Reputational risk management in conditions of credibility gap in the banking system

Abstract

Purpose This paper aims to provide appropriate and effective methods for diagnosing and managing the reputational risk of a bank. Design/methodology/approach Using the data-mining system CONFOR, the authors have defined and arranged in the objective way the factors influencing the reputational risk level of a bank, with specific factor groups assigned. The mathematical tools of the theory of graphs and cognitive mapping are used for building the cognitive map of reputational risk assessment of a bank. To enable choosing which risk management strategies to use, the methods of impulse modeling and scenario analysis are applied for the purpose of prognostication. Findings The authors proposed an integrated approach to the objective determination of the reputational risk level and an easy-to-use choice of risk management scenarios considering all components of management decision making and the main managerial functions. Practical implications The range of tools proposed can be used as a simple measure of the bank's reputational risk level, as a component of risk management model, and as a strategic risk management tool. Implementing this approach at the level of the central bank will facilitate realization of its strategic program for restoring confidence in the banking system as a whole. Originality/value A relatively greater objectivity in the reputational risk assessment of the bank was achieved, with each stage formalized and confirmed by an economic and mathematical basis, risk parameters quantified. The authors have developed a comprehensive approach to the reputational risk level assessment (the integral assessment included) as well as the management algorithm.

Reputational risks in banks: A review of research themes, frameworks, methods, and future research directions

“…theory (Barakat et al, 2018), pattern recognition theory (Trostianska & Semencha, 2020), theory of blame avoidance (Hungin & James, 2019), unified theory of reaction in assets market (Sturm, 2013), and the theory of reputational alignment (Carter & Power, 2012). Only one study combines two theories to explore reputational risks of banks.…”

Reputational Risks in Banks: A Review of Research Themes, Frameworks, Methods, and Future Research Directions

“…Strategy related mitigation factors include, identification of broader stakeholder expectations, aligning strategy with higher order goals (Tăchiciu et al, 2020), inter alia. Management related mitigation factors involves the preparation of an organization to deliver on its commitments (Miklaszewska et al, 2020), increasing reputational risks awareness (Trostianska & Semencha, 2019), inter alia. Governance related mitigation factors include transparency in environmental, social and governance policies, effective reputational monitoring system, adoption and implementation of the Equator principles (Mason & Ying, 2020;Banhalmi-Zakar, 2016;Eisenbach et al, 2014), standard model for the reporting of non-financial results, remuneration policies, and self-regulation (Saleuddin, 2014), inter alia.…”

“…Four main indicator-based measures of reputational risks are identified in the literature. These are stakeholder reputation score (Miklaszewska et al, 2020), reputational index point (Zaby & Pohl, 2019), portfolio perspective model (Eckert & Gatzert, 2019) and cognitive mapping model (Trostianska & Semencha, 2019).…”

“…In the banking sector, reputational risk management (RRM) frameworks are still underdeveloped (Fiordelisi, Soana, & Schwizer, 2013;Zaby & Pohl, 2019) and reactive, suggesting that RRM of banks are developed mainly in the context of minimizing losses after a scandal, rather than as a strategic, long term goal (Eccles, Newquist, & Schatz, 2007;Trostianska & Semencha, 2019).This may be attributable to a delay in understanding the significance of reputational risks (Tăchiciu et al, 2020;Zaby & Pohl, 2019) and difficulty with its measurement (Gatzert, Schmit, & Kolb, 2016;Miklaszewska et al, 2020); although, awareness of reputational risks has increased considerably, relative to other risks (Heidinger & Gatzert, 2018).Over the last decade, numerous studies have discussed various themes in RRM, including sources/determinants of reputational risks (Fiordelisi et al, 2013;Zaby & Pohl, 2019), measurement (Miklaszewska et al, 2020;Trostianska & Semencha, 2019), implications (Fiordelisi, Soana, & Schwizer, 2014;Gillet, Hübner, & Plunus, 2010), and mitigation measures (Zaby & Pohl, 2019) What are the methodologies adopted by previous studies on reputational risks in banks? (5) What are the most frequently identified research frameworks adopted in the study of reputational risks in banks?…”

Fuzzy cognitive maps in systems risk analysis: a comprehensive review

“…They integrated the FCM, agent-based model, Bayesian inference mapping, and cost-loss model. Trostianska and Semencha [151] employed FCM to diagnosing the reputational bank risks and used impulse modeling and scenario analysis to determine better risk management strategies. Khodadadi et al [72] conducted a study using FCM with the aim of diagnosing the strike.…”