Application Of Risk Management Research Articles

The Impact of ChatGPT on Finance Technology

With the rapid development of science and technology, financial technology has risen rapidly around the world and has become a key driving force in the financial field. The emergence of natural language processing models such as ChatGPT, has brought new possibilities to financial services. This paper mainly studies the role and impact of ChatGPT in financial technology. Focus includes its applications in customer service, investment advice and risk management, as well as the potential impact of these applications on financial operations and user experience. The purpose of the research is to gain an in-depth understanding of the application of ChatGPT in the financial field, and reveal its potential contribution to improving financial service efficiency. This study uses literature analysis, case analysis and data analysis. Through systematic combing of relevant literature, we can understand the development status of ChatGPT in financial services. The research of this paper found that ChatGPT has a significant influence in the field of financial technology. Its application in customer service can improve service efficiency and provide users with faster and more personalized services. However, to ensure information security and privacy protection, financial institutions need to carefully weigh the pros and cons when adopting ChatGPT technology.

Quantum Temporal Winds: Turbulence in Financial Markets

This paper leverages turbulence theory from physics to examine the similarities and differences between financial market volatility and turbulent phenomena on a statistical physics level. By drawing analogies between the dynamics of financial markets and fluid turbulence, an innovative analytical framework has been developed to enhance our understanding of the complexity inherent in financial markets. The research methodology involves a comparative analysis of several national stock market indices and simulated turbulent velocity time series, with a particular focus on key statistical properties such as probability distributions, correlation structures, and power spectral densities. Furthermore, a financial market capital flow model has been established, and corresponding solutions have been proposed. Through computational simulations and data analysis, it was discovered that financial market volatility shares some statistical characteristics with turbulence, yet there are significant differences in the shape of probability distributions and the timescales of correlations. This indicates that although financial markets exhibit patterns similar to turbulence, as a multivariate-driven complex system, their behavioral patterns do not completely correspond to natural turbulence phenomena, highlighting the limitations of directly applying turbulence theory to financial market analysis. Additionally, the study explores the use of Bézier curves to simulate market volatility and, based on these analyses, formulates trading strategies that demonstrate practical applications in risk management. This research provides fresh perspectives for the fields of financial market theory and econophysics, offering new insights into the complexity of financial markets and the prevention and management of financial risks.

Applying the Precautionary Principle to Hidden Collapse

There is growing evidence around the world of serious decline in biodiversity requiring urgent application of precautionary risk management. A better regulatory regime for precautionary management of long-term risk is now an urgent priority. This article addresses the prioritization of long-term risk management by examining risk management of ecosystems that may be experiencing hidden collapse. Hidden collapse refers to the existence of environmental indicators indicative of future collapse of forests, even though the forest appears intact and not at risk of ecosystem collapse. Professor David Lindenmayer and Dr Chloe Sato (Lindenmayer) first identified hidden collapse in 2018 in Mountain Ash forests of Victoria, Australia. The risk of hidden collapse represents a long-term environmental threat and is a potential trigger for application of the precautionary principle (principle). Implicit in hidden collapse are two preconditions for application of the principle; the risk of a serious or irreversible environmental threat, and the existence of scientific uncertainty about the nature of the risk. Despite hidden collapse satisfying these essential preconditions for applying the principle, decision makers did not apply it in respect hidden collapse of Mountain Ash forests in Victoria. This article considers the current status of the principle in regulation and how it can be adjusted to address long term environmental risk.

A review of nature‐based infrastructures and their effectiveness for urban flood risk mitigation

AbstractAnthropogenic climate change and rapid urbanization are contributing to more frequent and intense urban flooding. There is widespread agreement that traditional gray infrastructure, a single‐purpose solution, fails to address the problem properly and contributes to adverse direct and indirect environmental impacts. As such, Nature‐based Solutions (NbS) can provide improved outcomes to flood risk management along with co‐benefits to society and the economy, as they have numerous benefits incuding often a smaller carbon footprint or even sometimes sequestering carbon. However, there is ambiguity and misconception about NbS and the uptake of NbS for flood management, which is still inadequate compared to traditional gray infrastructure. This research seeks to explore various nature‐based infrastructures including their present status of application for flood risk management to build resilience to urban flooding through a systematic literature review. The robustness of some NbS is questionable and varies across different spatial scales from plot to watershed. NbS can work stand‐alone in many cases as well as supplement traditional gray infrastructure to achieve wider benefits. The review provides a comparison of nature‐based solutions with gray infrastructure, identifies flood mitigation infrastructures that include nature‐based elements, and provides an overview of their effectiveness across different scales. The research findings should contribute to a better understanding of appropriate and diverse options of NbS, gray, and hybrid designs by policymakers and decision‐makers to enable them in effectively designing and implementing urban flood risk mitigation measures.This article is categorized under: Climate and Development > Urbanization, Development, and Climate Change Vulnerability and Adaptation to Climate Change > Learning from Cases and Analogies

