Efficient Risk Management Research Articles

Role of Big Data – Shaping the Future of Project Management

Big data is reshaping the future of the project management by ushering in new era of datadriven decision-making processes and improving efficiency in the organization. It is throughthe transformative force of big data that it has revolutionized the way in which projects areconceived and executed in different sectors. Project managers can rely on big data in integratingreal-time data into the project management’s operations and gain historical insights on the trendand successes of the projects.In this article, the aim is to assess the role of big data in shaping the future of the projectmanagement initiatives. Key concepts are discussed including the role of big data in planningand delivery, and this involves identifying the possible bottlenecks affecting the success of theproject. It outlines the significance of big data in the project team environment, with the bigdata encouraging collaboration among the project team improving their productivity level andefficiency in conducting their activities. It is also integral in knowledge management withproject managers relying on big data analytics in understanding the market trends and analyzinghistorical data. Other issues discussed relevant to the future of project management is efficientrisk management and quality management, which are critical aspects in improving success ofthe projects and reducing cost of operations. As such, the future of project management is bigdata analytics.

CUSTOMS FRAUD DETECTION USING EXTREMELY BOOSTED NEURAL NETWORK (XBNET)

Noticing the vital role of cross-border trade has made Customs plays a crucial role not only in maintaining supply chain but also in securing government revenue from intentional fraud. Given the huge volume of international trade and limited workforce, Customs across the world must implement efficient and effective risk management. This paper proposes XBNet, an ensemble of tree-based algorithms with deep learning algorithms, to detect fraud in import activity. The strength of XBNet is combining gradient-boosted trees with neural networks where the weights, bias, and loss are adjusted simultaneously with the importance features of each tree in each layer. The object of this study is Import Declaration data from four Customs Offices, and the model is set for each Customs office to capture fraud patterns related to their region. We compared the model with two different parameters and concluded the models with learning rates = 1%, number of hidden layers = 2, activation function = sigmoid, and number of epochs = 100 as the most suitable for the Belawan, Merak, and Makassar and model with number of hidden layers = 2, number of epochs = 50 and other parameters are set the same as the most suitable for Tanjung Emas.

Enhancing Organizational Performance in Bangladeshi Industries: The Role of Enterprise Resource Planning (ERP) Systems

In today’s dynamic business environment, organizations strive to enhance their operational efficiency and customer satisfaction to maintain their competitive edge. Therefore, the proper implementation of supply chain management, effective inventory management, and efficient financial risk management are key factors in improving a company's efficiency and customer satisfaction. To achieve these goals, companies must effectively utilize Enterprise Resource Planning (ERP) systems, which enable seamless integration across supply chain, production, and administrative processes. The aim of this research is to investigate the effect of ERP systems on organizational performance in Bangladeshi industries. A comprehensive literature review sets the foundation by analyzing existing research on ERP systems and organizational performance metrics. A survey questionnaire was sent to different types of industries in Bangladesh, and data were collected. Descriptive analysis and hypothesis testing were conducted to analyze the survey data. The hypothesis was tested using the z-test, revealing significant findings regarding the impact of ERP systems on various organizational aspects, such as inventory management, production planning, operational efficiency, order management, financial risk management, and customer satisfaction. This research makes a significant contribution to the academic literature on technology adoption, especially as it relates to the use of ERP in Bangladeshi enterprises. It will support further research on the topic.

