Risk Management Research Research Articles

Abstract 4137820: Extreme heat and cardiovascular disease in a changing climate: An urgent need for risk management and health research

Background: Extreme heat can trigger or exacerbate cardiovascular disease (CVD) through ischemia or thrombosis, and its deleterious impacts are inclined to worsen as increased heat exposure and heightened vulnerability from climate change interacted with natural and social environment. Research Questions: Previous studies have primarily focused on quantifying the increased risk of CVD due to heat exposure, while lacking a comprehensive understanding of the association, underlying mechanisms, and vulnerability in CVD patients. Consequently, there is a scarcity of clinical and public health interventions aimed at preventing the convergence of extreme heat exposure and the risk of CVD within the context of climate change. Methods: We conducted a systematic search in PubMed, Embase, and Web of Science from inception to December, 2023, and we retrieved articles which assessed the effects of extreme heat or climate change on CVD. Results: In this review, we outline the existing evidence on the epidemiology and pathophysiological mechanisms that elucidate how extreme heat affects the risk of CVD. Extreme heat is associated with increased risks for CVD morbidity and mortality, where older persons, low socioeconomic status, pre-existing medical conditions, and unfavorable living environments confer greater susceptibility to heat. The adverse effects of extreme heat on cardiovascular system involved a wide range of complex mechanisms between different organs and regulatory pathways (Figure 1). Additionally, we identify the factors that contribute to the vulnerability of CVD patients to heat exposure. The discrepancy in heat-related risk can be attributed to individual susceptibility, sociocultural factors, population temperature adaptations, and environmental factors. Future climate change is projected to exacerbate the burden of extreme heat, particularly due to the interaction of air pollution, population aging, and urbanization (Figure 2). Conclusions: Addressing extreme heat should be within the framework of the prevention of CVD. Health professionals and scientists are responsible for mitigating the cardiovascular effects mediated by extreme heat, with an emphasis on engaging in risk management tactics and health research to implement preventive strategies. As CVD is a climate sensitive disease, evidence-based interventions during heat extremes are urgently needed to cope with the cardiovascular risks associated with the inevitable trajectory of climate change.

A systematic literature review of risk management research on hydrometeorological hazards-related Natech events

Natech events refer to technological accidents triggered by natural hazards, potentially leading to fires, explosions, and hazardous material (hazmat) releases. Notably, hydrometeorological hazards, exemplified by hurricanes, have led to an escalating frequency of Natech events over recent decades, resulting in significant socio-economic impacts and substantial financial losses. Consequently, Natech has evolved into a critical domain within risk management, garnering heightened scholarly and professional attention. Urgent application of effective strategies for managing and mitigating risks associated with hydrometeorological-related Natech events is crucial, particularly in the context of increasing climate change threats. Thus, A comprehensive review of risk management developments for hydrometeorological Natech events is essential to identify the research gaps and summarize the future direction. Aimed at achieving that purpose, this study was conducted to systematically review literature pertaining to hydrometeorological Natech events, conducting a comprehensive analysis to elucidate research trends, identify current knowledge gaps, and propose future research directions. The findings of this study would support risk managers and multi-stakeholders in developing effective Natech risk reduction strategies and climate change adaptation policies. Meanwhile, this study is expected to promote future research in risk management, causal mechanisms, and preparedness strategies for hydrometeorological Natech events.

Mediation Role of Strategic Direction between Knowledge Risk Management and Organization's Effectiveness Analytical research in the Iraqi government banking sector

