This study investigates the practical implementation and effectiveness of risk management tools and techniques in construction projects, with a particular focus on how organizational culture influences their success. Through qualitative research, including semi-structured interviews with professionals in the construction industry, the study explores the challenges and best practices in risk identification, assessment, and mitigation. Findings reveal that traditional tools such as checklists, risk registers, and brainstorming remain widely used, though their application is often limited by a lack of formal education and training in risk management. While quantitative methods, such as Monte Carlo simulations, are recognized for their predictive capabilities, they are underutilized, particularly for non-financial risks. The study highlights significant gaps between theory and practice, particularly in the integration of advanced data-driven approaches, which could improve the accuracy of risk assessments for high-risk tasks. Furthermore, the research identifies the absence of structured knowledge transfer mechanisms as a barrier to effective risk management. The study concludes that while risk management frameworks are theoretically sound, their practical implementation is often hindered by organizational barriers, including inadequate training, poor communication, and the failure to incorporate emerging technologies. The results suggest that future research should focus on developing standardized, technology-integrated risk management frameworks and fostering a culture of continuous improvement to enhance project success.
This study examines the critical role of logistics performance in building resilience against physical risk, utilizing the FM Global Resilience Index—a composite measure of countries’ relative enterprise resilience to disruptive events—as a key benchmark. By applying a Bayesian Belief Network (BBN) modeling approach, data from 130 countries are analyzed, focusing on key risk factors such as climate risk quality, fire risk quality, and cybersecurity. The novelty of this research lies in its integration of logistics performance as a moderating factor within a probabilistic resilience model, which allows for the identification of how logistics alters the influence of different risk variables on national resilience—an approach not previously undertaken in resilience literature. The findings reveal strong interconnections between logistics performance and resilience, demonstrating that efficient logistics systems play a vital role in mitigating physical risks, particularly in fire safety and cybersecurity. This study provides valuable insights for policymakers and industry leaders to develop resilience strategies that reduce exposure to physical risks and enhance adaptability in an evolving risk landscape.
Rohima W. Ningrum, Wiwit Suryanto, Wahyudi Wahyudi
et al.
Tsunamis pose a significant threat to the Jailolo coastal area in North Maluku, Indonesia, because of its proximity to the Maluku Sea subduction zone, where seismic activity has historically triggered destructive waves. This study aims to map tsunami hazards in the Jailolo coastal area by integrating the Structure-from-Motion (SfM) photogrammetry method with numerical calculations. The SfM photogrammetry method involves using an unmanned aerial vehicle (UAV) to produce digital elevation model (DEM) data in the form of digital terrain model and digital surface model, as well as orthomosaic data. In addition, tsunami wave propagation simulation modelling was carried out using the Cornell Multi-Grid Coupled Tsunami computational program, with input data including Manning’s coefficient data and fault parameters. The aerial photography resulted in DEMs with a vertical accuracy of LE90 of 0.15 metres and an orthomosaic with a horizontal accuracy of CE90 of 0.5 metres. The tsunami simulation revealed tsunami waves reaching 5.8–17.4 metres, with a hazard zone of approximately 119.31 hectares and an inundation distance of about 700 metres from the coast. The affected areas include settlements, agriculture and mangrove forests. In conclusion, the integration of UAV-based SfM photogrammetry and numerical simulations effectively produces high-precision tsunami hazard maps.
Contribution: This study provides a significant contribution to disaster mitigation and evacuation planning by providing an accurate and efficient method for mapping tsunami hazards. The precise data can support decision-making in high-risk coastal areas such as Jailolo.