Financial Engineering in Complex Dynamic Systems

This paper explores the dynamic nature of financial markets through the lens of complex adaptive systems (CAS) theory, aiming to provide a comprehensive understanding of how financial markets deviate from the Efficient Market Hypothesis in extreme events such as bubbles and crashes. Traditional economic models often struggle to capture the intricate dynamics of 'self-organizing' financial markets, particularly the interaction between supply and demand in the face of evolving risks. CAS theory offers a promising framework for modeling asset prices, emphasizing the interconnectedness and adaptability of various agents within the system. The literature review highlights the significance of CAS theory in understanding the collective adaptation that emerges from interactions among heterogeneous agents. Notably, researchers such as Holland (1995) and Axelrod (1997) have demonstrated how simple agent-level rules can lead to sophisticated, self-organizing behaviors at the system level, resulting in more efficient outcomes. This paper also discusses the pivotal role of financial engineering in enhancing the adaptive capacity of socioeconomic systems under extreme stress. In an increasingly unpredictable world characterized by natural disasters, economic crises, and other unforeseen events, risk management serves as a vital mechanism for volatility mitigation and financial protection. By spreading risk collectively through hedging strategies, financial engineering not only provides portfolio security but also contributes to the resilience of financial and economic systems. By merging insights from CAS theory and the role of financial engineering in increasing adaptive capacity, this paper contributes to a more comprehensive understanding of the risk dynamics in financial markets impacting economic activities. Financial engineering tools mitigate negative shocks and reduce the severity of recessionary cycles. An attempt is made to explain how collective adaptation can lead to more efficient risk management and pricing, ultimately helping policymakers, fund managers, and researchers navigate the complexities of modern financial markets and fortify socioeconomic systems against extreme stressors.

Do Human Capital, Economic Growth, and Trade Openness Instigate Entrepreneurial Activities? The Moderating Role of Financial Development

This research dives into the complex interplay of human capital, economic growth, trade openness, and entrepreneurial activities, while also investigating the moderating influence of financial development. The study uses two steps generalized method of movement (GMM) and the random effect estimation technique by using the data from 2011 to 2019. The results indicate that human capital has a significant and positive impact on entrepreneurial activities through secondary and tertiary education. Also, financial development has a strong moderating effect on the relationship between human capital and entrepreneurial activities especially at secondary and tertiary levels of education. Economic growth and trade openness also instigate entrepreneurial activities in primary, secondary, and tertiary education. Our empirical findings suggest that financial development benefits entrepreneurial activities by satisfying entrepreneurs' demand for accessible, cheap, and extensive credit, as well as investors' demand for efficient and cost-effective risk and information management. This research provides policymakers, researchers, and academia with valuable information for fostering an environment conducive to entrepreneurial activities.

The impact of urbanization on urban flood risk of Nakhon Ratchasima, Thailand

Urban flooding is a hazardous consequence of urbanization, climate change, and spatial factors. It occurs when people gather in specific areas, disrupting natural and built environments. Despite its importance, the manner in which urbanization transforms land use patterns and overall land topography, contributing to the uncertainty of urban flooding, remains unclear. This study presents a comprehensive spatiotemporal assessment of the impact of urbanization on urban flood risk based on the Urban Flood Risk Triangle Index (UFRTI) of Nakhon Ratchasima, Thailand, in 2000, 2010, and 2020. The study developed this assessment framework through spatial analysis using Bivariate Local Indicators of Spatial Association and K-means clustering analysis. The revised parameters for urban flood hazard, exposure, and vulnerability constitute the UFRTI, illustrating how urbanization influences the degree and characteristics of urban flood risk. This study highlights the potential of using BiLISA and K-means clustering in GIS-based spatial analysis that reveals flood risk characteristics are shifting towards the city's periphery, requiring land use regulations and efficient flood risk management to confront urban growth.

How do Fintech and green bonds ensure clean energy production in China? Dynamics of green investment risk.

This study inquiry examines the vital role that Fintech and green bonds play in ensuring that China generates clean energy in a comprehensive manner, taking into consideration the mechanisms of green investment risk. This paper provides exciting new insights on the link between financial technology (Fintech), green bonds, and China's clean energy economy by using a reliable approach and doing an in-depth data analysis. The findings provide a significant contribution to our understanding of how Fintech may help enable financing for renewable energy projects by using digital platforms, mobile payment technologies, and blockchain technology to boost investor confidence and accessibility to funds for clean energy projects. This aspect of green bonds is emphasized since the study highlights the importance of sustainable energy generation. The need of proactive regulatory frameworks that are able to adjust to the shifting Fintech and green bond markets while supporting good policies, motivating factors, and efficient risk management procedures is emphasized in the research. The study contributes to the enlightenment of academic discourse, focuses efforts at policymaking, and gives knowledge that is helpful to people who are working to promote a greener and more sustainable future.