تواجه المنظمات في الوقت الراهن، سواء كانت منظمات عامة أو خاصة، إنتاجية أو خدمة (مثل المصارف) تحديات كبيرة نتيجة للتغيرات السريعة والمستمرة، وأمام هذه التحديات تصبح عمليات وأدوات الإدارة التقليدية غير قادرة على جعل المنظمة قادرة على العمل، مما ينبغي على هذه المنظمات اتباع الأساليب والممارسات الإدارية، بما في ذلك إدارة المخاطر المعرفة المقترحة والتوجه الاستراتيجي بحيث يمكن رفع إجراءاتها على مستوى التطورات في الواقع، بمعنى أن هذه المنظمات وفي ضوء من التطورات البيئية لتوصيف توجهها الاستراتيجي كنهج فكري يتميز بالحداثة والريادة، ومن خلال عملياتها ووسائلها، فإنها قادرة على رفع مستوى أداء المنظمة وتعزيز مكانتها في قطاعها الاقتصادي. ولتوضيح العلاقات والروابط المتوقعة بين متغيرات البحث تم صياغة عدد من الأسئلة تعكس هذه الروابط وفي مقدمتها، ما هو واقع التوجه الاستراتيجي في بيئته المفاهيمية وصلاته الفكرية بإدارة مخاطر المعرفة وفاعلية المنظمة. ولتحقيق ذلك جرى وضع مجموعة من الأهداف ذات الصلة في جوهرها للكشف عن الدور بين إدارة مخاطر المعرفة والتوجه الاستراتيجي وتأثيرها على فاعلية المنظمة من خلال تحديد طبيعة هذا الدور كهدف رئيسي. وكانت أهم الاستنتاجات التي توصل إليها البحث هو السعي إدارة المصارف المدروسة نحو صياغة رسالتها مكتوبة بشكل واضح وحرصها على توافق تلك الرسالة مع الأنشطة التي تمارس في يومياً على المستوى التشغيلي وافاق المستقبل المتجددة على المستوى الاستراتيجي.

Enhancing transparency and fairness in automated credit decisions: an explainable novel hybrid machine learning approach

This paper uses a generalised stacking method to introduce a novel hybrid model that combines a one-dimensional convolutional neural network 1DCNN with extreme gradient boosting XGBoost. We compared the predictive accuracies of the proposed hybrid architecture with three conventional algorithms-1DCNN, XGBoost and logistic regression (LR) using a dataset of over twenty thousand peer-to-peer (P2P) consumer credit observations. By leveraging the SHAP algorithm, the research provides a detailed analysis of feature importance, contributing to the model’s predictions and offering insights into the overall and individual significance of different features. The findings demonstrate that the hybrid model outperforms the LR, XGBoost and 1DCNN models in terms of classification accuracy. Furthermore, the research addresses concern regarding fairness and bias by showing that removing potentially discriminatory features, such as age and gender, does not significantly impact the hybrid model’s classification capabilities. This suggests that fair and unbiased credit scoring models can achieve high effectiveness levels without compromising accuracy. This paper makes significant contributions to academic research and practical applications in credit risk management by presenting a hybrid model that offers superior classification accuracy and promotes interpretability using the model agnostic SHAP framework.

Managing Induced Seismicity Risks From Enhanced Geothermal Systems: A Good Practice Guideline

AbstractGeothermal energy is a green source of power that could play an important role in climate‐conscious energy portfolios; enhanced geothermal systems (EGS) have the potential to scale up exploitation of thermal resources. During hydraulic fracturing, fluids injected under high‐pressure cause the rock mass to fail, stimulating fractures that improve fluid connectivity. However, this increase of pore fluid pressure can also reactivate pre‐existing fault systems, potentially inducing earthquakes of significant size. Induced earthquakes are a significant concern for EGS operations. In some cases, ground shaking nuisance, building damages, or injuries have spurred the early termination of projects (e.g., Basel, Pohang). On the other hand, EGS operations at Soultz‐sous‐Forêts (France), Helsinki (Finland), Blue Mountain (Nevada, USA), and Utah FORGE (USA) have adequately managed induced earthquake risks. The success of an EGS operation depends on economical reservoir enhancements, while maintaining acceptable seismic risk levels. This requires state‐of‐the‐art seismic risk management. This article reviews domains of seismology, earthquake engineering, risk management, and communication. We then synthesize “good practice” recommendations for evaluating, mitigating, and communicating the risk of induced seismicity. We advocate for a modular approach. Recommendations are provided for key technical aspects including (a) a seismic risk management framework, (b) seismic risk pre‐screening, (c) comprehensive seismic hazard and risk evaluation, (d) traffic light protocol designs, (e) seismic monitoring implementation, and (f) step‐by‐step communication plans. Our recommendations adhere to regulatory best practices, to ensure their general applicability. Our guidelines provide a template for effective earthquake risk management and future research directions.

