Risk Management Model Research Articles

Enabling organizations to strategically manage risks in circular supply chains

AbstractCircular supply chains (CSCs) prioritize resource efficiency by reusing, remanufacturing, and recycling materials, offering environmental benefits and competitive advantages. However, they are facing various risks and organizational challenges that hinder their efficiency. To ensure long‐term sustainability, this study identifies the key risks and introduces a prioritization model for risk management in CSC strategies proposing a framework for the risk evaluation based on the set of criteria and prioritization model using a combination of three multicriteria decision‐making (MCDM) methods. The model applies fuzzy Delphi‐based analytical hierarchy process (AHP) method to determine the criteria weights and fuzzy axial distance‐based aggregated measurement (ADAM) method for ranking the alternatives. The findings of this study allow for the identification of the most important risks associated with CSC enabling stakeholders to allocate resources strategically and focus efforts on crucial areas. Results highlight that the most important risks are supply chain complexity, resource availability, and quality and technological challenges. The most important contributions are the identification of the most relevant risks that threaten the resilience and sustainability of CSCs, the establishment of the framework for their evaluation, and the development of a novel hybrid MCDM model for their ranking. These contributions are also the main theoretical implications of the study. On a practical level, it enables organizations to strategically manage risks, standardize risk assessment, and improve their competitive advantage by enhancing resilience, reducing disruptions, and lowering operational costs.

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.

A new drought model for disaster risk management in the Punjab, Sindh and Baluchistan provinces of Pakistan

Drought poses a continual threat to both lives and livelihoods in the Global South. Although the impact on food security from drought could be reduced through early release of funds, the humanitarian sector typically reacts to crises rather than anticipates them. A significant challenge lies in devising a drought monitoring and forecasting system that can function across environmentally and economically diverse regions. This is particularly evident in Pakistan, which encompasses environments ranging from fertile riverbeds to arid deserts. This paper details the development, implementation, and operation of an anticipatory drought Disaster Risk Financing (DRF) programme for the provinces of Punjab, Sindh, and Baluchistan in Pakistan. Key to the DRF development are a new yield model for the primary crop in the target season (winter wheat), and a novel forecasting system for four seasonal drought indicators - namely winter wheat yield, precipitation, normalised difference vegetation index (NDVI) and vegetation health index (VHI). Formal evaluations demonstrate that the forecasts are skillful up to 2 months in advance of the end of the season – enabling anticipatory release of funds. The work presented here is applicable beyond Pakistan. Indeed, the model and the methodologies are sufficiently broad and adaptable to be utilised in arid and semi-arid regions across the Global South.

Normative Approach to Workers’ Mental Well-Being in the Digital Era

The paper aims to provide theoretical insights and explore the comparative legal practice of approaching mental health and well-being at the workplace by applying legal normative and comparative methods in a digitalized world of work. In this regard, subordination vs autonomy needs to be considered as a starting theoretical point accompanied by an overview of comparative legal approaches that have recently introduced some novel legal mechanisms, such as the right to disconnect to deal better with the exercise of fundamental labor rights. Additionally, introducing a psychosocial risk management model in occupational health and safety could significantly improve workers’ mental health and well-being in the digital age. Therefore, the proactive, holistic, and integrated approach to workers’ rights and status in the digital environment must be analyzed by exploring the bounding point between organizational management views on the subject and labor law standpoints.

Stochastic models of risk management of worker fatigue emergence

The purpose of the paper is to develop stochastic models for managing the risk of fatigue in an organisation, taking into account the intensity of the negative impact of fatigue factors on workers at the workplace and the intensity of their recovery from such an impact.It uses the method of analysis of scientific literature to actualise the purpose and define the research tasks; Markov process theory methods are used for mathematical description of random processes of worker fatigue development and their recovery from it during a work shift; methods of probability theory and queuing are used to find the limiting probability distribution of random Markov process’ states.The proposed stochastic models allow the organisation to carry out the process of managing the risk of fatigue emergence by changing the work-rest schedule’s duration, depending on the parameters’ characteristics of the negative impact intensity of fatigue factors on workers and the recovery of their corpora from such an impact. By changing the specified parameters’ characteristics, it is possible to determine the work schedule during which the period of worker’s fatigue will be as long as possible and the rest schedule during which the period of recovery from the fatigue state will be minimal.The application of the proposed models makes it possible to increase the level of labour productivity in the organisation by determining such durations of work and rest schedules, which provide the opportunity for workers to carry out labour activities during the maximum possible period of time of the work shift, without reaching a fatigued state.For the first time, an approach for managing the fatigue risk is proposed by establishing dependencies between the duration of work and rest schedule and the parameters’ characteristics of the negative impact intensity of the fatigue factors on the worker and their recovery from such an impact, based on the application of the Markov processes theory.