Credit Risk Management on Financial Performance of Selected Microfinance Institutions

The purpose of this study was to look at the impact of credit risk management on the financial performance of a few Kenyan microfinance firms. The study's approach was a descriptive survey research design and a panel data analysis technique. The study comprised credit managers from all 52 Kenyan microfinance institutions registered in the Association of Microfinance Institutions in Kenya (AMFI) database. The study included all of the institutions that were targeted. The questionnaire, which had previously been tested on local microfinance banks in Kakamega County, was used to collect data. Data analysis included regression analysis and correlation. Throughout the data collection process, the researcher observed integrity. Tables were used to present the study's findings. According to the model summary, credit risk management accounts for 49.1% of the variance in the financial performance of Kenyan MFIs, while other factors not included in the study model account for the remaining 50.9%. With a p-value of 0.01 that is statistically significant. Multiple linear regression analysis revealed that a one-unit change in credit risk management resulted in a significant improvement of 0.672 units in microfinance institution performance (= 0.672 (0.087); at p.01). The study found that prudent and effective credit risk management boosts net profit margins, return on capital invested, and cash flow. The study adds to existing theories by emphasizing the importance of credit risk management in microfinance, lays the groundwork for future research, and advises Kenyan microfinance organizations to invest in efficient credit risk management to improve their financial performance. The report also suggests that studies on Savings and Credit Cooperative Societies (SACCOs) be conducted to compare study findings and that the Association of Microfinance Institutions do studies on non-registered microfinance across the country.

WPFSAD: Wind Power Forecasting System Integrating Dual-Stage Attention and Deep Learning

Wind power forecasting with high accuracy and stability concretely contributes to efficient scheduling and risk management of wind power systems. However, current studies remain limited due to these shortcomings: (1) overemphasizing model combination strategy and data preprocessing, thus ignoring the improvement of model's internal computing mechanism; (2) much attention was paid to the single-stage attention mechanism, but little is known about dual-stage attention (DSA) mechanism. In this study, we propose a wind power forecasting system (WPFSAD), especially attaching importance to the improvement of forecasting precision and stability using DSA-based mechanism. Specifically, the features of the input and hidden layers are adaptively and dynamically extracted in-depth. Wind power datasets from three wind farms are used to carry out simulation experiments to demonstrate the effectiveness of the WPFSAD. The study findings confirm the total supremacy of the WPFSAD in forecasting precision & stability, convergence, and robustness over its benchmarks.

Multi-criteria decision-making model for risk management in modular construction projects

The modular sector needs a precise guide to determine the most efficient risk management approaches. The main research objective of this study is to develop a multi-criteria decision-making model to find the most efficient risk management approach according to the relevant risk criteria. The risk criteria and risk management approaches for modular construction projects were also identified and classified within this scope. A systematic literature review, semi-structured interviews, and open-ended questionnaires were performed for identification and classification purposes. For ranking and quantifying the identified risks and risk approaches, as well as developing the decision-making model, the Delphi method and the Analytical Hierarchy Process (AHP) were conducted. A two-round Delphi method, with eleven experts, was conducted to achieve efficient performance scores of the identified risk management approaches. The percentage standard deviation decreased, Relative Importance Index (RII), Cronbach’s alpha, and Kendall’s coefficient of concordance (Kendall’s W) were calculated to ensure the outputs’ reliability, validity, and agreement level. The AHP method opted to quantify the Delphi method outputs, solve the multi-criteria decision-making process, and develop the multi-criteria decision-making model for risk management of modular construction projects. Triangulation results show that the critical risk categories are supply chain, health and safety, stakeholders, and governmental support. Lean principles such as the Last Planner System, Value Stream Mapping, Just in Time, and Kaizen are top-rated risk management approaches. This research’s novelty is identifying and analyzing crucial risk categories, providing the relevant risk management approaches ranked according to efficiency performance, and presenting a decision-making model as a guideline for risk management of modular construction projects.

Risk Management in Clinical Trials: Assessment of Current Practices at Portuguese Clinical Trial Sites.