Abstract One of the most notable developments in the asset management industry in recent decades has been the growth of algorithmic trading. At the same time, significant structural changes in the industry have occurred, with passive investing gaining momentum. The intersection of these two major trends poses special challenges during market downturns, magnifying portfolio losses and leading to significant outflows. Emerging market (EM) investors have seen two major downturn events in the 2020s, namely the COVID-19 pandemic and the Russia-Ukraine conflict, both of which have strongly affected EM portfolios’ risk-return profiles and increased their correlations with their developed market counterparts, eliminating much or all of EMs’ diversification benefits. This has led to major capital outflows from EM countries, further destabilizing these fragile economies. Against this backdrop, we argue that capital need not exit these riskier markets during periods of turmoil and support this by developing a second-generation Automated Adaptive Trading System (AATS) back-tested on a relevant, diversified EM portfolio that tracks the Morgan Stanley Capital International (MSCI) Emerging Markets Index during a volatile period characterized by negative returns, high risk, and a high correlation with global markets for the buy-and-hold EM portfolio. The system incorporates an Autoregressive Moving Average-Generalized AutoRegressive Conditional Heteroskedasticity model that offers an interpretability advantage over machine-learning methods. The main strength of the AATS is its ability to allow the embedded hybrid forecasting model to adapt to the changing environments that characterize EMs. This is done by implementing a recursive window technique and running a user-specified fitness function to dynamically optimize the mean equation parameters throughout the lead time. Back-testing several configurations of the flexible AATS consistently reveals its superiority while assuring the robustness of the results. We conclude that with the right investment tools, EMs continue to offer compelling opportunities that should not be overlooked. The novel AATS proposed in this study is such a tool, providing active EM investors with substantial value-added through its ability to generate abnormal returns, and can help to enhance the resilience of EMs by mitigating the cost of crises for those countries.
Introduction. The task of assessing industrial potential seems to be especially relevant for Russia. The presence of large-scale capacities, resource potential and specialists, combined with poor market knowledge and tougher competition, suggests that the regions of the Russian Federation are poorly using their industrial potential. As a result, industrial systems not only do not control their niche in the market, but their market positions is constantly becoming worse. In such conditions, it becomes necessary to search for and develop a new approach to assessing and managing the industrial potential of the regions of the Russian Federation. Goal. The study aims to develop risk analysis tools for the oil refining industry to assess the industrial potential of this industry. Materials and methods. To solve the tasks, methods of economic and mathematical modeling, system, technical, economic and financial analysis, expert methods, methods of research of operations and decision-making, mathematical statistics, general and special-purpose software MS EXCEL, STATISTICA, SPSS were used. Results and discussion. The use of the proposed methodological approach to risk assessment makes it possible to identify and prevent multiple hazards for enterprises in the industry that can negatively affect the operating activities of the company and create risks of its existence as a whole. The solution to the problem of effective minimization of risks in the oil refining sector should be based on a complete and adequate assessment of events and processes, both in the industry and in the country and in the world, which is the basis for the effective functioning of the enterprise and, as a direct result, of the regions as a whole, since, as historically developed, often these companies are city-forming. Conclusion. The studies conducted show that the issues of effective management of the industrial potential of the regions of the Russian Federation are especially relevant in conditions of lack of necessary resources. This determines the need for a comprehensive study of the industrial potential of the regions, the impact of the external and internal environment on it, and a study on the management of industrial potential.
Kelechukwu Kelvin Ibe, Owen Chiweshe, Mercy Ichiko Ola
et al.
India experiences various natural disasters each year, leaving communities with significant consequences. The eastern coast, particularly Odisha, is the most cyclone-prone region globally. Rural and remote communities in the state are not immune to the effects of these cyclonic events and often suffer more heavily. This study utilized primary data from the participatory rural approach and secondary data from the relevant literature to understand the impact of natural disasters on remote communities. Vulnerability was also assessed via a six-dimensional framework encompassing natural, institutional, physical, economic, social, and technological aspects. Evidence from the rural community study indicates impacts across all dimensions at different levels. These were discussed for targeted risk management and strategic planning to enhance resilience, improve recovery, and minimize impacts.
Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi
et al.
