Survey and Prospect for Applying Knowledge Graph in Enterprise Risk Management

Enterprise risk management holds significant importance in fostering sustainable growth of businesses and in serving as a critical element for regulatory bodies to uphold market order.Amidst the challenges posed by intricate and unpredictable risk factors,knowledge graph technology is effectively driving risk management,leveraging its ability to associate and infer knowledge from diverse sources.This review aims to comprehensively summarize the construction techniques of enterprise risk knowledge graphs and their prominent applications across various business scenarios.Firstly,employing bibliometric methods,the aim is to uncover the developmental trends and current research hotspots within the domain of enterprise risk knowledge graphs.In the succeeding section,systematically delineate the technical methods for knowledge extraction and fusion in the standardized construction process of enterprise risk knowledge graphs.Objectively comparing and summarizing the strengths and weaknesses of each method,we provide recommendations for addressing the existing challenges in the construction process.Subsequently,categorizing the applied research of enterprise risk knowledge graphs based on research hotspots and risk category standards,and furnishing a detailed exposition on the applicability of technical routes and methods.Finally,the future research directions that still need to be explored in enterprise risk knowledge graphs were discussed,and relevant improvement suggestions were proposed.Practitioners and researchers can gain insights into the construction of technical theories and practical guidance of enterprise risk knowledge graphs based on this foundation.

Mapping Climate Services for Disaster Risk Management: A Systematic Review and Research Gaps from a Policy Process Perspective

Climate services (CS) are crucial for mitigating and managing the impacts and risks associated with climate-induced disasters. While evidence over the past decade underscores their effectiveness across various domains, particularly agriculture, to maximize their potential, it is crucial to identify emerging priority areas and existing research gaps for future research agendas. As a contribution to this effort, this paper employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to review the state-of-the-art in the field of climate services for disaster risk management. A comprehensive search across five literature databases combined with a snowball search method using ResearchRabbit was conducted and yielded 242 peer-reviewed articles, book sections, and reports over 2013-2023 after the screening process. The analysis revealed flood, drought, and food insecurity as major climate-related disasters addressed in the reviewed literature. Major climate services addressed included early warning systems, (sub)seasonal forecasts and impact-based warnings. Grounded in the policy processes’ theoretical perspective, the main focus identified and discussed three prevailing policy-oriented priority areas: 1) development of climate services, 2) use-adoption-uptake, and 3) evaluation of climate services. In response to the limitations of the prevalent supply-driven and top-down approach to climate services promotion, co-production emerges as a cross-cutting critical aspect of the identified priority areas. Despite the extensive research in the field, more attention is needed, particularly pronounced in the science-policy interface perspective, which in practice bridges scientific knowledge and policy decisions for effective policy processes. This perspective offers a valuable analytical lens as an entry point for further investigation. Hence, future research agendas would generate insightful evidence by scrutinizing this critical aspect given its importance to institutions and climate services capacity, to better understand intricate facets of the development and the integration of climate services into disaster risk management.

Electronic Commerce Security and Risk Management: An Important Part of College Curriculum

With the rapid expansion of e-commerce,its security and risk management problems become increasingly prominent.In the current business environment,the ability to understand and apply e-commerce security and risk management has become an important criterion to measure a good person.Therefore,the importance of e-commerce security and risk management courses in college education is self-evident.This course can not only help students master the basic knowledge of e-commerce,but also enable them to understand how to deal with various risks in practical work and ensure the safe operation of e-commerce.At the same time,through the study of e-commerce security and risk management,students can better understand the operation mode and law of e-commerce,and lay a solid foundation for their future career.In general,e-commerce security and risk management occupy an important position in the curriculum of colleges and universities,and play a crucial role in cultivating e-commerce professionals with practical operation ability and innovative thinking.

