Operational risk is one of the influential risks identified by banking practitioners, and as the international banking supervisor, the Basel Committee on Banking Supervision has paid special attention to it. There are various methods for measuring and managing this type of banking risk (eg, the advanced measurement approach). Using the radial basis function approach, we compute the probability of bank survival using a partial Volterra integrodifferential equation. Given the importance of operational risk in financial institutions, especially banks, we examine a mathematical model of this risk and solve it using numerical methods. Also, by considering the impact of the bank’s probability of survival on the amount of risk reserves, we investigate the effect of fluctuation in risk reserves on the probability of survival of an organization. To complete the investigation, we calculated the amount of risk storage required to achieve the desired probability of survival.

In financial institutions, operational risk is viewed as a serious risk as it can have a considerable effect on profitability. The primary objective of this research is to discover methods of quantifying operational risk and regulatory capital in financial institutions, as well as any interrelationships between them. The research is based on a sample of public and private sector banks. The study demonstrates the capability of certain public sector banks to bear operational risk on a particular level of regulatory capital. The ability of a bank to be successful under unfavorable conditions is related to its operational risk, regulatory capital and management processes.

Internal audit activities have been expanded worldwide in order to protect local government organizations and, in particular, municipalities. Although several previous studies have examined internal auditing in local government organizations worldwide and provided further development in this area, none of them have developed a methodology for identifying and evaluating the possible risks when preparing an audit plan in municipalities in relation to Greece and elsewhere. This paper aims to address this gap and to ensure the adequate identification, assessment and linkage of the risks that characterize the municipality within the audit universe, leading to the corresponding preparation of an audit plan based on audit priorities. Further, the proposed methodology follows the International Professional Practices Framework and the International Standards for the Professional Practice of Internal Auditing.

Cyber risk is undeniably one of the most critical emerging risks to the financial industry. However, even though cyber risk is recognized as a significant threat to financial institutions and, more generally, to financial stability, the lack of proper data on cyber risk losses impedes efforts to effectively measure and manage this risk. This paper aims to address this gap by providing a cyber risk definition and classification scheme for risk management purposes, to be used as a data collection template for financial institutions. As such, the proposed scheme would ensure that the adopting institutions utilize common language and would allow consistent data collection and sharing.We provide a deeper dive into the reasoning behind the variables we propose to collect and demonstrate how some of the existing cyber security events map into our proposed scheme.

Some value-at-risk (VaR) calculations yield extremely large results, which are often rejected on the grounds that they are inconsistent with the operational loss profile of the organization concerned. Therefore, an informal limit has effectively been placed on VaR. Hitherto, the concept of a "maximum" VaR has rarely been considered. In this paper, we propose an objective and simple process to determine whether or not a calculated VaR is "too large", and thereby give a precise definition of "too large" in this context. A simple decision process, using a constant multiplier of the annualized sum of losses, is proposed to reject distributions that produce extremely high VaR values. This decision process works in conjunction with a bootstrap to also reject distributions that produce very low VaR values. Together, they determine whether or not a calculated VaR value is "credible". A practical guide to using the combined procedures is given, along with a discussion of potential problems and viable solutions to those problems.

Financial institutions manage operational risk (OpRisk) by carrying out activities required by regulation, such as collecting loss data, calculating capital requirements, and reporting. For this purpose, for each OpRisk event, loss amounts, dates, organizational units involved, event types, and descriptions are recorded in the OpRisk databases. In recent years, operational risk functions have been required to go beyond their regulatory tasks to proactively manage operational risk, preventing or mitigating its impact. As OpRisk databases also contain event descriptions, an area of opportunity is to extract information from such texts. The present work introduces for the first time a structured workflow for the application of text analysis techniques (one of the main Natural Language Processing tasks) to the OpRisk event descriptions to identify managerial clusters (more granular than regulatory categories) representing the root-causes of the underlying risks. We have complemented and enriched the established framework of statistical methods based on quantitative data. Specifically, after delicate tasks like data cleaning, text vectorization, and semantic adjustment, we have applied methods of dimensionality reduction and several clustering models with algorithms to compare their performances and weaknesses. Our results improve retrospective knowledge of loss events and enable to mitigate future risks.