This study investigates and predicts the likelihood of operational risk occurrence in the banking industry using machine learning algorithms. The primary objective is to analyze operational risk data and evaluate the performance of various machine learning models to develop effective tools for enhancing risk management and minimizing financial losses in banks and financial institutions. Operational risk data were collected, pre-processed, and then used for predictions with machine learning models, including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), and k-Nearest Neighbors (KNN). Model performance was assessed using evaluation metrics such as accuracy, precision, recall, F1-score, and the Area Under the Curve (AUC) to determine the most effective model for risk prediction. The findings indicate that the RF and SVM algorithms outperform other models in predicting operational risk across all scenarios. Furthermore, the results demonstrate the strong predictive capability of machine learning algorithms in assessing operational risk, highlighting their potential as valuable decision-making tools for risk management in the banking sector.Keywords: Risk Prediction, Operational Risk, Risk Management, Machine Learning IntroductionOperational risk is defined as the risk arising from external factors or failures in internal controls or information systems, which may lead to both anticipated and unexpected losses (Crouchy et al., 1998). Lopez (2002) characterizes it as any unquantifiable risk that a bank may encounter. According to the Basel II Agreement, operational risk refers to the probability of loss resulting from deficiencies, breakdowns, or inefficiencies in human resources, processes, technologies, infrastructure, or internal and external events (Pena et al., 2018).To estimate the capital required to cover operational risk, the Basel framework introduces three approaches: the Basic Indicator Approach (BIA), the Standardized Approach (SA), and the Advanced Measurement Approach (AMA) (Mora Valencia, 2010; Mora Valencia et al., 2017). The BIA and SA estimate capital requirements based on annual gross income, with the key distinction being that the SA categorizes a bank’s activities into eight business lines. Under the BIA, an alpha coefficient (α) of 15% is applied, whereas in the SA, each business line has a specific beta coefficient (β) ranging between 12% and 18%. The AMA employs both quantitative and qualitative methods for operational risk modeling, leveraging databases to collect statistical data and utilizing the loss distribution approach (LDA) to model frequency and severity distributions. Capital coverage is then determined based on the cumulative distribution of these variables. Since the LDA is data-driven, the Basel framework (BCBS, 2004) emphasizes the necessity of a robust database for collecting operational risk data. Four key databases are required: internal loss event data, external loss event data, scenario-based analysis data, and a database of business environment and internal control factors.Compared to other banking risks, such as credit and market risks, measuring, monitoring, and managing operational risk is considerably more complex. This risk has gained increasing attention in recent years, as large operational losses have led to the liquidation of financial institutions (Abdymomunov et al., 2020; Afonso et al., 2019). Crisanto and Perino (2017) identify cyber threats and cyber fraud as critical factors influencing operational risk capital estimation. These risks have intensified with the growth of electronic banking services and include illegal access, system disruptions, and the misuse or theft of digital assets for financial gain (BCBS, 2016; Drew & Farrell, 2018). To quantify potential losses in electronic banking transactions, Bouveret (2018) proposed a Bayesian Network (BN) model to estimate operational risk capital requirements in financial institutions.Machine learning has emerged as one of the most promising yet challenging approaches in modern finance (Tsai & Wu, 2008). These methods have transformed the financial industry, with deep learning (DL) being extensively studied and applied due to its adaptability and predictive capabilities (Ivanov, 2019). Pena et al. (2021) employed a fuzzy convolutional deep learning model to estimate the maximum operational risk value at a 99.9% confidence level. Similarly, Zhou et al. (2020) utilized semi-supervised machine learning algorithms to classify operational risks based on financial news, analyzing 5,843 documents from financial articles and newspapers in the Asia-Pacific region between February and March 2019. Their model demonstrated the capability to predict various types of risks in the banking industry. In another study, Akbari and Yazdanian (2023) applied machine learning algorithms to determine optimal thresholds for operational loss severity data, classifying the data and estimating the capital required to cover operational risk by integrating severity and frequency distribution functions with Monte Carlo simulation. Method and DataIn this study, operational risk data were collected, pre-processed, and then used for predictions with machine learning models, including RF, DT, SVM, LR, NB, and KNN. The models' performance was assessed using evaluation metrics such as accuracy, precision, recall, F1-score, and AUC to identify the most effective model for predicting the likelihood of risk occurrence. FindingsThe results indicate that the RF and SVM algorithms exhibit strong performance in predicting operational risk across all scenarios. Specifically, the RF algorithm achieved an accuracy of 0.9690, while the SVM algorithm attained an accuracy of 0.9587 in State 1, making them the most effective models in this setting. Both algorithms demonstrated comparable performance across other modes. Conclusion and DiscussionThis study analyzes and predicts operational risk occurrence in the banking industry using machine learning algorithms. The findings indicate that various algorithms, particularly RF and SVM, demonstrate strong predictive performance. These results have the potential to transform operational risk management in banks, leading to significant reductions in associated costs and losses.A key insight from this study is that leveraging large and diverse datasets can substantially enhance prediction accuracy. Machine learning models can process complex datasets, identify hidden patterns, and facilitate early risk detection, enabling banks to implement preventive measures before risks materialize. Moreover, integrating machine learning into risk management enhances decision-making by providing precise, data-driven predictions, allowing for more effective strategies and efficient resource allocation.Future research could incorporate additional data, such as historical records, economic indicators, and internal process information, to further improve prediction accuracy. With advancements in technology, more sophisticated techniques—such as reinforcement learning methods (e.g., DQN, Q-Learning, DDPG, and Meta-Learning)—could enhance the accuracy and efficiency of operational risk prediction models.