Dynamics of Corporate Finance Research in Global Scientific Literature

This study explores the dynamics of corporate finance research in global scientific literature from 2000 to 2024 using a bibliometric approach. By analyzing keyword co-occurrence and citation data, the research identifies key themes, emerging trends, and influential contributors in corporate finance. Core topics such as capital structure, cost management, and debt remain central, reflecting traditional corporate finance priorities. However, newer areas, including corporate social responsibility (CSR), corporate cash holdings, and behavioral finance, have gained prominence, signaling a shift towards integrating non-financial factors into corporate strategies. The study also highlights the lasting impact of global financial crises, particularly the 2008 crisis, on shaping research in financial distress, risk management, and institutional roles. These insights provide a comprehensive overview of the evolution of corporate finance research and offer directions for future studies.

Value-at-risk modeling of naira exchange rates

The naira exchange rate has experienced extreme volatility, and the use of derivatives to control interest rates and currency risks has grown quickly. These developments have made risk management research necessary. To this effect, this paper investigated the optimal volatility model that offers the best VaR forecasts and minimizes expected loss in the Naira exchange rate. The data was collected from FX time and consisted of the Euro to Naira exchange and the daily closing rate of the US dollar to Naira exchange rate from 24/10/2016 to 15/06/2018. These data were estimated using different symmetric and asymmetric GARCH models. The finding shows that there is less volatility in the USD to Naira exchange rate compared to the Euro to Naira exchange rate. The study shows that SPLINE- GJR under skewed student t distribution is the optimal model for the Euro to Naira exchange rate while GARCH is the optimal model for the US dollar to Naira exchange rate. This study suggests that model risk assessments be given careful consideration by regulators, banks, and other stakeholders and integrated into the process of operational and regulatory design.

Developing a Conceptual Model for Promoting Risk Management for Public–Private Partnerships Projects

Over the past decade, there has been significant growth in the literature addressing risk issues associated with Public–Private Partnerships (PPPs). However, few studies have attempted to comprehensively map and visualize research in this domain. This study aims to fill this gap through a systematic literature review conducted in four stages, utilizing knowledge mapping analysis. More than 400 peer-reviewed articles published between 1990 and September 2023 were examined, making this study the first to analyze such a substantial sample of papers in this field. The research identifies three distinct phases of growth trends in PPP risk management studies. To minimize arbitrariness and subjectivity in analyzing research focuses, this study employed a scientometric approach using VOSviewer software (version 1.6.19), which provides insights into the current state and trends of research on PPP risk management, including high-frequency keywords, contributions of core authors, and distribution across countries, regions, institutions, and journals. In addition to the scientometric analysis, a qualitative review identified six primary research interests, various research perspectives, and diverse methodologies present within the literature. Based on these findings, this study culminates in the development of a conceptual model for risk management in PPPs, offering a structured framework for future research and practical application. This study contributes to the literature by providing the first large-scale, comprehensive mapping and analysis of PPP risk management research, presenting a novel conceptual model that connects theoretical insights with practical implications, and highlighting key areas for future exploration.

Profiling disaster risk management research: A bibliometric analysis

Background: Disaster risk management is essential to mitigate the adverse impacts of disasters and ensure sustainable development. This study explores the importance of disaster information management and integrating information and communication technologies in crisis management strategies. It emphasizes the role of technologies, such as location-based systems and big data analytics, in reducing disaster losses. Methods: This study uses a qualitative descriptive approach to understand the social and cultural context by analyzing documents from various sources such as papers, regulations, and academic literature. Data collection techniques were library review, 957 relevant documents, and a minimal keyword search on Scopus, resulting in 44 journal articles discussed in the public administration and governance framework. The articles included journals indexed from Google Scholar to WOS and Scopus and nationally accredited journals. Findings: This study underscores the importance of disaster resilience strategies, especially in urban areas vulnerable to earthquakes, volcanic eruptions, and floods due to inadequate urban planning. Furthermore, this study examines international efforts to integrate disaster risk reduction and climate change adaptation into sustainable development frameworks, including UN initiatives. Conclusions: This comprehensive analysis highlights the global relevance of disaster risk management and its implications for policy and community resilience. Novelty/Originality of this study: Research on disaster risk management integrates the latest information and communication technologies, such as location-based systems and big data analytics, into crisis management strategies. This provides a new approach to disaster mitigation.