Financial technology (fintech) has driven a profound transformation in the finance industry, and the application of various new technologies in the financial sector has brought about changes in the operating environment faced by commercial banks. To assess the impact of fintech on commercial banks, we selected Chinese commercial banks as sample data, calculated the fintech application index at the individual bank level using text mining methods and principal component analysis, and conducted empirical tests using fixed-effect panel regression models. Our results indicate that the application of fintech can effectively increase a bank’s revenue. In terms of risk, using binned groups analysis we found that the development of fintech has a nonlinear, roughly L-shaped relationship with the risk of commercial banks. Further, we also considered the characteristics of the Chinese banking industry for further analysis of the impact of bank-type heterogeneity and to control for sample differences. In addition, we enhanced the robustness of the empirical results using substitution variables, instrumental variable methods and propensity score matching. In conclusion, starting from a new perspective on fintech, using the Chinese banking industry as an example, we offer suggestions for developing the fintech capabilities of banks in developing countries.

Operational risks remain one of the greatest threats to businesses’ profit-making. Numerous studies have been conducted on operational risks and relevant themes to discover their causes, which are of great concern to organizations as they may help predict expected costs and minimize irregularities in the financial system. This bibliometric study assesses the research in this area in order to raise awareness of the hazards and losses incurred by organizations. Despite significant progress, there are still research gaps that need to be filled. The findings from this study help to explore both the gaps and new avenues in the research in order to minimize losses for financial intermediaries. This study quantitatively assesses the quality of research on operational risk using several metrics: citations, networks, coauthorship and region-based publications. The Scopus database was used to analyze the scientific productivity on operational risk and was selected based on the accuracy of the research data it holds. The findings of this paper reveal that the study of operational risk has gained in popularity over the past few years, with researchers hoping to identify the potential benefits of its minimization.

Learning from the past can be invaluable in enhancing risk resilience and developing prevention strategies. One common approach to investigating operational risks is analyzing safety records, which contain various incident data. However, traditional operational risk analysis methods have several limitations. The first significant drawback is that safety records are often documented as unstructured or semistructured data, and the database can be enormous, making it challenging to extract risk information efficiently. Further, the traditional risk assessment method per the ISO 31000 standard is qualitative and subjective, which can lead to inconsistent and inaccurate risk computation, especially when dealing with hazards that have multidimensional consequences. To address these issues, a new method, called the risk-based knowledge graph (RKG), is developed in this paper. The RKG method integrates text mining and analytic hierarchy process (AHP) risk assessment with knowledge graphs for operational risk analysis. This approach provides a systematic method for industrial practitioners to examine operational risk by using AHP risk assessment and graphical semantic networks to illustrate cause-and-effect relationships between risk entities. The use of text mining improves the efficiency of risk information extraction, while the use of AHP risk assessment enhances the consistency and accuracy of risk computation. To evaluate the accuracy and efficacy of RKG, a case study of a computer numerical control manufacturer is conducted. Overall, the RKG method shows promise in addressing the limitations of traditional operational risk analysis methods. It provides a more efficient and accurate way to extract and analyze risk information, making it easier for industrial practitioners to evaluate and manage risks associated with complex hazards.

We review prior empirical studies that investigate the effect of audit committee characteristics on audit report lag and provide insights about this corpus of scholarly literature. We also determine whether these audit committees’ compositional features are associated with audit report lag in the context of an emerging market (Greece). Using a unique set of data hand-collected from the annual reports for 130 firms listed on the Athens Stock Exchange, a multiple regression analysis is conducted to identify and explain this association. This analysis identifies the four audit committee characteristics most frequently mentioned in the literature. By empirically examining these, we demonstrate that audit committee diligence is associated with a shorter audit report lag. The results generally satisfy a number of alternative benchmark tests that control for different types of proxies and transformations of audit report lag. Our results could assist practitioners and/or policy makers in perceiving the efficacy of audit committee diligence as a means to improve the timeliness of financial reporting. This is, to the best of the authors’ knowledge, the first study that provides a synopsis of the prior literature and examines this relationship in the Greek context.