​Mean reflected BSDE driven by a marked point process and application in insurance risk management

This paper aims to solve a super-hedging problem along with insurance re-payment under running risk management constraints. The initial endowment for the super-heding problem is characterized by a class of mean reflected backward stochastic differential equation driven by a marked point process (MPP) and a Brownian motion. By Lipschitz assumptions on the generators and proper integrability on the terminal value, we give the well-posedness of this kind of BSDEs by combining a representation theorem with the fixed point argument.

Cigarette Company Classification and Environmental Uncertainty with Risk Management Mediation in Predicting Excise Tax Returns

This research was conducted on the basis of the fact that the amount of excise returns as a deduction from state revenues from the excise sector is fluctuating and tends to increase over the last five years from 2018 to 2022. This research aims to conduct an analysis and obtain empirical evidence regarding the influence of Cigarette Company Classification on excise returns through the implementation of management. risks and the influence of Environmental Uncertainty on excise returns through the application of Risk Management. This research falls into the realm of quantitative research that adheres to a causality approach, the focus of which is testing cause-and-effect relationships between variables. Data collection and analysis were performed using quantitative methods, with a statistical testing approach through path analysis. The source of information used in this research comes from secondary data, namely financial report data from the Directorate General of Customs and Excise and Risk Management Values in the form of profiling tobacco products companies from 2015 to 2022. This research is different from previous research and shows a higher level of originality. because it uses Risk Management variables as mediators and utilizes a unique unit of analysis by adopting indicators of tobacco production capacity and the size of cigarette company capital ownership as a method for measuring company size variables.

Risk Management Strategies in Facing Global Financial Market Volatility

Global financial market volatility refers to the unpredictable fluctuations in the prices of financial assets such as stocks, bonds, currencies, and commodities. This volatility can stem from various factors such as economic indicators, geopolitical events, policy changes, technological advancements, and investor sentiment. This research aims to analyze effective risk management strategies in facing global financial market volatility. Market volatility often triggers anxiety, uncertainty, and irrational investment decisions. In this situation, it is important for investors to adopt the right strategies to minimize risks and maximize long-term investment outcomes. In this study, data is collected from various sources related to global financial market volatility and risk management strategies adopted by investors during periods of uncertainty. Qualitative and quantitative analyses of this data are conducted to identify patterns of investor behavior and the effectiveness of applied risk management strategies. The research findings indicate that maintaining a long-term perspective, retaining portfolio diversification, and taking an active approach to risk management are effective strategies in dealing with market volatility. Open communication with financial professionals is also proven to be essential in helping investors stay calm and make rational decisions.

IDENTIFIKASI POTENSI BAHAYA K3 DAN PENGENDALIAN RISIKO PADA KEGIATAN PEMBORAN DI BALAI DIKLAT TAMBANG BAWAH TANAH SAWAHLUNTO IDENTIFICATION OF POTENTIAL OHS HAZARDS AND RISK CONTROL IN DRILLING ACTIVITES IN THE SAWALUNTO UNDERGROUND MINING AND TRAINING CE

OHS in mining activities is generally known as the Mining Occupational Health and Safety Management System (MOHS) which will regulate how and what forms of prevention and control both potential hazards or risks occur during mining activities. Drilling is one of the stages carried out before blasting activities. This is to obtain drilling fast, with the right position. The drilling, which was carried out at the Underground Mining Training Center, used a Jumbo drill and Jackleg machine. Risk identification is the process of identifying hazards, assessing risks and developing management strategies. The application of risk management has benefits including being able to guarantee business continuity by reducing the risk of every activity that contains a hazard, while the purpose of this study is to identify potential hazards and control the risk of work accidents for both workers and trainees at the Sawalunto Underground Mining Training Center.