Strategic flood risk management over time. Applying a temporal lens to see how flood risk management measures are (or are not) implemented

ABSTRACT Strategic flood risk management of river catchments involves significant increases in the complexity both of the contents (e.g. the aims and measures) of a given strategy and also its social, spatial, and temporal scales. Conceptually, flood risk management research to date has underestimated the importance of time and temporality. This paper, which is based on ‘historical Institutionalism,’ introduces a temporal lens to focus on strategic flood risk management; it highlights issues of duration and timing as well as tempo and change in tempo with respect to the implementation of measures to reduce flood risk at catchment level. The application of a temporal lens is illustrated through empirical research into strategic flood risk management for the medium-sized Aist river catchment in Austria. The paper uses a longitudinal qualitative research design to analyze the changes in strategic flood risk management in the catchment. The analysis shows that strategy efforts in reaction to an extreme flood event in the catchment in August 2002 can be differentiated into three phases. Phase 1 is characterized by the design of ambitious catchment-wide management; Phase 2 by struggles to implement the strategy due to institutional conditions and protests by citizens; and Phase 3 by redesign of the initial strategic plan to make it less ambitious and by changes to the actor constellation supporting the plan. The present paper offers a process-oriented institutional explanation for this pattern of phases, and it highlights issues of timing and tempo. It concludes with general suggestions for enhancing the temporal dimension in flood risk management.

The connectivity of internal audit and risk management research

The connectivity of internal audit and risk management research

Risk assessment of land subsidence based on GIS in the Yongqiao area, Suzhou City, China.

This study focuses on the Yongqiao District in Suzhou City, Anhui Province, China, aiming to analyze the current situation of ground settlement and its influencing factors in the area. The selected risk indices include settlement rate, cumulative settlement amount, groundwater level drop funnel, thickness of loose sediment layer, thickness of soft soil layer, and the number of groundwater extraction layers. Additionally, vulnerability indices such as population density, building density, road traffic, and functional zoning are considered. An evaluation index system for assessing land Subsidence risk was established. The risk evaluation of land Subsidence was conducted using the Hierarchical analysis-composite index method and ArcGIS spatial analysis, The evaluation results show that the area of higher risk area is about 2.82 km2, accounting for 0.96% of the total area, mainly distributed in the area of Jiuli village, Sanba Street. The middle risk area is distributed around the higher area, with an area of about 9.18 km2, accounting for 3.13% of the total area. The lower risk areas were distributed in most of the study area, covering an area of 222.24 km2, accounting for 75.82% of the total area. The low risk assessment area is mainly distributed in Bianhe Street and part of Zhuxianzhuang Town, with an area of about 58.88 km2, accounting for 20.09% of the total area. The findings of this study are not only crucial for informing local policies and practices related to land use planning, infrastructure development, and emergency response but also enhance our understanding of the complexities of land Subsidence processes and their interactions with human activities, informing future research and practice in environmental risk assessment and management.

Machine Learning in Credit Risk Forecasting —— A Survey on Credit Risk Exposure

Machine Learning in Credit Risk Forecasting —— A Survey on Credit Risk Exposure

Bibliometric Analysis: Risk Management in Islamic Finance Under The Islamic Jurisprudence Framework

Research Objectives: This study aims to comprehensively analyze the stakeholders who play the most role in risk management research in Islamic finance, analyze the evolution and trends in the topic to provide future research direction.Method: This study uses a bibliometric method in biblioshiny R-Studio. Observation data is taken from the Scopus database in the form of articles and English-language.Results: We found that MK Hassan is the leading author, International Islamic University Malaysia (IIUM) is the leading affiliation, the Journal of Islamic Accounting and Business Research is the leading journal, and Malaysia is the leading country. We highlight the need for further studies to develop risk management models under Islamic principles. In addition, the rapid development of Islamic finance due to the Industrial Revolution 4.0 must be balanced with technology to minimize risks in Islamic finance.Novelty: Not only analyzes the publication quantitatively, this study also reviews the most cited papers. Furthermore, the overall analysis provides insight into the future direction of research.Conclusion: The trend of research on Islamic financial risk is not consistently growing. Besides that, the citation rate also shows a downward trend. This shows a lack of research interest in this topic. Furthermore, Islamic financial risk management is still dominated by the Islamic banking sector and limited to other sectors.

