Journal of Industrial and Systems Engineering

Journal of Industrial and Systems Engineering

Providing an innovative model for a modern intelligent banking system based on AIoT

Document Type : Research Paper

Author
Bentolhoda Aliahmadi
Department of Industrial Engineering, Islamic Azad University, Central Tehran Branch
Abstract
The fusion of Artificial Intelligence (AI) and the Internet of Things (IoT), known as AIoT, is revolutionizing industries by combining real-time data collection with intelligent decision-making. The banking sector stands to gain significantly from this paradigm, addressing challenges such as operational inefficiencies, cybersecurity threats, and limited personalization. This paper proposes an innovative model for a modern intelligent banking system powered by AIoT. The model integrates IoT-enabled devices, edge computing, machine learning algorithms, and secure cloud infrastructure to deliver personalized, adaptive, and secure banking services. Key features include real-time fraud detection, biometric authentication, predictive analytics for financial decision-making, and intuitive AI-driven customer interactions. A case study is presented to validate the model's effectiveness, demonstrating improved transaction efficiency, enhanced user experience, and strengthened security. This research highlights the transformative potential of AIoT in creating customer-centric, secure, and scalable banking solutions for the digital era. Future research directions are also discussed.
Keywords
smart banking
artificial intelligence of things
AIoT-based banking
conceptual framework
Subjects
Brown, A., & Singh, R. (2022). AI and IoT in Banking: Opportunities and Challenges. Journal of Fintech Innovations, 10(3), 45-56.
Chen, J., Lee, H., & Park, Y. (2019). IoT-enabled Fraud Detection in Banking: A Case Study. International Journal of Financial Technologies, 8(4), 101-115.
Gharachorloo, N., Nahr, J. G., & Nozari, H. (2021). SWOT analysis in the General Organization of Labor, Cooperation and Social Welfare of East Azerbaijan Province with a scientific and technological approach. International Journal of Innovation in Engineering, 1(4), 47-61.
Gupta, P., Sharma, A., & Verma, K. (2020). Biometric Authentication in Banking: A Review. Journal of Secure Computing, 12(2), 34-46.
Khan, S., Li, J., & Zhang, Y. (2022). Ethical and Regulatory Considerations in AIoT for Banking. AI and Ethics Journal, 5(1), 23-38.
Kumar, V., & Gupta, S. (2021). Personalized Banking Services Using AIoT. Proceedings of the International Conference on Emerging Technologies, 34(3), 223-234.
Li, F., & Zhang, L. (2021). AIoT in Banking: A Framework for Innovation. Journal of Digital Banking, 14(1), 56-78.
Nozari, H. (2024). Investigating Key Dimensions and Key Indicators of AIoT-Based Supply Chain in Sustainable Business Development. In Artificial Intelligence of Things for Achieving Sustainable Development Goals (pp. 293-310). Cham: Springer Nature Switzerland.
Nozari, H., & Szmelter-Jarosz, A. (2024). An Analytical Framework for Smart Supply Chains 5.0. In Building Smart and Sustainable Businesses With Transformative Technologies (pp. 1-15). IGI Global.
Nozari, H., Ghahremani-Nahr, J., Fallah, M., & Szmelter-Jarosz, A. (2022). Assessment of cyber risks in an IoT-based supply chain using a fuzzy decision-making method. International Journal of Innovation in Management, Economics and Social Sciences, 2(1).
Patel, V. (2021). Challenges in Implementing AIoT in Legacy Banking Systems. IoT Journal, 8(2), 89-100.
Sharma, M., Verma, S., & Gupta, R. (2021). Personalization in Banking through AIoT. Journal of Intelligent Systems, 17(3), 112-127.
Smith, J., & Brown, T. (2020). Security in IoT-Enabled Banking Systems. Journal of Cybersecurity, 15(5), 67-78.
Zhang, X., & Wang, H. (2020). Machine Learning in Credit Risk Assessment. Journal of Banking Research, 18(2), 56-73.

  • Receive Date 05 February 2024
  • Revise Date 17 April 2024
  • Accept Date 25 June 2024