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Prioritizing interdependent drivers of financial, economic, and political risks using a data-driven probabilistic approach

A Correction to this article was published on 14 March 2022

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Abstract

Financial, economic, and political risks pose a significant threat to the development and progress of countries as such risks can impact all spheres of life including education, healthcare, logistics, transportation, and safety among others. Although these risks seem quite distinct, they are mutually influenced by multidimensional interdependent factors such as internal and external conflict, socioeconomic conditions, corruption, law and order, and bureaucratic quality among others. In this paper, we utilize a data-driven approach to explore dependencies among factors influencing financial, economic, and political risks and establish their relative importance in a network setting while capturing the entire distribution of individual factors. A probabilistic network-based model was developed using the data by the International Country Risk Guide, which revealed significant differences between the conventional and the proposed schemes for prioritizing drivers of political, economic, and financial risks. Internal conflict and socioeconomic conditions were considered as the most critical factors in terms of reducing and enhancing the network-wide risk exposure, respectively. The two prioritization schemes relative to the vulnerability and resilience impact of individual factors are not correlated and therefore, policy-makers need to focus on both schemes while developing risk mitigation strategies.

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Correspondence to Abroon Qazi.

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The original online version of this article was revised: Modifications have been made in the text and references. Full information regarding the corrections made can be found in the erratum/correction for this article.

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Qazi, A., Simsekler, M.C.E. Prioritizing interdependent drivers of financial, economic, and political risks using a data-driven probabilistic approach. Risk Manag 24, 164–185 (2022). https://doi.org/10.1057/s41283-022-00089-8

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Keywords

  • Financial risks
  • Conflict
  • Socioeconomic conditions
  • Corruption
  • Data-driven approach
  • International country risk guide
  • Bayesian belief network