We provide robust evidence that the strategic adoption of artificial intelligence (AI) yields a net reduction in corporate litigation risk. Using firm-level data from Chinese A-share listed firms from 2008 to 2023, we show that AI significantly reduces corporate litigation risk and that firms with greater AI adoption experience fewer lawsuits, face reduced monetary damages and are less likely to be sued. The findings remain consistent across various robustness tests and endogeneity treatments. We also demonstrate that AI operates through two complementary channels: an organization-improving effect, which enhances innovation proactivity, knowledge diversity and information transparency; and a cost-cutting effect, which reduces operational, management and financial constraints. In addition, we indicate that the risk-mitigating effects of AI are heterogeneous in terms of firm characteristics and life cycle, and we find that AI adoption also corresponds to a lower probability of corporate default and increased managerial agility. The findings suggest that AI has benefits in mitigating litigation risk, and that it is not a replacement for human oversight but a powerful tool that empowers and enhances corporate stability and governance. The implications for various stakeholders are also highlighted.
This paper explores the role of artificial intelligence (AI) in the adoption of password-less authentication in an Indian context. It focuses on how AI manages the balance between strong security and transparency. The study adopts a quantitative research design and uses primary data collected from 438 Generation Y and Generation Z respondents from the National Capital Region, India, through a selfadministered questionnaire. Modeling with the partial least square structural equation modeling (PLS-SEM) algorithm reveals that AI has a significant impact on the acceptability of password-less authentication to Gen Y and Z in an Indian context. These generations are tech savvy and use multiple digital services that can benefit from authentication controls, so they are willing to accept AI-based password-less authentication. This study provides actionable insights for policy makers, information technology developers and digital service providers in providing a secure, transparent and AI-driven password-less authentication mechanism in India. The paper investigates the intersection of AI, security and transparency that is important when it comes to authentication systems. It highlights how crucial it is to consider their social and technical aspects, particularly in emerging markets such as India. The paper may be used as a thoughtful guide to responsibly rolling out AI in identity verification.
This descriptive case study analyzes more than 5000 operational risk incidents from a major New Zealand bank to document risk patterns within a concentrated, dual-regulated banking environment. Using incident-level data from 2007 to 2023, the analysis reveals that human factors (such as training deficiencies and procedural lapses) accounted for more than half of all recorded incidents, challenging prevailing assumptions that technology failures dominate in digitally transforming banks. Regression analysis shows that, while human errors occur frequently, they are associated with lower-severity outcomes. Process-related risks exhibit significant associations with customer, financial and regulatory impacts, whereas system failures (though less frequent) are uniquely linked to reputational damage in baseline models. This association becomes statistically insignificant when macroeconomic factors are controlled for, highlighting the contextual nature of operational risk dynamics. Incident patterns evolved alongside key regulatory reforms, including New Zealand's Financial Markets Conduct Act 2013 and Financial Markets (Conduct of Institutions) Amendment Act 2022, though these temporal correlations do not imply causation. Approximately 80% of incidents originated from front-office functions, particularly within practices relating to clients, products and business, underscoring concentration in customer-facing processes. A forensic review of around 400 material incidents identifies six operational risk concentration areas: documentation verification, customer engagement, compliance processes, fraud prevention, payment processing and data management. These areas represent priorities for future risk mitigation but are beyond the study's evaluative scope. As one of the first analyses to use incident-level operational risk data from a live banking environment, this research offers rare empirical evidence in a data-scarce domain and establishes a replicable approach for confidential case studies in concentrated banking markets.
Gaps in the data available for assessing cyber risk have limited the development of metrics that would help the public and private sectors prevent and recover from cyber attacks and reduce systemic risk. Cyber incident disclosure rules, introduced to close the data gaps, help but fall short in supporting the effective management of cyber risk. This paper examines the current and proposed reporting requirements, especially in the financial sector, where they are the most prevalent. It describes the data gaps that remain and discusses how moving toward a better and harmonized cyber incident data collection rule could improve cybersecurity, reduce the risk of catastrophic cyber incidents and reduce the regulatory burden on companies that currently must report cyber data to multiple agencies.