Trends in Corporate Governance Research of Risk Management: A Bibliometric Analysis

Purpose: The purpose of this article is to determine research trends on the relationship between corporate governance and risk management. Company managers are known for corporate governance by collecting various policies to make the company's basic norms more effective. Methods: Rapid economic changes, technological developments, the challenges of climate change, and political uncertainty are all factors that can disrupt company operations. A total of 30 documents from the Scopus database have been analyzed using VOSviewer. Resullt and Conclusion: It can be seen from the research results that the most relevant author is Von Solms R., with the countries that publish the most, namely China and South Africa. Research Implication: Research with relevant keywords in corporate governance and risk management is information technology, information management, industrial management and risk assessment. Publication will be carried out from 2004-2022. Originality/Value: So from the analysis carried out, conclusions were drawn regarding the keywords that are widely used, the most relevant publication years, and the countries that publish the most research.

Participatory Risk Governance for Seoul, South Korea’s Flood Risk Management

Risk governance is a widely used framework in natural and societal risk management research. Risks from natural hazards in urban areas call for the establishment of rigorous and participatory urban risk governance. In this study, we examined participatory risk governance (PRG) of flood risk management (FRM) in Seoul, South Korea. We conducted key informant interviews and implemented a survey with citizens, to explore perceptions of flood risks and risk management, and to examine prospects for improving PRG in Seoul. We found a gap between the perceptions of key informants and citizens. Key informants believed that citizens’ low awareness of flood risks hinders PRG. Yet our research found that citizens’ risk awareness was not low, and risk awareness may not be the main barrier to participation in PRG. Instead, we found that citizens lacked knowledge of FRM actions, and they assigned government bodies a high level of responsibility for FRM, compared to the level of responsibility that citizens assigned to themselves. Moreover, the actors involved in FRM tended not to trust each other, which implies a lack of mutual understanding. To increase the effectiveness of PRG, we suggest a polycentric governance structure anchored by a leading actor group, and active promotion of the participation of actors at multiple levels of governance. Communication between government and citizen participants, designed to foster improved understanding and recognition of one another’s roles and contributions to FRM, will enhance trust and improve the implementation of PRG in Seoul.

Evolution and future directions of banking risk management research: A bibliometric analysis

This bibliometric analysis examines the evolution of academic research on banking risk management over the past four decades. The research maps publication trends, influential works, authorship, geographical distribution, conceptual themes, and future research directions using quantitative analysis of 286 English-language articles from the Scopus database. Since the 1990s, publication output and citations have been on the rise, with peaks in 2012 and 2019, indicating a rise in scholarly interest. The focus of research has expanded beyond credit risk to include operational, liquidity, and other key risks. Governance and culture are also expanding areas of emphasis. Geographic diffusion is revealed by bibliometric mapping, shifting from the early dominance of U.S. and European scholars to the increasing contributions of Asia and other emerging economies. Analysis of frequently occurring keywords illustrates the importance of fundamental risk management concerns. Six conceptual domains are identified by cluster analysis: operational risks, governance, liquidity risks, commercial bank risks, credit risks and performance, and market risk interactions. Based on the bibliometric analysis and research findings, three promising future research directions are proposed: the impact of pandemics and natural disasters on bank risk management; emerging threats such as cybercrime and climate change; and the impact of risk culture and governance on outcomes. AcknowledgmentIt is crucial to recognize everyone who contributed to the success of this study, notably the Ho Chi Minh University of Banking, Vietnam.

Can ChatGPT exceed humans in construction project risk management?