THE INFLUENCE OF PRODUCTION RISKS ON THE SAFETY OF PRODUCTION PROCESSES: ANALYSIS AND FORECASTING

The study is focused on the analysis of production risks in the field of enterprise management. Production risk is described as the probability of occurrence of undesirable events that may lead to deviations from expected results due to various management decisions. The main characteristics of such a risk are its variability and uncertainty. Risk management is defined as a procedure that includes all aspects of the company’s activities, starting with the formation of a strategy and ending with the control of acceptable risks in order to achieve the set goals. The management process covers the identification of events that may adversely affect the production process and their proper management. The article presents a logical model of production risk management and its integration with the management system of the company’s fixed assets. To assess the level of risk and its comparative analysis, the index method was proposed, which involves the calculation of the production risk index for each type of production resources in order to determine the priority of work. The article also discusses the scales for assessing the probability of production risk and the impact of its consequences, developed on the basis of mathematical forecasting. These scales can be adapted taking into account the opinions of experts and collected data. The article reveals how enterprise resources are prioritized taking into account production risk and budget constraints. This includes developing asset maintenance and repair cost plans, as well as updating the risk register and creating a probability and consequence matrix. An important feature of the model is its difference from existing approaches to the distribution of production resources, which includes the use of a formalized criterion for priority selection of works.

How AI Changes the Game in Finance Business Models

Purpose: This paper aims to examine the impact of AI on finance, including banking, risk management, and business models, while introducing AI-based tools. By exploring these applications, one can gain insights into the benefits of artificial intelligence in enhancing efficiency, productivity, and fostering innovation within financial sectors. Methodology: The research methodology employed in this study is a literature review. Various sources have been explored, studied, and analyzed within the financial domain to highlight the utilization of artificial intelligence. This includes risk management, identity verification, asset management, and data science applications. Furthermore, specific cases have been examined as part of case studies. Findings: The findings indicate that AI is transforming the finance industry by reshaping traditional business models and creating new growth opportunities. The integration of AI in finance has empowered companies to enhance decision-making processes, improve customer experiences, boost operational efficiency, and lower costs. AI-driven tools like predictive analytics, machine learning, and natural language processing are revolutionizing financial institutions' operations and customer interactions. As AI advances, it will play a crucial role in shaping the future of financial business models, driving competitiveness, and unlocking new industry potentials. Embracing AI technology is imperative for companies seeking to stay competitive and thrive in the dynamic financial landscape. Originality/Value: This research introduces various applications of artificial intelligence across different financial sectors alongside practical tools that can enhance the significance of this paper.

Geoinformation model for risk management in Burundi

This paper is focused on the development of a geographic information system model for decision preparation. The application of indicator approach for visualization of risks associated with agricultural activities is presented. The paper describes not only the functioning of the geographic information model, but also the interaction between the geographic information model, database and web application in order to present in real time the geographic risks inhibiting agricultural activities.

Developing conceptual model for safety risk management in aviation as building capacity and resilience strategy