Against the dual backdrop of digital economy development and financial risk prevention and control, this paper investigates the impact of banks' digital transformation on operational risk using panel data for Chinese listed commercial banks from 2013 to 2021. It shows that bank digital transformation and its subareas, except for management digitalization, significantly lower operational risk. This effect is stronger for regional banks and those facing less capital regulation pressure. Further, the operational risk reduction from banks' digital transformation is mainly driven by traditional and financial businesses rather than nontraditional ones. More importantly, digitalization-induced bank operational risk mitigation enhances the market value. These findings will provide practitioners and regulators with new insights into the role of banks' digital transformation in operational risk governance and offer useful references for other countries to decrease bank risk losses and alleviate capital pressure by digital means.
This paper explores the impact of national geopolitical risk perception on corporate innovation behavior and its underlying mechanisms. We construct a national-level geopolitical risk perception index based on Chinese online newspaper data and use a sample of Chinese A-share listed companies from 2000 to 2022 to conduct an empirical study from the perspectives of innovation quantity, efficiency and quality. Our study finds that geopolitical risk perception significantly promotes the improvement of innovation quantity and efficiency but leads to a decline in innovation quality. Mechanism analysis indicates that geopolitical risk perception affects corporate innovation by intensifying market competition and reducing corporate profitability. Heterogeneity analysis reveals that firms with different levels of marketization, more political connections and different industry types exhibit significant differences in their innovation responses to geopolitical risk. This study provides a new perspective for understanding the relationship between geopolitical risk and corporate innovation and also offers a basis for relevant policy formulation.
This study investigates the perceptions and understandings of various demographic groups about risk management and its implementation in the South African banking industry. As no reliable secondary data was available, research was carried out using a survey questionnaire. The survey's participants were employees at the top five commercial banks in South Africa. The Student t-test, analysis of variance and factor analysis are the inferential statistical techniques used to explain the results of the survey. The majority of the survey respondents thought risk management was crucial and knew the fundamentals of its structure. Moreover, the survey revealed that demographic factors such as age, sex, ethnicity and length of service influence whether participants perceive risk differently. This indicates that in the context of South African banking, demographics are crucial to understanding and applying risk management as a whole. This study is groundbreaking because it clarifies the important role of demographics in influencing bank employees' perceptions of risk. Ultimately, it may be deduced that a bank's total operational risk management improves when its staff members understand the risk management procedure.
Current stress testing frameworks for operational risk often lack clarity with respect to measurement standards and the balance to be struck between systemic (macroeconomic) and idiosyncratic risk factors in the test design and evaluation. This paper surveys different approaches that have applied in different jurisdictions, and it provides a conceptual framework for designing operational risk stress tests to a desired likelihood standard that can achieve a full and balanced coverage of a bank's material operational risks.
Leveraging China's anti-corruption campaign as a quasi-natural experiment, this paper uses a panel data set comprising all Chinese listed firms over the period from 2008 to 2018. We employ dynamic difference-in-differences estimation to investigate the causal effect of the Chinese government's anti-corruption campaign on firm operational efficiency. Our empirical evidence supports the notion that the anti-corruption campaign can enhance firm operational efficiency significantly (by approximately 7%). Drawing upon the theoretical framework of the attention-based view, we further examine the moderating effects of political connections, regional market development and corporate social responsibility performance. Our empirical results align with the attention-based view and indicate that these moderating factors collectively undermine firms' operational efficiency by diverting management attention. Our study contributes to the literature by investigating the impact of an improved institutional environment on firm operational performance and by expanding the scope of the attention-based view. In addition, we provide essential insights for policy makers, emphasizing the importance of comprehensive anti-corruption measures, and we offer decision makers valuable insights into prioritizing operational activities.
The Basel III accord recommends diversification of the banking industry to better achieve financial stability. However, diversification creates bank complexity, which increases bank risk. This research examines the effect of bank complexity on bank risk within Asian banking systems, specifically those of China, Malaysia, Pakistan and Qatar. The study finds that the impact of bank complexity varies across countries and risk measurements. For instance, organizational complexity affects bank risk in China, Malaysia and Pakistan but not in Qatar. Meanwhile, business complexity reduces the risk of financial distress in Qatar and idiosyncratic risk in Malaysia. Geographical complexity increases financial risk in China but not in Malaysia and Qatar, while it increases market risk in Pakistan. The findings contribute to the literature by suggesting that bank complexity is not always beneficial or disadvantageous for banks in a risk context, and they cause us to reconsider some aspects of diversification studies. Moreover, the study provides policy implications, emphasizing the importance of regulatory oversight in managing bank complexity and mitigating regulatory arbitrage.
