h-index: 18     i10-index: 25

Integrated Risk Management: Banking and Chemical Safety

Document Type : Original Research Article

Authors

1 Department of Chemical Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos, Nigeria

2 Department of Biochemistry, Faculty of Basic Medical Sciences, University of Lagos, College of Medicine, Idi -Araba, Lagos State, Nigeria

3 Department of Accounting Faculty of Management Science University of Lagos Akoka, Lagos, Nigeria.

4 Department of Biochemistry Faculty of Pure and Applied Sciences Federal University of Wukari, Wukari Taraba State, Nigeria

5 Department of Risk Management & Data Analytics, Faculty of Business, Ohio Dominican University, Columbus, U.S.A

6 Department of Accounting & Finance, Faculty of Humanities, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

7 Department of Computer Science, Faculty of Science and Technology, Babcock University, Ilishan, Nigeria

Abstract
This study conducted a comparative conceptual analysis of risk management approaches in banking and chemical processing industries, concentrating on credit risk management and process safety risk management, respectively. The research investigated underlying frameworks, including Basel II/III and IFRS 9 in banking, and HAZOP, LOPA, and Quantitative Risk Assessment (QRA) in chemical safety, revealing significant similarities in risk identification, assessment, mitigation, and monitoring. Parallels in quantitative tools were identified between credit risk indicators (Probability of Default, Loss Given Default, and Exposure at Default) and safety metrics (frequency, consequence analysis, risk matrices). The study examined artificial intelligence applications in predictive modeling for both credit defaults and process safety incidents, uncovering methodological overlap that enhances foresight and decision-making. Human and organisational influences on risk perception and control were investigated. Results produced a conceptual Integrated Risk Management Framework demonstrating the viability of applying unified risk principles across highly regulated but diverse industrial contexts. The framework integrates three pillars: predictive intelligence (leveraging advanced analytics, AI, and historical data), quantitative evaluation (systematic measurement of likelihood, severity, and exposure), and human systems management (addressing behavioural biases and fostering risk-aware cultures). This cross-sectoral approach enables knowledge transfer, establishes unified risk language, and promotes AI development synergies. The framework requires empirical validation through case studies and pilot implementations for practical application across financial and industrial sectors.

Graphical Abstract

Integrated Risk Management: Banking and Chemical Safety

Keywords

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©2026 The author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit: http://creativecommons.org/licenses/by/4.0/

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Volume 7, Issue 2
Spring 2026
Pages 149-158

  • Receive Date 25 October 2025
  • Revise Date 21 November 2025
  • Accept Date 25 November 2025