Purpose This study aims to develop an inclusive, multidisciplinary, flexible and organizationally adaptable safety risk management framework, including diversity management, that will be implemented to ensure safety is and remains at the desired level. If the number of incidents and potential incidents that could lead to accidents and their impact rates are to be reduced operationally and administratively, aviation safety risks and sources of risk must be better understood, sources of risk identified, and the safety risk management framework designed in an organization-specific and organization-wide sustainable way. At this point, it is necessary to draw the conceptual framework well and to define the boundaries of the concepts well. In this study, a framework model that can be adapted to the organization is proposed to optimize the management of risks and provide both efficient and effective resource allocation and organizational structure design in its operations and management functions. Design/methodology/approach The qualitative research method – triple techniques – was deemed appropriate for this study, which aims to identify, examine, interpret and develop the situations of safety management models. In this context, document analysis, business process modeling technique and Delphi techniques from qualitative research methods were used via integration as the methodology of this research. Findings To manage dynamic civil aviation management activities and business processes effectively and efficiently, the risk management process is the building block of the “Proposed Process Model” that supports the decision-making processes of aviation organizations and managers. This “Framework Conceptual Model” building block also helps build capacity and resilience by enabling continuous development, organizational learning, and flexible structuring. Research limitations/implications This research is limited to air transportation and aviation safety management issues. This research is limited specifically to a safety-based risk management framework for the aviation industry. This research may have social implications as source saving, optimum resource use and capacity building will make a contribution to society and add value besides operational and practical implementation. Social implications This research may contribute to more safe operations and functions in the aviation industry. Originality/value Management and academia may gain considerable support from this research to manage their safety risks via a corporate-tailored risk management framework, both improving resilience and developing corporate capacity. With this model presented, decision-makers will have a guiding structure that can optimally manage the main risk types that may be encountered in the safety risk in the fields of suppliers, manufacturers, demand changes, logistics, information management, environmental, legal and regulatory. Existing studies in the literature are generally in the form of algorithms and cannot be used as a decision-making support tool. This model aims to fill the gap in the literature. In addition, added value may be created by applying this model to optimum management safety risks in the real aviation industry and its related sectors.

The advantage of artificial intelligence application in financial risk assessment and management

The increasing perfection of artificial intelligence technology has brought subversive changes to the field of financial risk management. The application of artificial models such as neural networks, support vector machines, and mixed intelligence in financial risk management can improve the speed of data processing, provide deep insight into data analysis, reduce human labour costs, and hence improve the efficiency of financial risk control. Meanwhile, the increasing amount of data and the application of AI also bring new challenges to financial risk management, such as the risk of program error and information security. This paper introduces in detail the application status of three models, including Support Vector Machine, Support Vector Machine, and Large Language Model in risk management Based on this, this paper analyses the advantages of AI applications in promoting and reforming the financial industry. The goal is to provide an in-depth examination of present implementations and their respective benefits, as well as to investigate potential future advances in this sector.

Achieving Sustainability in Newly Founded Start-Up Businesses: Navigating Challenges and Gaining Insights

Entrepreneurs embody multifaceted roles as leaders, risk-takers, and learners, driving toward success while transforming dreams into reality. However, amid their journey, they encounter formidable challenges. Hence, this article delves into startup entrepreneurs' hurdles in fostering sustainability. Amidst today's focus on sustainability, startups must navigate obstacles to enhance customer ratings and long-term performance. Examining risk management models, implementing eco-friendly technologies, and refining marketing strategies are crucial. Moreover, fostering virtual collaboration among geographically dispersed employees is essential to mitigate external challenges and achieve sustainability. Through these endeavors, entrepreneurs pave the path toward enduring success in the dynamic landscape of startup ventures.

Risk management in diffused structures: Analysis for Shared Services Centers


 
 
 Research background: Shared Services Centers (SSCs) are often a spin-off of corporate services to separate all operational tasks from the corporate headquarters. The primary focus of the headquarters should be on leadership and corporate governance. However, many SSCs have also been given some corporate functions. Risk management is a significant part of governance and one of its principles. The effectiveness of risk management depends on its integration into the organization’s governance, including decision-making. The question is how a modern SSC manages this part of governance.
 Purpose: This study aims to present the conditions that prove the implementation of risk management in SSCs and to evaluate the most effective risk management model.
 Research methodology: Targeted literature review followed by surveys and a case study.
 Results: The risk management process consists of several stages that are repeated in many models (identification and risk assessment, risk management model selection, and monitoring). Thus, it is possible to use each model in the SSC concerning the migrated processes, considering the limitations of the strategic goals definition. However, not all methods will find a potential application in risk management in the SSC, considering the most effective risk assessment.
 Novelty: While the literature provides models that can be of use, a lack of specific guidelines has been recognized, especially regarding the risk management aspect in SSCs. In the last decade, the focus was on the expected benefits coming from standardization, ignoring the new risks in SSC processes.
 