Value-at-risk (VaR) and tail value-at-risk (TVaR) have been used extensively in the financial sector to estimate the worst possible losses for a given portfolio. However, not much has been done to apply these concepts in insurance. It is particularly useful to know, on average, the largest possible claim an insurance company can pay in order to readjust its annual premium rate for compensating possible losses. To this end, this study estimates the VaR and TVaR of comprehensive motor insurance losses (claims) paid by an insurance company in Ghana. In order to identify which continuous distribution function best fits our data, we fit our data to a number of different continuous distributions and then test their goodness-of-fit using the Kolmogorov-Smirnov test. The lognormal distribution is the best fit to our data. VaR and TVaR are then estimated using the lognormal distribution function. Analysis of variance is used to check if there are statistically significant differences between the estimates obtained from both risk measures. Given the vast difference in the estimates provided by both risk measures, it is essential for actuaries to critically assess the type of risk measure used when advocating for reinsurance.
With the continuous development of the metaverse and the deepening of people's understanding of it, its internal activities and interactions have become increasingly complex, leading to discussions on how to effectively regulate various behaviors in the metaverse. This paper focuses on the metaverse's regulatory model, which aims to ensure that activities in the metaverse are carried out within a framework of legality, morality and security. We first define the regulation of digital asset transactions and explore the role of financial regulatory agencies. Next, we delve into the censorship and filtering of content in the metaverse, especially the question of how to handle illegal content and address copyright issues. We also elaborate on the importance of identity authentication, while emphasizing the necessity of protecting user privacy. We then discuss virtual land and real estate development, analyzing the definition and attributes of virtual land and how to plan its development and utilization. In addition, we also explore the characteristics of digital currency transactions and propose strategies and rules for preventing money laundering. Finally, we analyze the possible legal responsibilities and dispute resolution mechanisms that may arise in the metaverse, and we predict the future of regulatory models and the overall challenges they will face. This study provides readers with comprehensive insight into the regulation of the metaverse, aiming to provide useful guidance for future research and practice.
This study investigates the influence of enterprise risk management (ERM) through bibliometric analysis and a systematic review using the theory-context-method (TCM) framework. Analyzing 135 Scopus-indexed documents including 27 high-quality journal articles, the research highlights global trends, thematic clusters and emerging topics in ERM. Performance analysis reveals a sustained interest in ERM, with significant geographic and institutional disparities. Six thematic clusters reveal critical research areas, while emerging topics include ERM's integration with digital transformation and sector-specific applications. The TCM analysis highlights the dominance of ERM theory and quantitative regression methods, particularly in the United States. The systematic review confirms that ERM generally enhances firm performance, particularly in mature stages of implementation, though mixed results in some contexts highlight the need for further exploration of mediating factors. By integrating bibliometric and systematic review methodologies, this study provides a comprehensive overview of ERM research, offering valuable insights to guide future theoretical, methodological and practical advances.
Operational risk capital regulation is moving from internal to standardized models-from the advanced measurement approach to the standardized measurement approach. These proposed changes will lead to more of the same: politically connected banks will further decrease their equity financing, making bank failures and taxpayer-backed bailouts more likely. Yet the goal of operational risk modeling must be to create useful models that capture relevant aspects of reality and improve decision-making. Therefore, operational risk modeling should assimilate seven fundamental properties into an operational risk managerial framework: well-aligned incentives; awareness of of data inaccuracies; explanation of empirical realities; acknowledgement of unknown unknowns; communication of uncertainty; creation of feedback loops; and recognition of dynamics.
Human-free (robo) bank branches operating autonomously with software and embodied robots are now a reality, posing significant operational risks. Robots utilizing diverse underlying technologies exhibit varying risk profiles based on their intelligence and level of autonomy. Risks stemming from robot-related incidents should be integrated into the Basel Committee on Banking Supervision's existing framework. Therefore, understanding the definition of "robot" in the banking sector is crucial for effective operational risk management. This study fills a gap in the literature by discussing the robot-labeling phenomenon (ie, indiscriminate use of the term "robot" to mean both a physical and digital form) and highlighting its widespread misuse in the literature and in banking practice. Analysis of the Hungarian Operational Risk (HunOR) database reveals that the current operations do not record robot autonomy or human oversight, and human-centered risk categorization predominates in risk event categorization, which lacks specific categories for autonomous artificial intelligence or robots. This paper advocates for a shift in the operational risk management mindset to address synthetic era challenges through enhanced risk profiling, human-robot task trade-off catalogs, incident catalogs, operational risk database modernization, training and the establishment of cross-functional robotics forums.