A hybrid nonparametric multivariate density estimator with applications to risk management

. Multivariate density estimation is plagued by the curse of dimensionality in theory and practice. We propose a hybrid density estimator of a multivariate density f that combines the strengths of the kernel estimator and the exponential series estimator. This estimator refines a preliminary kernel estimate f ̂ 0 with a multiplicative correction that estimates the ratio r = f / f ̂ 0 with an exponential series estimator. Thanks to the consistency of the pilot estimate, the coefficients of the series expansion tend to approach zero asymptotically. Accordingly, we design a thresholding method for basis function selection. A major obstacle of multivariate exponential series estimator is the calculation of its normalization factor. We resolve this difficulty with Monte Carlo integration, using the pilot kernel estimate as the trial density for importance sampling. This approach greatly enhances the practicality of the hybrid estimator. Numerical simulations demonstrate the good finite sample performance of the hybrid estimator. We present one empirical application in financial risk management.

Temporal Implicit Multimodal Networks for Investment and Risk Management

Many deep learning works on financial time-series forecasting focus on predicting future prices/returns of individual assets with numerical price-related information for trading, and hence propose models designed for univariate, single-task, and/or unimodal settings. Forecasting for investment and risk management involves multiple tasks in multivariate settings: forecasts of expected returns and risks of assets in portfolios, and correlations between these assets. As different sources/types of time-series influence future returns, risks, and correlations of assets in different ways, it is also important to capture time-series from different modalities. Hence, this article addresses financial time-series forecasting for investment and risk management in a multivariate, multitask, and multimodal setting. Financial time-series forecasting, however, is challenging due to the low signal-to-noise ratios typical in financial time-series, and as intra-series and inter-series relationships of assets evolve across time. To address these challenges, our proposed Temporal Implicit Multimodal Network (TIME) model learns implicit inter-series relationship networks between assets from multimodal financial time-series at multiple time-steps adaptively. TIME then uses dynamic network and temporal encoding modules to jointly capture such evolving relationships, multimodal financial time-series, and temporal representations. Our experiments show that TIME outperforms other state-of-the-art models on multiple forecasting tasks and investment and risk management applications.

Manajemen Risiko Reputasi Perbankan Syariah

Inherent risk is the risk attached to the business activities of Islamic banks, whether quantifiable or not, which has the potential to affect the financial position of the bank. Risk management applications, especially the risk of reputation for Islamic banks, either individually or for banks by way of confirmation by The children's industry does not include the active supervision of the board of commissioners, the board, and the DPS, policies, methods, and determination of limits, as well as methods of identification, measurement, control, and risk management and risk SIM. This risk arose, among others, due to the presence of media reports and rumors about banks having a negative attitude and the existence of an inefficient bank communication strategy. A negative announcement to one Islamic bank would tarnish the good name of another Islamic bank, even though other Islamic banks did not participate in this responsible action. The best reputation risk management method is to implement a predictive or preventive action program and a reputation maintenance program. Risk A good name is an abstract risk and is an intangible asset for the industry. Prosecution of reputational risks should be in a preventive way because the cost of handling this risk is very large and can eventually disrupt and kill the industry.

Applications of digital twin technology in construction safety risk management: a literature review

PurposeWith increasing complexity of construction projects and new construction processes and methods are adopted, more safety hazards are emerging at construction sites, requiring the application of the modern risk management methods. As an emerging technology, digital twin has already made valuable contributions to safety risk management in many fields. Therefore, exploring the application of digital twin technology in construction safety risk management is of great significance. The purpose of this study is to explore the current research status and application potential of digital twin technology in construction safety risk management.Design/methodology/approachThis study followed a four-stage literature processing approach as outlined in the systematic literature review procedure guidelines. It then combined the quantitative analysis tools and qualitative analysis methods to organize and summarize the current research status of digital twin technology in the field of construction safety risk management, analyze the application of digital twin technology in construction safety risk management and identify future research trends.FindingsThe research findings indicate that the application of digital twin technology in the field of construction safety risk management is still in its early stages. Based on the results of the literature analysis, this paper summarizes five aspects of digital twin technology's application in construction safety risk management: real-time monitoring and early warning, safety risk prediction and assessment, accident simulation and emergency response, safety risk management decision support and safety training and education. It also proposes future research trends based on the current research challenges.Originality/valueThis study provides valuable references for the extended application of digital twin technology and offers a new perspective and approach for modern construction safety risk management. It contributes to the enhancement of the theoretical framework for construction safety risk management and the improvement of on-site construction safety.

