technology solutions reduce audit cycle times by up to 50 percent, increase property value accuracy by 15 to 30 percent, and permit instant monitoring skills previously impossible through manual processes. This study examines the powerful role of technology in improving performance review and risk management in the real estate banking sector. By boosting machine learning and automation, financial institutions can ease audit processes, find potential risks, and make calculated decisions. This detailed framework examines the planned rollout of artificial intelligence, machine learning, robotic process automation, blockchain, cloud computing, and advanced metrics to change traditional manual audit processes into intelligent, automated, and fact-based tasks that deliver better risk insights while reducing costs and response times.

Keywords

Real Estate Banking, Audit Technology, Artificial Intelligence, Risk Management, Digital Transformation, Property Valuation, Compliance Automation.

References

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[4] Office of the Comptroller of the Currency. (2021). Model Risk Management: Comptroller's Handbook. OCC Publications.

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[6] Gartner. (2023). Predicts 2023: AI and data science solutions drive new opportunities. Gartner Research.

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[8] Redman, T. C. (2018). If your data is bad, your machine learning tools are useless. Harvard Business Review, March. Davenport, T., & Westerman, G. (2018). Why so many high-profile digital transformations fail. Harvard Business Review. Retrieved from https://ijbssrnet.com/index.php/ijbssr/article/view/181

[9] FinTech Magazine. (2024, January 5). Digital banking transformation: Accelerating into 2024. Retrieved from https://fintechmagazine.com/articles/digital-banking-transformation-accelerating-into-2024

[10] McKinsey & Company. (2020, May 14). Welcome to the digital factory: The answer to how to scale your digital transformation. McKinsey Digital. Retrieved from https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/welcome-to-the-digital-factory-the-answer-to-how-to-scale-your-digital-transformation

[11] McKinsey & Company. (2024, August 7). What is digital transformation? McKinsey Explainers. Retrieved from https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-digital-transformation

[12] Papathomas, P., & Konteos, G. (2024). The impact of digital transformation on banking operations. World Journal of Advanced Research and Reviews. Retrieved from https://wjarr.com/sites/default/files/WJARR-2024-2706.pdf

[13] Product School. (n.d.). 15 digital transformation frameworks for impactful change. Retrieved from https://productschool.com/blog/digital-transformation/digital-transformation-framework

[14] Verhoef, P. C. (2019). Digital transformation and business model innovation. In A framework for digital transformation and business model innovation (pp. 1-15).

[15] Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading digital: Turning technology into business transformation. Harvard Business Review Press.

[16] Whatfix. (2024, December 23). Digital transformation in real estate (+Examples, Challenges). Retrieved from https://whatfix.com/blog/real-estate-digital-transformation/

[17] World Economic Forum. (n.d.). Digital transformation: Future of real estate. Retrieved from https://www.weforum.org/realestate/digital-transformation/

How to cite this paper

Solomon Odoh "Integrating Technology for Enhanced Audit Efficiency and Risk Management in Real Estate Banking" Iconic Research And Engineering Journals Volume 9 Issue 5 2025 Page 218-223 https://doi.org/10.64388/IREV9I5-1711761-2004
Solomon Odoh "Integrating Technology for Enhanced Audit Efficiency and Risk Management in Real Estate Banking" Iconic Research And Engineering Journals, vol. 9, no. 5, Nov. 2025, doi: https://doi.org/10.64388/IREV9I5-1711761-2004
Solomon Odoh (2025). Integrating Technology for Enhanced Audit Efficiency and Risk Management in Real Estate Banking. Iconic Research And Engineering Journals, 9(5). doi: https://doi.org/10.64388/IREV9I5-1711761-2004
Solomon Odoh "Integrating Technology for Enhanced Audit Efficiency and Risk Management in Real Estate Banking" Iconic Research And Engineering Journals, vol. 9, no. 5, Nov. 2025. Crossref, https://doi.org/10.64388/IREV9I5-1711761-2004
@article{1711761,
      author = {Solomon Odoh},
      title = {Integrating Technology for Enhanced Audit Efficiency and Risk Management in Real Estate Banking},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {5},
      pages = {218-223},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1711761.pdf},
      abstract = {The meeting point of technology and real estate banking has radically changed audit processes and risk management structures. This paper evaluates the combination of developing technologies (including artificial intelligence (AI), machine learning (ML), blockchain, robotic process automation (RPA), and cloud computing) into real estate audit operations within banking institutions.Through review of current applications, case studies, and industry shifts, this research shows how technology acceptance improves audit performance, improves risk review accuracy, and strengthens legal requirements. Banking is expected to produce a yearly capacity of USD 200 billion to USD 340 billion of added economic value from AI uptake, mostly from increased productivity. The findings show that integrated technology solutions reduce audit cycle times by up to 50 percent, increase property value accuracy by 15 to 30 percent, and permit instant monitoring skills previously impossible through manual processes. This study examines the powerful role of technology in improving performance review and risk management in the real estate banking sector. By boosting machine learning and automation, financial institutions can ease audit processes, find potential risks, and make calculated decisions. This detailed framework examines the planned rollout of artificial intelligence, machine learning, robotic process automation, blockchain, cloud computing, and advanced metrics to change traditional manual audit processes into intelligent, automated, and fact-based tasks that deliver better risk insights while reducing costs and response times.},
      keywords = {Real Estate Banking, Audit Technology, Artificial Intelligence, Risk Management, Digital Transformation, Property Valuation, Compliance Automation.},
      month = {November},
      doi = {https://doi.org/10.64388/IREV9I5-1711761-2004}
  }