The indispensable role of resilience in rational landslide risk management for social sustainability

Landslide disasters reflect the conflict between human society and the natural environment,posing challenges to the sustainable development of mountain regions.Identification of potential landslides,estimation of the degree of damage and potential losses of elements at risk,and control of the loss are the major tasks of landslide risk management.Resilience is defined as a social system’s comprehensive abilities to cope with disasters,including the abilities to prepare,anticipate,preserve,absorb,respond,resist,recover,mitigate,learn,and adapt.As an indispensable role,resilience enables more rational landslide risk management for social sustainability.However,quantitative landslide risk management does not pay sufficient attention to the role of resilience.Hence,in this paper,the role of resilience in a landslide risk management framework is systematically discussed.A quantita-tive landslide risk management framework consists of hazard analysis,exposure analysis,risk estimation,risk evaluation,and risk control.In hazard analysis,resilience assessment could help identify potential landslides that could cause significant damage due to the poor resilience of the elements at risk.Resilience assessment in exposure analysis might aid in identifying the most vulnerable elements or regions to certain landslides.Consid-eration of resilience in risk estimation aids in the calculation of indirect losses and improves the results of direct losses analysis.In risk evaluation,resilience as a disaster-coping ability will impact the social system’s landslide risk tolerance threshold.Enhancing resilience is an essential strategy to reduce the vulnerability of social sys-tems.We also proposed that the efficient use of risk information will increase the accuracy of landslide resilience assessments.

Online payment fraud:from anomaly detection to risk management

Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account.Successfully preventing this requires the detection of as many fraudsters as possible,without producing too many false alarms.This is a challenge for machine learning owing to the extremely imbalanced data and complexity of fraud.In addition,classical machine learning methods must be extended,minimizing expected financial losses.Finally,fraud can only be combated systematically and economically if the risks and costs in payment channels are known.We define three models that overcome these challenges:machine learning-based fraud detection,economic optimization of machine learning results,and a risk model to predict the risk of fraud while considering countermeasures.The models were tested utilizing real data.Our machine learning model alone reduces the expected and unexpected losses in the three aggregated payment channels by 15%compared to a benchmark consisting of static if-then rules.Optimizing the machine-learning model further reduces the expected losses by 52%.These results hold with a low false positive rate of 0.4%.Thus,the risk framework of the three models is viable from a business and risk perspective.

Enterprise Internal Control Based on Risk Management with Moutai Group as an Example

Focusing on the two themes of"internal control"and"risk management",this paper makes an in-depth analysis of the current situation of risk management and internal control of Moutai Group.It analyzes the current situation of risk management and internal control of Moutai Group.It is found that the risk management of Moutai Group is not perfect,the information exchange is not smooth,and the internal control assessment is not perfect.Finally,it puts forward some corresponding countermeasures,including establishing an effective information communication mechanism,perfecting the risk assessment system,and strengthening the construction of the professional talent team.

Safety risk management of underground engineering in China：Progress, challenges and strategies

Underground construction in China is featured by large scale, high speed, long construction period,complex operation and frustrating situations regarding project safety. Various accidents have been reported from time to time, resulting in serious social impact and huge economic loss. This paper presents the main progress in the safety risk management of underground engineering in China over the last decade, i.e.（1） establishment of laws and regulations for safety risk management of underground engineering,（2） implementation of the safety risk management plan,（3） establishment of decision support system for risk management and early-warning based on information technology, and（4） strengthening the study on safety risk management, prediction and prevention. Based on the analysis of the typical accidents in China in the last decade, the new challenges in the safety risk management for underground engineering are identified as follows：（1） control of unsafe human behaviors;（2） technological innovation in safety risk management; and（3） design of safety risk management regulations. Finally, the strategies for safety risk management of underground engineering in China are proposed in six aspects, i.e. the safety risk management system and policy, law, administration, economy, education and technology.

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT) model and the K-means cluster algorithm to produce a regional landslide susceptibility map. Yanchang County, a typical landslide-prone area located in northwestern China, was taken as the area of interest to introduce the proposed application procedure. A landslide inventory containing 82 landslides was prepared and subsequently randomly partitioned into two subsets: training data(70% landslide pixels) and validation data(30% landslide pixels). Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means cluster algorithm. The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC) curve) of the proposed model was the highest, reaching 0.88,compared with traditional models(support vector machine(SVM) = 0.85, Bayesian network(BN) = 0.81,frequency ratio(FR) = 0.75, weight of evidence(WOE) = 0.76). The landslide frequency ratio and frequency density of the high susceptibility zones were 6.76/km^(2) and 0.88/km^(2), respectively, which were much higher than those of the low susceptibility zones. The top 20% interval of landslide occurrence probability contained 89% of the historical landslides but only accounted for 10.3% of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without containing more " stable" pixels. Therefore, the obtained susceptibility map is suitable for application to landslide risk management practices.

