Cyber risk management in the US banking and insurance industry: A textual and empirical analysis of determinants and value

Abstract

In this paper, we first construct a cyber risk consciousness score using a text mining algorithm, applied to annual reports of large‐ and mid‐cap US banks and insurers from 2011 to 2018. We next categorize the firms' cyber risk management based on keywords to study determinants and value‐relevance. Our results show an increasing cyber risk consciousness, regardless of the industry. In addition, for the entire sample we find that firms belonging to the banking industry, with a higher cyber risk consciousness score and a higher general risk awareness are more likely to implement cyber risk management, which also holds for both industries separately. We find the opposite in the case of profitable firms for the entire sample and the insurer subsample. Finally, we observe a significant positive relationship between cyber risk management and firm value measured by Tobin's Q for the entire sample and the subsamples of banks and insurers.

Building a thematic framework of cybersecurity: a systematic literature review approach

“…, 2022). Clear and well-defined cybersecurity policies help in understanding key indicators of uncertainty in the cybersecurity domain, directly impacting an organization’s level of cybersecurity knowledge (Garcia-Perez et al. , 2022).…”

“…Raising cybersecurity awareness within organizations can increase response efficacy (Wong et al, 2022), promote compliance with information security policies and mitigate potential security threats (Chen et al, 2018;Ahmad et al, 2019;Li et al, 2019;Malaivongs et al, 2022). Stronger awareness of cybersecurity risks and greater consciousness of potential security threats can lead to the implementation of effective cyber risk management strategies (Gatzert and Schubert, 2022), ultimately contributing to improved cybersecurity practices within the organization (B elanger et al, 2022).…”

Insurers' climate change risk management quality and natural disasters

“…Third, we contribute to the corporate risk management literature (see, e.g., Berry‐Stölzle & Xu, 2018; Ellul & Yerramilli, 2013; Froot et al, 1993; Hoyt & Khang, 2000; Hoyt & Liebenberg, 2011; Kamiya et al, 2021; Mayers & Smith, 1990; Smith & Stulz, 1985) by examining factors associated with a higher quality climate change risk management approach. Fourth, we contribute to the literature on insurance company operations (see, e.g., Fritzsch et al, 2021; Gatzert & Heidinger, 2020; Gatzert & Schubert, 2022) by examining insurers' climate change risk management approach in detail. Our results may also be of interest to state regulators who are concerned about insurers' ability to adapt to climate change and continue to provide reliable coverage to consumers.…”

“…Thistlethwaite and Wood (2018) perform a qualitative analysis of climate risk disclosures of 183 insurance companies for the years 2012 and 2015 and document that the majority of insurers do not integrate climate risk into their risk management process. Gatzert and Schubert (2022) categorize US and UK insurance companies into two groups based on an indicator of climate change risk awareness from the Refinitiv Eikon environmental, social and governance (ESG) database. They find that larger European insurers are more likely to exhibit climate risk awareness and that such awareness is positively associated with Tobin's Q.…”

Two Statistical Approaches to Justify the Use of the Logistic Function in Binary Logistic Regression

“…(1) Scoring in the banking sector [6] (2) Risk management in the insurance sector [7] (3) Early prediction of diseases the health sector [8] (4) Hotel booking in the tourism sector [9] (5) Spam detection in the cybersecurity sector [10] (6) Prediction of the price of a property in a given region [11] Te present study provides, through two diferent approaches, a rigorous mathematical answer to the crucial question that torments us, namely where does this logistic function on which most neural network algorithms are based come from? Tese two approaches of logistic function are very interesting.…”