AI - Massive Data Management Conducive to Optimal VAR Model Making

Authors

  • Reza G Hamzaee BOG-Distinguished Professor Emeritus of Economics, Missouri Western State University, St. Joseph, MO, USA and RMD Hamzaee Econometrics International Consultants LLC, Overland Park, USA
  • Maryam Salimi Instructor of Infographics, Mass Media, and Data Processing Management, Sooreh University, Tehran, Iran

DOI:

https://doi.org/10.14738/abr.1403.20109

Abstract

While a comparative review of some selected applicable and controversial aspects of AI is presented in addressing both academic and operational concerns, respecting the supremacy of credible data, the data massiveness, and the significance of varying essential facts, instead of hypothesizing a predetermined economic or any other scientific models, we are proposing a data-based Vector Autoregression (VAR) methodology for AI optimal application to the ongoing fraud and anti-fraud structure and hence, more effective policymaking. It may include many other macro or microeconomic policy optimization. Hopefully, the entire attempt will portend some tangible prospective contribution in an achievable positive societal change. Our adopted data will be compiled in a broad international and national similarly surveyed source by Dorris (2022) and/or various governmental fraud data sources.

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Published

2026-03-23

How to Cite

Hamzaee, R. G., & Salimi, M. (2026). AI - Massive Data Management Conducive to Optimal VAR Model Making. Archives of Business Research, 14(03), 13–23. https://doi.org/10.14738/abr.1403.20109