Engineering complex systems applied to risk management in the mining industry

Related to complexity, there is a wide diversity of concepts, ranging from ‘‘systemic" to ‘‘complex", implying a need for a unified terminology. Per different authors, the main drivers of complexity can be found in human behaviour and uncertainty. This complexity, structural or dynamic can be organizational, technological, or nested in their relationship. ISO international standard 31000:2009 definition of risk management ‘‘coordinated activities to direct and control an organization with regard to risk", when applied to economic sectors, industry, services, project, or activity, it requires the use of models or theories as guidelines. Therefore, as its basic elements comprehend human behaviour and/or uncertainty, risk management to be effective and adapted as much as possible to reality, must be operational within complex systems, as already demonstrated in different R&D environments. Risk management faces demanding challenges when approaching specific and endogenous needs, such as the mining sector. This paper presents a multivariable function analysis methodology approach based on complex system modelling and through real data corresponding to a risk management tool in the mining sector.

The use of UAVs for landslide disaster risk research and disaster risk management: a literature review

On a global scale,from 2005 to 2019,there were 275 high-magnitude,low-frequency disasters that involved 14,172 fatalities and four million affected people.Similar patterns have taken place during longer periods of time in recent decades.This paper aims to analyse the contribution of the international landslide research community to disaster risk reduction and disaster risk management in reference to the use of Unmanned Aerial Vehicles(UAVs)in a literature review.The first section notes the relevance of disaster risk research contributions for the implementation of initiatives and strategies concerning disaster risk management.The second section highlights background information and current applications of drones in the field of hazards and risk.The methodology,which included a systematic peer review of journals in the ISI Web of Science and SCOPUS,was presented in the third section,where the results include analyses of the considered data.This study concludes that most current scholarly efforts remain rooted in hazards and post-disaster evaluation and response.Future landslide disaster risk research should be transdisciplinary in order to strengthen participation of the various relevant stakeholders in contributing to integrated disaster risk management at local,subnational,national,regional and global levels.

Managing Climate Change Risk in China's Agricultural Sector: The Potential for an Integrated Risk Management Framework

Climate change poses a serious threat to the future food security of China, which is among the most disaster-prone countries in the world. This paper discusses the implications of climate change for China＇s agricultural sector. Its main objectives are to identify the agricultural risks associated with climate change, to introduce a conceptual framework for agricultural climate risk management and to enumerate key adaptation strategies, challenges, and recommendations.

Systemic risk management and investment analysis with financial network analytics:research opportunities and challenges

Recent economic crises like the 2008 financial tsunami has demonstrated a critical need for better understanding of the topologies and various economic,social,and technical mechanisms of the increasingly interconnected global financial system.Such a system largely relies on the interconnectedness of various financial entities such as banks,firms,and investors through complex financial relationships such as interbank payment networks,investment relations,or supply chains.A network-based perspective or approach is needed to study various financial networks in order to improve or extend financial theories,as well as develop business applications.Moreover,with the advance of big data related technologies,and the availability of huge amounts of financial and economic network data,advanced computing technologies and data analytics that can comprehend such big data are also needed.We referred this approach as financial network analytics.We suggest that it will enable stakeholders better understand the network dynamics within the interconnected global financial system and help designing financial policies such as managing and monitoring banking systemic risk,as well as developing intelligent business applications like banking advisory systems.In this paper,we review the existing research about financial network analytics and then discuss its main research challenges from the economic,social,and technological perspectives.

Analysis and evaluation of enterprise risk management capability elements

Research on enterprise risk management capability is conducted with a view of discerning and processing risks, in which an evaluation index system and an evaluation model of enterprise risk management capabilities are constructed. The risk management capability consists of four aspects, i.e. risk identification capability, risk assessment capability, risk planning capability and risk control capability. Risk identification and assessment capabilities reflect the level of enterprises on finding and analyzing risks efficiently, while risk planning and control capabilities reflect the level of enterprises on resolving risks effectively. Each of capabilities is then further divided into more detailed elements based on the characteristics of enterprise development for a quantitative analysis of enterprise risk management capability. The approach adopted is a quantitative technique based on the use of fuzzy comprehensive evaluation. Finally, a case is pulled in to illustrate the feasibility of the method from an empirical perspective. This study is expected to be helpful for enterprises in cultivating their core capabilities.