 

Sovereign Credit Default Swap Market Volatility in BRICS Countries Before and During the COVID-19 Pandemic

SCDS (Sovereign Credit Default Swaps) are becoming more widely used as a country risk indicator after 2008 and stand out for providing real-time information rather than periodic reporting. The COVID-19 pandemic has led to economic disruptions and a decline in international trade. Understanding how the Pandemic affects SCDS return volatility in emerging economies like BRICS forms the motivation for our research. With this study, we aim to determine the impact of the COVID-19 Pandemic on SCDS return volatility in Brazil, Russia, India, China and South Africa, known as the BRICS countries. We used the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model to analyze the data, which consisted of the daily closing price data for SCDS. The date of the first COVID-19 case in each country has been taken as the beginning of the COVID-19 Pandemic in each country. The results of the estimated GARCH models show that the volatility processes of the SCDS return series differ between periods. EGARCH model results indicate that shocks created by news in these countries during the Pandemic have a small and persistent effect on Brazil and Russia's SCDS return volatility, while they have a large and enduring effect on China and South Africa's SCDS return volatility. The findings will guide policymakers and portfolio managers in determining risk management models.

A simulation-based risk interaction network model for risk management in international construction projects

The inherent complexity in International Construction Projects (ICPs) brings forth the intricate interactions between risks, invalidating the risk analysis under the conventional perception of independent risks. The diverse phases of ICPs feature the temporal dynamics of risks and interactions, exacerbating the challenge of pinpointing critical risks. This study aims to model how the risks are interconnected and amplified by proposing an ICP risk management framework, in which the project phase-based Risk Interaction Network (RIN) is constructed. An integrated index is designed to evaluate each risk’s criticality and discern key risks within a two-part procedure, where the simulation-based model estimates risk sensitivity to predecessor risks and captures the stochasticity of risk occurring, and network analysis gauges risk spread power on the entire network. An example of an application is provided to illustrate the utility of the proposed framework.

Cognitive Modeling of Cargo Port Risk Management

Рurpose of research. Increasing the efficiency of cargo port business processes through the use of risk management technology based on the application of a cognitive modeling.Methods. A cognitive modeling of cargo port risk management is proposed, based on a comprehensive step-by-step application of the concept of multi-level goal setting, which involves a thorough elaboration of the cargo port's goals, as well as indicators for assessing the achievability of goals by developing a balanced scorecard (BSS) and constructing a logicalprobabilistic (LP) model; a logical-ontological model developed on the basis of the connections established by the LP model; a simulation model used to check recommendations for adjusting the elements of the system under consideration, developed on the basis of queries to the ontological model, in order to select the most acceptable options for recommendations or combinations thereof and formulate management decisions based on them. Results. Based on the formulated purpose of the study and the assigned tasks, a concept of cognitive modeling was developed, which involves the use of knowledge about the connections between risks, goals, indicators for assessing port activities, as well as clarifying coefficients and the nature of their influence on each other in order to develop recommendations for managing the risks of a cargo port at based on queries to the ontological model. The simulation model within the framework of the proposed conceptual approach allows us to develop management decisions on adjusting the operational components of the system in order to prevent risk situations in the long term (at the tactical and strategic levels) taking into account the influence of external factors. Cognitive modeling is based in this work on the integration of logical-probabilistic, logical-ontological and simulation modeling. Conclusion. As a result of the implementation of the set goals and objectives, a cognitive model of cargo port risk management was proposed. This model combines various types of modeling and takes into account different levels of management. As a result of experiments with a simulation model, the most effective recommendations generated based on a query to the ontological model are selected as management decisions.

A q$q$ Theory of Internal Capital Markets

ABSTRACTWe propose a tractable model of dynamic investment, spinoffs, financing, and risk management for a multidivision firm facing costly external finance. Our analysis formalizes the following insights: (i) Within‐firm resource allocation is based not only on divisions' productivity, as in winner‐picking models, but also their risk; (ii) firms may voluntarily spin off productive divisions to increase liquidity; (iii) diversification can reduce firm value in low‐liquidity states, as it increases the spinoff cost and hampers liquidity management; (iv) corporate socialism makes liquidity less valuable; and (v) division investment is determined by the ratio between marginal and marginal value of cash.