Over the last years, in response to the increasing complexity and demand of clinical trials, there has been a growing concern with the application of efficient risk management methodologies. The main objective of this work is to assess the current level of implementation of risk management activities by clinical trials sites' teams and identify points of improvement. A cross-sectional study was conducted through an original, non-validated questionnaire created to assess the risk management practices at Portuguese clinical trial sites. The web-based survey was sent by e-mail to the clinical trial sites identified, and it was available for one month. Descriptive statistics were used to summarize the findings. In total, 46 clinical trial sites accepted to participate in this study. The surveys' answers showed that although 57.0% of sites reported the use of a systematic risk management tool, only nine sites (19.6%) described a standard tool or document that captured systematically the analysis of risks at the site level. Most of the sites (87.0%) showed willing to use a risk management tool specifically tailored for their operational needs, with the lack of knowledge about risk management being the main reason against its implementation. This work indicates that the surveyed clinical trial sites generally recognize the importance of risk management methodologies as an opportunity to anticipate difficulties in the trial conduct and optimize the use of sites' resources. However, mainly due to lack of experience with risk management methodologies, sites are not currently implementing these strategies in the management of their trial-related operations. The development of a risk management tool for sites can be useful in this context.

What we learn from nexus between greener growth andenergy‐relatedemissions: Sustainability perspective evidence in context of financial globalization

With gradually increasing global climate issues, the Energy Protection Agency (EPA) emphasizes emission reduction measures at the national and international levels. Financial globalization and green innovation are considered a solution to the low‐income problem through internalization and specialization of markets and bring technological progress and innovation through open market access. The developed nations have been using financial globalization as the main driver of economic progress. This study examines the effects of financial globalization and green innovation on energy‐related greenhouse gas emissions for G7 countries. Unlike the literature that considered carbon and overall greenhouse gas emissions, this study is novel as it specifically focuses on energy‐related emissions in developed economies. The authors use the panel data of G7 economies from 1990 to 2020. To estimate the individual effects of each explanatory variable on emissions despite the nonlinear data distribution, this study uses the novel method of moment quantile regression (MMQR) approach. The results suggested that financial globalization, income, eco‐innovation, financial risk and human capital significantly influence greenhouse gas emissions in G7 countries, where economic growth and human capital are the leading drivers of energy‐related emissions. However, green technology, financial globalization and financial risk significantly help improve the environmental quality of the region. The results suggested fruitful implications, including increased participation in global economic activities, investment in green innovation, human capital and efficient financial risk management to attain sustainable development.

TRIAD method to assess ecological risks of contaminated soils in abandoned mine sites

Site-specific soil ecological risk assessment is important for protecting soil ecosystems because it reflects the environmental factors at the site to detect ecological risks and develop risk management measures. This study assessed the ecological risks from chemical pollutants in abandoned mine sites using the TRIAD approach, evaluating its overall applicability, including the tiered system of assessment. A site-specific soil ecological risk assessment was conducted for five abandoned mine sites (Sites 1–4 and R, the reference site); integrated risks (IRs) for each site were calculated. Our results of the Tier 2 assessment showed that IRs at Sites 1–4 were 0.701, 0.758, 0.840, and 0.429, respectively. The IR classification was moderate, high, high, and low risk, in that order for Sites 1–4, the same as that for Tier 1. The IR had more varied analyses, emphasizing the significance of conducting higher tiered analyses under TRIAD while maintaining a balance between soil ecosystem protection and socioeconomic costs. Multiple analyses reduced the uncertainty of IR, thus enabling efficient risk management decision-making to protect soil ecosystems. Our study provides a basis for using the TRIAD for soil assessment and establishing policies for site-specific soil ecological risk assessments.