Implementation of a risk management plan in a hospital operating room

A risk management program based on ASeNZS4360 risk management standards was developed and implemented in the operating room of Peking University Third Hospital.To accomplish this task,we developed a risk quantification matrix and a risk register form to identify potential risks in the operating room,and then implemented operating room policies designed to reduce or eliminate those risks.We also established a consultation mechanism and risk monitoring system designed to minimize risks to operation room nurses.Finally,we continuously seek to improve our operating room risk management capabilities,so we can continue to improve the quality of service provided and guarantee the safety of surgical patients.

Risk management in open cut coal mines

There are a number of geotechnical systems used at open cut operations of Anglo American's Metallurgical Coal business unit in Australia. These systems are mainly mine site specific, based on geological information and used as hazard plans. They are qualitative and reliant upon the experienced judgement of site based engineers and the general workforce. They are not suitable for calculating the associated risks of geological structures and mining responses. A uniform risk rating system that can be used to provide an unbiased, standard and quantifiable assessment of the risk from highwall and lowwall failures is therefore developed using the experience gained with such systems in Anglo American's South African operations. This system is a semi-quantitative risk rating system and takes into account the relative differences in the importance of hazards as experienced at each mine site as a result of different combinations of geotechnical factors and mining conditions. It is based on critical geotechnical and other parameters that have been identified by site mining engineers, geologists and geotechnical engineers. The primary advantage of this risk rating system is that all open cut mines in the Anglo American's Metallurgical Coal operations use a near identical system, which enables the user to compare the risk after each blast and with other mines. The system can be adjusted to meet local mine specific requirements. The implementation of this system, a computer program that automatically calculates the risks and recommends predetermined controls, has been made as practical and as easy to use as possible. This program can be used by personnel from other mining disciplines not directly related to geotechnical engineering.

Evolution of seismic risk management for insurance over the past 30 years

During the past 30 years, there has been spectacular growth in the use of risk analysis and risk management tools developed by engineers in the financial and insurance sectors. The insurance, the reinsurance, and the investment banking sectors have enthusiastically adopted loss estimation tools developed by engineers in developing their business strategies and for managing their financial risks. As a result, insurance/reinsurance strategy has evolved as a major risk mitigation tool in managing catastrophe risk at the individual, corporate, and government level. This is particularly true in developed countries such as US, Western Europe, and Japan. Unfortunately, it has not received the needed attention in developing countries, where such a strategy for risk management is most needed. Fortunately, in the last five years, there has been excellent focus in developing ＂Insur Tech＂ tools to address the much needed ＂Insurance for the Masses＂, especially for the Asian Markets. In the earlier years of catastrophe model development, risk analysts were mainly concerned with risk reduction options through engineering strategies, and relatively little attention was given to financial and economic strategies. Such state-of-affairs still exists in many developing countries. The new developments in the science and technologies of loss estimation due to natural catastrophes have made it possible for financial sectors to model their business strategies such as peril and geographic diversification, premium calculations, reserve strategies, reinsurance contracts, and other underwriting tools. These developments have not only changed the way in which financial sectors assess and manage their risks, but have also changed the domain of opportunities for engineers and scientists.This paper will address the issues related to developing insurance/reinsurance strategies to mitigate catastrophe risks and describe the role catastrophe risk insurance and reinsurance has played in managing financial risk due to natural catastrophes. Historical losses and the share of those losses covered by insurance will be presented. How such risk sharing can help the nation share the burden of losses between tax paying public, the ＂at risk＂ property owners, the insurers and the reinsurers will be discussed. The paper will summarize the tools that are used by the insurance and reinsurance companies for estimating their future losses due to catastrophic natural events. The paper will also show how the results of loss estimation technologies developed by engineers are communicated to the business flow of insurance/reinsurance companies. Finally, to make it possible to grow ＂Insurance for the Masses - IFM＂, the role played by parametric insurance products and Insur Tech tools will be discussed.

Risk Management of Clinical Reference Dosimetry of a Large Hospital Network Using Statistical Process Control

Managing TG-51 reference dosimetry in a large hospital network can be a challenging task. The objectives of this study are to investigate the effectiveness of using Statistical Process Control (SPC) to manage TG-51 workflow in such a network. All the sites in the network performed the annual reference dosimetry in water according to TG-51. These data were used to cross-calibrate the same ion chambers in plastic phantoms for monthly QA output measurements. An energy-specific dimensionless beam quality cross-calibration factor, , was derived to monitor the process across multiple sites. The SPC analysis was then performed to obtain the mean, , standard deviation, σk, the Upper Control Limit (UCL) and Lower Control Limit (LCL) in each beam. This process was first applied to 15 years of historical data at the main campus to assess the effectiveness of the process. A two-year prospective study including all 30 linear accelerators spread over the main campus and seven satellites in the network followed. The ranges of the control limits (±3σ) were found to be in the range of 1.7% - 2.6% and 3.3% - 4.2% for the main campus and the satellite sites respectively. The wider range in the satellite sites was attributed to variations in the workflow. Standardization of workflow was also found to be effective in narrowing the control limits. The SPC is effective in identifying variations in the workflow and was shown to be an effective tool in managing large network reference dosimetry.