PurposeThe objective of this research is to investigate the capabilities of the ChatGPT GPT-4 model, a form of artificial intelligence (AI), in comparison to human experts in the context of construction project risk management.Design/methodology/approachEmploying a mixed-methods approach, the study draws a qualitative and quantitative comparison between 16 human risk management experts from Finnish construction companies and the ChatGPT AI model utilizing anonymous peer reviews. It focuses primarily on the areas of risk identification, analysis, and control.FindingsChatGPT has demonstrated a superior ability to generate comprehensive risk management plans, with its quantitative scores significantly surpassing the human average. Nonetheless, the AI model's strategies are found to lack practicality and specificity, areas where human expertise excels.Originality/valueThis study marks a significant advancement in construction project risk management research by conducting a pioneering blind-review study that assesses the capabilities of the advanced AI model, GPT-4, against those of human experts. Emphasizing the evolution from earlier GPT models, this research not only underscores the innovative application of ChatGPT-4 but also the critical role of anonymized peer evaluations in enhancing the objectivity of findings. It illuminates the synergistic potential of AI and human expertise, advocating for a collaborative model where AI serves as an augmentative tool, thereby optimizing human performance in identifying and managing risks.

Data-augmented landslide displacement prediction using generative adversarial network

Landslides are destructive natural disasters that cause catastrophic damage and loss of life worldwide. Accurately predicting landslide displacement enables effective early warning and risk management. However, the limited availability of on-site measurement data has been a substantial obstacle in developing data-driven models, such as state-of-the-art machine learning (ML) models. To address these challenges, this study proposes a data augmentation framework that uses generative adversarial networks (GANs), a recent advance in generative artificial intelligence (AI), to improve the accuracy of landslide displacement prediction. The framework provides effective data augmentation to enhance limited datasets. A recurrent GAN model, RGAN-LS, is proposed, specifically designed to generate realistic synthetic multivariate time series that mimics the characteristics of real landslide on-site measurement data. A customized moment-matching loss is incorporated in addition to the adversarial loss in GAN during the training of RGAN-LS to capture the temporal dynamics and correlations in real time series data. Then, the synthetic data generated by RGAN-LS is used to enhance the training of long short-term memory (LSTM) networks and particle swarm optimization-support vector machine (PSO-SVM) models for landslide displacement prediction tasks. Results on two landslides in the Three Gorges Reservoir (TGR) region show a significant improvement in LSTM model prediction performance when trained on augmented data. For instance, in the case of the Baishuihe landslide, the average root mean square error (RMSE) increases by 16.11%, and the mean absolute error (MAE) by 17.59%. More importantly, the model's responsiveness during mutational stages is enhanced for early warning purposes. However, the results have shown that the static PSO-SVM model only sees marginal gains compared to recurrent models such as LSTM. Further analysis indicates that an optimal synthetic-to-real data ratio (50% on the illustration cases) maximizes the improvements. This also demonstrates the robustness and effectiveness of supplementing training data for dynamic models to obtain better results. By using the powerful generative AI approach, RGAN-LS can generate high-fidelity synthetic landslide data. This is critical for improving the performance of advanced ML models in predicting landslide displacement, particularly when there are limited training data. Additionally, this approach has the potential to expand the use of generative AI in geohazard risk management and other research areas.

Beware of botshit: How to manage the epistemic risks of generative chatbots

Advances in large language model (LLM) technology enable chatbots to generate and analyze content for our work. Generative chatbots do this work by predicting responses rather than knowing the meaning of their responses. In other words, chatbots can produce coherent-sounding but inaccurate or fabricated content, referred to as hallucinations. When humans uncritically use this untruthful content, it becomes what we call botshit. This article focuses on how to use chatbots for content generation work while mitigating the epistemic (i.e., the process of producing knowledge) risks associated with botshit. Drawing on risk management research, we introduce a typology framework that orients how chatbots can be used based on two dimensions: response veracity verifiability and response veracity importance. The framework identifies four modes of chatbot work (authenticated, autonomous, automated, and augmented) with a botshit-related risk (ignorance, miscalibration, routinization, and black boxing). We describe and illustrate each mode and offer advice to help chatbot users guard against the botshit risks that come with each mode.