Integrating flood risk assessment and management based on HV-SS model: A case study of the Pearl River Delta, China

Global climate change has resulted in frequent extreme weather events in recent decades. The Pearl River Delta, one of the most developed regions in China, frequently faces the threat of flood disasters, causing significant human and economic losses. However, previous flood risk studies ignored the energy flow, material transfer and information transfer in the region, resulting in a disconnect between risk assessment results and management strategies, making it difficult to achieve the desired disaster prevention and mitigation effects. Thus, the HV-SS model was proposed with the aim of integrating flood assessment and management, thereby achieving more accurate and efficient risk management. The results showed that the majority of the study areas were under medium to high flood hazard levels, and their spatial distribution was closely related to natural conditions. The proportion of risk area was inversely proportional to the risk level, and the spatial distribution of flood risk exhibited a central-high and perimeter-low characteristic, with medium-risk surrounding the high-risk areas. Comparative verification, combined with the TOPSIS method achieved a matching rate of 89.89%, indicating that the assessment result was reasonable. The flood risk pattern constructed using the source-sink theory found that the primary risk sources were located in the central region and continuously transferred risk to surrounding areas through risk corridors. It is crucial to note that easily ignored risks were hidden around the risk nodes, and some reasonable flood management strategies were proposed. This study can provide a reference for regional flood risk assessment and management for sustainable development.

Dynamic risk investigation of urban natural gas pipeline accidents using Stochastic Petri net approach

The safety of urban gas pipelines is challenged by a series of adverse factors, and the unexpected accidents may pose a catastrophic threat to humans, the environment and assets. The existing studies mainly focused on causation investigation of natural gas pipeline accident, the dynamic evolution process of urban natural gas pipeline accident is still challenging task for accident prevention. To find out the unfavorable factors that cause accidents, this study presents a dynamic risk modeling of urban natural gas pipeline accidents using Stochastic Petri net (SPN). An SPN model of an accident evolution process is constructed based on the discrete events in an accident flowchart, and the critical places and transitions are evaluated through this model. Considering the slow development of the early events leading to accidents, the delay time of the transitions at this stage cannot be determined, Bayesian theory is used to dynamically update SPN model. Critical accident nodes and their occurrence probabilities are estimated, which are used to support the efficient risk management strategies. The gas explosion accident in Shiyan, Hubei, as a representative case is investigated, and the results show that pipeline corrosion, ignition sources, inefficient information feedback and unreasonable solutions are critical accident causations. The probabilities of critical accident nodes increase over time, which means that these factors need to be managed efficiently to prevent the accidents. The application shows that the model can be used as a tool for gas companies and governments to investigate urban gas pipeline accidents.

نموذج مقترح لإدارة المخاطر في الشركات ذات المشروعات المتعددة

Recently, the importance of risk management has increased in the field of project management, especially software projects. The importance of risk management is because it has direct effects on the success of the project. Therefore, software organizations face a major challenge in achieving effective and efficient risk management. Therefore, measuring risks in software development is a key activity to be undertaken in risk management. Risk measurement is also one of the important topics that need more effort, especially in a work environment with multiple software projects. A multi-project environment involves multiple projects sharing the same available resources. This research aims to conduct a literature review to discover patterns or trends in software development to measure project risks and to reveal aspects of the measurement process, risk indicators, and various metrics for software projects. This research also aims to present a proposed framework for assessing risks in a multi-project work environment with shared resources for different levels in organizations with different capabilities.

Digital Twin Smart Cities for Disaster Risk Management: A Review of Evolving Concepts

Natural hazard-induced disasters have caused catastrophic damage and loss to buildings, infrastructure, and the affected communities as a whole during the recent decades and their impact is expected to further escalate in the future. Thus, there is a huge demand for disaster risk management using digitalisation as a key enabler for effective and efficient disaster risk management systems. It is widely accepted that digital and intelligence technologies can help solve key aspects of disaster risk management such as disaster prevention and mitigation, and rescue and recovery. Digital Twin (DT) is one of the most promising technologies for multi-stage management which offers significant potential to advance disaster resilience. Smart Cities (SCs) use pervasive information and communications technology to monitor activities in the city. With increasingly large applications of DTs combined with big data generated from sensors in a SC, it is now possible to create Digital Twin Smart Cities (DTSCs). Despite the increasing prevalence of DTSC technologies and their profound impact on disaster risk management, a systematic and longitudinal view of the evolution to the current status of DTSC for disaster risk management does not exist. This review analyses 312 titles and abstracts and 72 full papers. To begin with, a scientific review of DT and SC is undertaken, where the evolution of DTSCs is reviewed. In addition, the intelligence technologies used in DTSCs for disaster risk management are assessed and their benefits are evaluated. Furthermore, the evolution and technical feasibility of DTSC-driven disaster risk management is evaluated by assessing current applications of DTSCs in disaster risk management. It was found that despite the significant potential benefits offered by DTSCs, they also add a new layer of complexities and challenges inherent to these technologies to the already complex web of complexities involved in disaster risk management. These challenges can be addressed by understanding how the process of utilising DTSCs in disaster risk reduction and sustainability is designed, which is essential for comprehending what DTSCs may offer, how it is implemented, and what it means to all involved stakeholders. This paper contributes to the knowledge by improving the understanding of the current status of DTSC technologies and their impact on disaster risk management, and articulating the challenges in implementing DTSC, which inspires the professional community to advance these technologies to address them in future research.