A Review on the Role of Risk Management (RM) and Value Engineering (VE) Tools for Project Successful Delivery

Both in developed and in developing countries, the construction industry is regarded as an economic investment activity without forgetting its significant relationship with national economic development due to its great contributions to the national gross domestic product (GDP) of the country. Concerning construction processes, both risk management (RM) and value engineering (VE) techniques have commonalities from the beginning up to the completion of the project due to enhancing the project value/quality, meeting the project deadline, and reducing overall project cost. VE includes resolving the uncertainty of project objectives and ensuring that the project is delivered in a value for money way. The key point of RM is to solve the uncertainty of the project itself and its results to ensure that the specifications are achieved within the prescribed time, cost, and quality constraints. This review work is comparatively and collectively focused on assessing the role of RM and VE tools for project successful delivery. It studies the points of difference and common features of the two aspects in terms of construction project delivery. So, this study concluded that in construction RM tool cannot be the chief aim of the all parties involved in the project execution because sometimes it produces itself negative results and reduces project management success. Therefore, RM needs a strong combination with VE due to the dependence of the target in identifying and assessing risks by considering the highest performance and lowest cost. The integration of RM and VE combination in a single study would avoid duplication of work and deliver better value for money thereby leading to better project outcomes.

The Utility of Artificial Intelligence for Mood Analysis,Depression Detection,and Suicide Risk Management

Mood disorders are often an indication or a sign of depression,and individuals suffering from mood swings may face higher probability and increased suicidal tendencies.Depression-also called“clinical depression”or a“depressive disorder”-is a mood disorder that adversely impacts how an individual feels,thinks,and handles daily activities,such as sleeping,eating,or working.To be diagnosed with depression,symptoms must be present most of the time,nearly every day for at least minimum of 2 to 3 weeks.Feeling sad or having low emotional energy may be common among people.For most,however,these feelings are transitory and can be managed by changing daily life routines.But for some,prolonged mood disorders can lead to depression and foster suicidal tendencies.Suicide is a major public health concern.Over 47,000 people died by suicide in the United States in 2017.It is the 10th leading cause of death overall according to NIMH(National Institute of Mental Health).Suicide is complicated and tragic,but it is often preventable.Identifying the warning signs for suicide and how to get help can be a major mitigating factor.In this short communication,we are reviewing the promise and limitations of AI(artificial intelligence)with its integrated tools such as ML(machine learning)and DL(deep learning)for mood analysis as a means for detecting early signs of depression and increased suicidal tendencies for possible suicide risk management.

Observations on the Application of Nursing Risk Management in the Care of Critically Ill Patients in the Respiratory Unit

Objechive:Investigate the effectiveness of mursing risk management in the care of cntically ill patients in the respiratory umit.Methods:Among the cntically ill respiratory patients admitted to our hospital between May 2019 and April 2020,78 patients were randomly selected and divided into an observation group and a control group,each consisting of 39 patients.In the observation group.a mursing nisk management model was implemented,i.e,patients'clinical symptoms were observed at any time to monitor their treatment satisfaction and the effectiveness of their care and routine care was implemented for the control group.Results:The heart rate,respiratory rate,and pH of patients in the observation group were more stable than those in the control group,and their respiratory status was better,with differences in data.There was also sigmifcant statistical significance(P<0.05).The incidence of patient-provider disputes,unplanned extubation,and uplammed events were lower in the observation group conpared to the control group,and their data difference was satistically siguificant(P-0.05).The treatment satisfaction as well as the total effective rate of patients in the observation group was also much higher than that of the contol group,and there was also a statistically sigmificant difference in the data(P<0.05).Conclusion:The musing nisk management model has a significant therapeutic effect in the care of cnitically ill respiratory patients.Therefore,it is worth popularizing to use in the clinical mursing of respiratory cnitical patients.