Role of Genetics in Diagnosis and Management of Hypertrophic Cardiomyopathy: A Glimpse into the Future.

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiomyopathy. It follows an autosomal dominant inheritance pattern in most cases, with incomplete penetrance and heterogeneity. It is familial in 60% of cases and most of these are caused by pathogenic variants in the core sarcomeric genes (MYH7, MYBPC3, TNNT2, TNNI3, MYL2, MYL3, TPM1, ACTC1). Genetic testing using targeted disease-specific panels that utilize next-generation sequencing (NGS) and include sarcomeric genes with the strongest evidence of association and syndrome-associated genes is highly recommended for every HCM patient to confirm the diagnosis, identify the molecular etiology, and guide screening and management. The yield of genetic testing for a disease-causing variant is 30% in sporadic cases and up to 60% in familial cases and in younger patients with typical asymmetrical septal hypertrophy. Genetic testing remains challenging in the interpretation of results and classification of variants. Therefore, in 2015 the American College of Medical Genetics and Genomics (ACMG) established guidelines to classify and interpret the variants with an emphasis on the necessity of periodic reassessment of variant classification as genetic knowledge rapidly expands. The current guidelines recommend focused cascade genetic testing regardless of age in phenotype-negative first-degree relatives if a variant with decisive evidence of pathogenicity has been identified in the proband. Genetic test results in family members guide longitudinal clinical surveillance. At present, there is emerging evidence for genetic test application in risk stratification and management but its implementation into clinical practice needs further study. Promising fields such as gene therapy and implementation of artificial intelligence in the diagnosis of HCM are emerging and paving the way for more effective screening and management, but many challenges and obstacles need to be overcome before establishing the practical implications of these new methods.

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.

Inhibiting Factors in the Application of Risk Management at Hospital X Jakarta

Background: The risk management program will run well with commitment and good management. This study aims to describe the experience of nurse managers in implementing risk management in hospitals. Research methodology: this study used a descriptive qualitative approach. The data collection technique used Focus Group Interviews with 12 nurse managers consisting of 1 Sub Installation Head, 7 Unit Heads, and 4 Team Leaders. Interviews were carried out three times, and each implementation consisted of four participants. Analysis using thematic content and consolidation for reporting qualitative research (COREQ) was used as a guide to research reporting. The research found three inhibiting factors in implementing risk management: 1) Busyness, 2) Lack of knowledge, and 3) Lack of structured monitoring and evaluation. Conclusion: The active participation of nurse managers in implementing risk management is very influential in the successful implementation of risk management in hospitals. Good time management, increasing the level of knowledge, and supported by a systematic risk management monitoring-evaluation system means that risk management will be carried out well

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.
 
 

Quantifying the probability of a successful marine bioinvasion due to source-destination risk factors.

The increasing spread of marine non-indigenous species (NIS) due to the growth in global shipping traffic is causing widespread concern for the ecological and economic impacts of marine bioinvasions. Risk management authorities need tools to identify pathways and source regions of priority concern to better target efforts for preventing NIS introduction. The probability of a successful NIS introduction is affected by the likelihood that a marine species entrained in a transport vector will survive the voyage between origin and destination locations and establish an independently reproducing population at the destination. Three important risk factors are voyage duration, range of environmental conditions encountered during transit and environmental similarity between origin and destination. In this study, we aimed for a globally comprehensive approach to assembling quantifications of source-destination risk factors from every potential origin to every potential destination. To derive estimates of voyage-related marine biosecurity risk, we used computer-simulated vessel paths between pairs of ecoprovinces in the Marine Ecoregions Of the World biogeographic classification system. We used the physical length of each path to calculate voyage duration risk and the cross-latitudinal extent of the path to calculate voyage path risk. Environmental similarity risk was based on comparing annual average sea surface temperature and salinity within each ecoprovince to those of other ecoprovinces. We derived three separate sets of risk quantifications, one each for voyage duration, voyage path and environmental similarity. Our quantifications can be applied to studies that require source-destination risk estimates. They can be used separately or combined, depending on the importance of the types of source-destination risks that might be relevant to particular scientific or risk management questions or applications.