Risk Assessment Analysis for the Merger of Online Platform Companies

The risks connected to the merger of Company X and Y, a well-known online platform company, are examined in this research, along with suggestions for efficient risk management tactics. The study identifies potential risks like revenue loss, increased debt, compliance with regulations, conflicts between corporate cultures, and data privacy issues. The research suggests strategies to reduce the identified risks and guarantee a successful merger process after thoroughly analyzing these risks. The recommendations cover the creation of strong risk management strategies, efficient coordination and communication between the merging businesses, retention of key personnel, implementation of data privacy measures, proactive risk management of legal and regulatory risks, market monitoring, and ongoing evaluation and updates of risk management plans. By putting these suggestions into practice, Company X and Y can successfully manage risks, reduce potential negative effects, and position itself for long-term success in the phase following the merger. This research makes a contribution to the field of risk management in the context of corporate mergers by offering insightful information for businesses engaged in mergers and acquisitions.

BUSINESS MODEL CANVAS: ENTERPRISE RISK MANAGEMENT ONLINE APPLICATION INNOVATION

Risk management is one of the important elements in running a business because the development of the business world and the increasing complexity of company activities result in an increase in the level of risk faced by the company. innovation to answer the constraints in handling business risks. ERM Online application is an innovative online Enterprise Risk Management (ERM) application that offers solutions for companies or entrepreneurs in terms of efficient risk management. This application can be used in real time, can be accessed easily, and facilitates decision making so that it is more effective and provides risk list services by paying attention to priorities that can be accessed via gadgets. The purpose of this study is a mapping of business model development planning using Business Model Canvas (BMC) and analyzing alternative business development strategies. The analytical tools used are Business Model Canvas (BMC) and SWOT analysis. Based on the research, several strategies that need to be developed are in the key partners element, namely marketing and insurance organizations, key activities, namely research and development, key resources, namely risk management human resources, value preposition, namely for the management of well-documented risk management, customer relationships, namely communication via whatsapp and social media, channels, namely social media and customer testimonials that have used ERM, and in customer segments, namely manufacturing companies, banks, telecommunications companies, state-owned companies, and retail companies.

Unleashing the algorithmic frontier: Navigating the impact of algo trading on investor portfolios

In the ever-evolving landscape of financial markets, algo trading has emerged as a powerful force, revolutionizing the way investors execute trades. With the speed and precision of computer algorithms, algo trading offers advantages such as faster trade execution, reduced errors, and access to complex trading strategies. However, its impact on investors is not without its challenges and risks. This case explores the profound influence of algo trading on investors and their portfolios. It delves into the opportunities and pitfalls associated with this technological advancement, shedding light on its implications for market liquidity, volatility, and investor behavior. It explores the potential for improved portfolio diversification and efficient risk management, while also addressing concerns about market manipulation and unfair advantages. Furthermore, the case study investigates the regulatory environment surrounding algo trading, highlighting the measures implemented to ensure fair and transparent markets. By presenting a nuanced understanding of the impact of algo trading on investor portfolios, this case study equips readers with the knowledge and insights necessary to navigate the algorithmic frontier. It encourages critical thinking and strategic decision-making, empowering investors to harness the potential of algo trading while managing its inherent risks.