The impact of the corporate risk management model on the quality of financial reports in Jordanian commercial banks

The objective of this study is to empirically elucidate the influence of the corporate risk management model on the quality financial reports within the context of Jordanian commercial banks, with a specific focus on two critical dimensions: relevance and faithful representation. According to signal theory, each company tries to reduce information asymmetry by providing high-quality of financial reports that have the ability to demonstrate their qualities, depending on that concept. The predominant trend in contemporary practice is the utilization of risk management as a tool to elevate the quality of financial reports. This, in turn, assists in curtailing the manipulation of financial reports, culminating in the issuance of reports characterized by credibility and transparency. This, in turn, reflected on all different users of financial reports in the decision-making process. To accomplish the study's objectives, a descriptive-deductive (analytical) research approach was adopted. The research population encompassed a total of thirteen commercial banks operating at Jordan. Data were collected through a structured questionnaire administered to a randomly selected sample of 234 employees from the financial and supervisory departments of these banks. The study's results strongly suggest that the risk management model has a big effect on the quality of financial reports in Jordanian banks. This model includes things like setting goals, event identification, risk assessment, risk response, supervisory activities, and the evaluation of the financial reporting system.

Model for Technology Risk Assessment in Commercial Banks

As the complexity of banking technology systems increases, the prevention of technological risk becomes an endless battle. Currently, most banks rely on the experience and subjective judgement of experts and employees to allocate resources for technological risk management, which does not effectively reduce the frequency of technology-related incidents. Through an analysis of mainstream risk management models, this study proposes a technology-based risk assessment system based on machine learning. It first identifies risk factors in bank IT, preprocesses the sample data, and uses different regression prediction models to train the processed data to build an intelligent assessment model. The experimental results indicated that the Genetic Algorithm–Backpropagation Neural Network model achieved the best performance. Based on assessment indicators, indicator weight values, and risk levels, commercial banks can develop targeted prevention and control measures by applying limited resources to the most critical corrective actions, thereby effectively reducing the frequency of technology-related incidents.

Stochastic properties of coastal flooding events – Part 2: Probabilistic analysis

Abstract. Low-intensity but high-frequency coastal flooding, also known as nuisance flooding, can negatively affect low-lying coastal communities with potentially large socioeconomic effects. Partially driven by wave runup, this type of flooding is difficult to predict due to the complexity of the processes involved. Here, we present the results of a probabilistic analysis of flooding events measured on an eroded beach at the Texas coast. A high-resolution time series of the flooded area was obtained from pictures using convolutional neural network (CNN)-based semantic segmentation methods, as described in the first part of this contribution. After defining flooding events using a peak-over-threshold method, we found that their size follows an exponential distribution. Furthermore, consecutive flooding events were uncorrelated at daily timescales but correlated at hourly timescales, as expected from tidal and day–night cycles. Our measurements confirm the broader findings of a recent multi-site investigation of the probabilistic structure of high-water events that used a semi-empirical formulation for wave runup. Indeed, we found a relatively good statistical agreement between our CNN-based empirical flooding data and predictions using total-water-level estimations. As a consequence, our work supports the validity of a relatively simple probabilistic model of high-frequency coastal flooding driven by wave runup that can be used in coastal risk management and landscape evolution models.

Aggregation of Risk Management and Non-Parametric Models to Rank Failure Modes of Radio Frequency Identification Systems

The failure mode causes and effects analysis (FMCEA) is a commonly used reliability approach. It identifies, predicts, and analyzes potential failure modes affecting the proper function of equipment or the process under study, along with their roots and consequences. FMCEA aims to evaluate and assess the risks resulting from their occurrence, intending to suggest corresponding repair, adjustment, and precautionary measures to be planned during the conception, instruction, or implementation stages. However, the FMCEA has been criticized in the literature for its many inherent shortcomings related to risk assessment and prioritization. Therefore, this study presents an enhanced FMCEA method to address the deficiencies of the traditional risk priority number (RPN) and improve the reliability of risk assessments and corrective actions. A data envelopment analysis (DEA), as a non-parametric method, is used to evaluate the efficiency of these failures by considering their fixing time and cost and deciding on their final priority ranks. Sub-failure modes and their interrelationships are also taken into account. The radio frequency identification (RFID) system was chosen as an example due to its core role in Industry 4.0 and the Internet of Things (IoT) to demonstrate the effectiveness and usefulness of the proposed method. A total of 67 failures related to both hardware and software parts, including the environmental impacts of this technology, have been disclosed. The results of the conventional and the suggested FMCEA methods are found to be considerably different, with ten failure modes classified as being the most efficient.