CORRELATION OF RISK MANAGEMENT METHODS AND PROCEDURES WITH RISK MANAGEMENT AND ECONOMIC SECURITY SYSTEMS

Task. Issues devoted to the delimitation of methods and procedures of related scientific areas: risk management, economic security, management and risk management are studied and researched by many authors. This gives rise to thoughts about a single basic basis for the listed scientific movements. So, the author, in order to avoid constant repetition of listing the scientific directions under consideration in the article, calls them directions, economic categories, etc., but the problem raised by the author of the article is the differentiation of the main elements, in particular methods, procedures, goals, objectives, principles between the studied categories, namely risk management, economic security, management and risk management. The purpose of the article is to systematize the listed elements and consider their duplication in the scientific areas under consideration. Model. The author systematizes, on the basis of those reviewed by modern economists and analysts, as well as classic scientific works, the basic elements of risk management, economic security, management and risk management. An attempt was made to find an answer to the question «are there specific developed methods and techniques for risk management of an organization that would distinguish it from the risk management system and other scientific areas?» Conclusions. Economic categories: risk management, economic security, management and risk management are considered as a single process in economic research, and therefore economists do not have a clear opinion and understanding of the differences between these categories. Practical significance. A comparative analysis of the essence of the main elements of risk management, economic security, management and risk management allows us to determine the most appropriate methods and procedures for the practical application of risk management in organizations. Allows you to develop an understanding of the implementation of risk management principles in organizations to achieve their goals. Originality. The list of goals, objectives, procedures and principles of risk management, economic security, management and risk management has been clarified, expanded and systematized

Analisis Manajemen Risiko Pada Proyek Konstruksi Jalan di Provinsi Jambi

In the implementation of road construction projects, there are often various obstacles that arise due to the risks that occur which result in not achieving the expected performance, quality and achievement of work results. The purpose of this study was to determine the risk factors that affect the implementation of work in the field and to determine the application of risk management in road construction projects. In this study, risk identification, risk analysis and risk response were carried out in the construction of road construction projects, especially road construction in Jambi Province. The research method used was by way of a survey distributing risk factor questionnaires and actions on risk factors to consultants. The assessment method uses a risk matrix assessment sourced from AS/NZS 4360: 2004. From the results of this study, it is found that the risk level for road construction work in Jambi Province is very high risk (very high) of 8%, high risk (High) of 7%, and a moderate risk of (Medium) of 16%.

Harnessing the Power of AI for Enhanced Regulatory Compliance and Risk Management in Fintech

Purpose: This article analyzes how artificial intelligence (AI) is revolutionizing risk management and regulatory compliance in the fintech industry. The objective is to conduct an analysis of AI applications, highlighting how it may be used for proactive risk management, fraud prevention, real-time regulatory monitoring, and risk assessment.
 Methodology: Using a literature review methodology, the paper puts together data gathered from multiple sources to give a comprehensive knowledge of how AI is being applied to change the regulatory and risk landscape for fintech. As part of the method, significant works in the subject are reviewed and analyzed, and numerous perspectives are integrated to provide a thorough overview.
 Findings: The results highlight how AI significantly improves decision-making processes in response to complicated risk situations and dynamic regulatory contexts while also increasing efficiency and lowering costs. Fintech practices are evolving due to specific applications such as proactive risk management, precise risk assessment, fraud detection, real-time monitoring, and accurate risk management.
 Unique contribution to theory, practice and policy: The work adds additional value by combining various AI applications for risk management and regulatory compliance in finance. It provides useful insights for researchers, practitioners, and policymakers by bridging the gap between theory and practice. The article offers industry professionals useful implications in addition to educating the academic community on the complex effects of AI on fintech. It also draws attention to the necessity of flexible regulatory frameworks that can keep up with the fintech industry's rapid advancements in technology, which adds to the policy considerations in this dynamic environment. For individuals negotiating the convergence of artificial intelligence, regulatory compliance, and risk management in the fintech industry, the article is essentially a short and important resource.