The consequences of accidents, fires and explosions in the oil and gas industry in the world have been analyzed in detail. Particular attention is paid to the lack of timely maintenance, inadequate knowledge of employees or their intentional misconduct, in-depth study to prevent such cases and the proper organization of fire protection of production facilities. we introduce early warning systems (EWS) in the context of disaster risk reduction, including the main components of an EWS, the roles of the main actors and the need for robust evaluation. Management of disaster risks requires that the nature and distribution of risk are understood, including the hazards, and the exposure, vulnerability and capacity of communities at risk.
The uncertainty arising from random sampling of non-debris flow samples significantly impacts the accuracy of debris flow susceptibility assessments (DFSA). This study introduces a novel uncertainty elimination method, Kernel Density Estimation (KDE), and compares it with Mean and Maximum Probability Analysis (MPA) methods. Furthermore, we investigate the responses of four commonly used machine learning models to sampling uncertainty, comparing two structurally similar models (Random Forest (RF) and Extremely Randomized Trees (ERT)) with two structurally different models (Support Vector Machine (SVM) and Multilayer Perceptron (MLP)). The results indicate that the application of these uncertainty elimination methods can significantly enhance AUC values and zoning accuracy, with the KDE method outperforming the others. Specifically, the AUC values based on KDE for RF, ERT, SVM, and MLP are 0.995, 0.999, 0.999, and 0.853, respectively. The corresponding zoning accuracy for these models is 1.00, 1.00, 1.00, and 0.78, respectively. The study further reveals that the responses to sampling uncertainty vary by model architecture: RF, ERT, and SVM typically exhibit bimodal normal distributions, while the MLP model shows a unimodal distribution. Additionally, MLP is more sensitive to variations in negative samples, whereas RF and ERT are less affected due to the ensemble structure.
Marwa Hassan, Ali M. El-Rifaie, Mahmoud Beshr
et al.
This study introduces a novel risk measurement and control framework tailored to optimize the stochastic energy trading strategy of a solar storage system at Egypt’s Siwa solar station. By integrating key risk measurements Shortfall Probability (SP), Value at Risk (VaR), and Conditional Value at Risk (CVaR)–into a stochastic optimization model, this framework caters to diverse risk preferences and effectively addresses uncertainties associated with electricity prices and solar power production. Using realistic data, simulation analysis reveals a significant finding: increasing the energy capacity of battery storage significantly enhances the system’s arbitrage capability, leading to a notable profit increase of approximately 20%. Furthermore, the integration of the risk framework demonstrates its effectiveness by revealing significant improvements in key areas, including risk mitigation, system stability, financial performance, decision-making insights, and adherence to international standards. These findings equip decision-makers in the Egyptian energy sector with actionable strategies to optimize their energy trading practices, thereby enhancing profitability and risk management in this dynamic industry.
This article is a synthetic, brief review of the literature, reports and references on the transformation of the automotive industry into zero-emission cars, in particular electric cars. It analyzes the technological and economic aspects of changes in the automotive industry regarding the transformation to zero-emission cars. Despite great de-emission parameters, the production of electric cars does not have a zero carbon footprint. The acquisition of critical elements, their production and the production of other components and materials needed for their construction have an environmental impact. The supply chains of materials for the construction of batteries for electric cars are characterized by significant risks related to, among others, a lack of diversification and limited flexibility. The dominant supplier of rare elements for batteries is China. The article analyzes the impact of prices on the demand for electric cars and compares them to internal combustion cars. Research shows that most electric cars are sold in China, the USA and Europe (about 95% of the supply). The costs of cars are of great importance, which, given the current reduction in the purchasing power of consumers, make the forecasts of the dynamic growth of electromobility very cautious, and even stagnation in the purchase of electric cars is expected in the second half of 2023.
A catastrophic debris flow occurred in the Zengda gully, a branch of the Dajinchuan River in Zengda town, Jinchuan County, Sichuan Province, China. The successful implementation of a real-time evacuation avoided 820 casualties for people living in 200 settlements. This was the largest-scale successful real-time evacuation for a debris flow disaster after the Wenchuan earthquake in China. In order to better reveal the causes of the successful real-time evacuation process of the giant debris flow disaster, the characteristics, formation and movement process of the debris flow were studied using multi-temporal remote sensing images, field investigation, laboratory analysis, and empirical formula calculations. It was found that the successful real-time evacuation was possible because of a well-executed monitoring system, timely release of early warning information, a highly effective operation disaster prevention system, and decisive and advanced avoidance. It also transferred strategies through in-depth analysis of several important stages in the real-time evacuation process. Finally, an exemplary mode of community-based warning is proposed based on the Zengda gully giant debris flow disaster real-time evacuation. Specifically, the mode was led by government, implemented by local residents and proceeded with the guidance by experts in the field. The experience and effective risk avoidance mode presented in this paper can be shared and employed by other countries or regions at serious risk for debris flow disasters.
Andrea Chierici, Andrea Malizia, Daniele Di Giovanni
et al.
Since the Fukushima Daiichi Nuclear Power Plant accident in March 2011, the technology available for unmanned aerial vehicles (UAVs) for radiation monitoring has improved greatly. Remote access to radiation-contaminated areas not only eliminates unnecessary exposure of civilians or military personnel, but also allows workers to explore inaccessible places. Hazardous levels of radioactive contamination can be expected as a result of accidents in the nuclear power industry or as a result of the intentional release of radioactive materials for terrorist purposes (dirty bombs, building contamination, etc.). The possibility to detect, identify, and characterize radiation and nuclear material using mobile and remote sensing platforms is a common requirement in the radiation sensing community. The technology has applications in homeland security and law enforcement, customs and border protection, nuclear power plant safety and security, nuclear waste monitoring, environmental recovery, and the military. In this work, the authors have developed, implemented, and characterized a gamma-ray detection and spectroscopy system capable of operating on a UAV. The system was mainly developed using open-source software and affordable hardware components to reduce development and maintenance costs and provide satisfactory performance as a detection instrument. The designed platform can be used to perform mapping or localization tasks to improve the risk assessment process for first responders during the management of radiological and nuclear incidents. First, the design process of the system is described; the result of the characterization of the platform is then presented together with the use of the prototype installed on a UAV in an exercise simulating a radiological and nuclear contamination scenario.
The authors show that there is a negative relationship between economic policy uncertainty (EPU) and firm overinvestment using Korean data from 2007 to 2016. Since Jensen (1986) shows that a firm's free cash flow is an important factor of overinvestment, the authors examine how free cash flow influences the sensitivity of overinvestment to EPU. The authors find that a high level of free cash flow attenuates the negative effect of EPU on overinvestment. The authors find that there is no significant difference in the effect of EPU on overinvestment between Chaebol (Korean family-run conglomerates) and non-Chaebol firms, which is consistent with the literature that the features of Chaebol are weakening.
The article analyzes the international Supervisory experience of the European Union in terms of monitoring the implementation of short sales. The analysis was carried out in order to determine further key directions of development of the Russian market of margin lending. In addition, the article also considers the current Russian legislation in terms of supervision of unsecured transactions, regulated by the Decree of the Central Bank of the Russian Federation, which entered into force on July 1, 2019. The obtained results of the analysis allowed to make a comparative characteristic of the regulation of short sales in the European and Russian jurisdictions, as well as to make a forecast regarding the hypothetical transformations of the current Russian legislation.The analysis of the European experience in part of control of implementation of the short sales established by SSR to disclosure of information on short positions, to restrictions on short sales, to powers and ESMA obligations, etc., allows to come to conclusion that regulation in the Russian jurisdiction can be expanded not only due to complication of a procedure for granting of a marginal loan by inclusion in a portfolio of the client of difficult nonlinear tools, but also due to establishment of requirements for disclosure of information on the high-concentrated short positions and establishment of thus constantly reconsidered threshold size. In article the assumption that the designated directions of development of regulation of short sales regarding expansion of powers of the regulator also will increase transparency of intermediary activity and the financial market is made, having provided thus reliable protection of interests of clients of financial intermediaries.