Risk Management Approach Research Articles

УПРАВЛІННЯ ПОДАТКОВИМИ РИЗИКАМИ ПІДПРИЄМСТВА ЧЕРЕЗ ІНСТРУМЕНТ СТРАХУВАННЯ

The introduction of the newest tax risk management approaches has become more and more critical for Ukrainian enterprises. This paper analyzes and summarizes international experience in tax risk management and defines trends in this area. The author presents several recommendations for improving fiscal relations using an insurance coverage mechanism. The article also emphasizes the changes in tax relations determined by the development of information technology, as well as new opportunities and challenges associated with these processes. The methods used worldwide to manage these processes are analyzed in order not only to mitigate tax risks but also to implement necessary changes required to make tax function more efficient in a company. Given the complexity of tax regulations and the development of new instruments for managing tax risks, the author proposes to consider and put into practice the US experience, where, as a way of providing transparency and consistency in accounting for income tax liabilities, Interpretation No. 48 "Accounting for Uncertainty in Income Taxes" (FIN 48) was adopted in 2006 by the FASB. Practical foreign experience of insurance coverage for tax risks, uncertain tax positions, and the potential development of such insurance in Ukraine are outlined. It is noted that the essence of tax risk insurance is to cover possible financial losses incurred by a business entity as a result of changes in tax legislation or an unformed approach of court practice. It is determined that there are certain inconsistencies in the sectoral legislation regarding the absence of rules governing tax risk insurance. At present, the national legislation does not define tax risk insurance as an independent class of insurance (or its component), which reduces its effectiveness. The development of tax risk insurance in Ukraine could become an effective tool to help minimize business risks of monetary liabilities to the budget. The authors argue that there is a need for high-quality transformation in the transparency of business, accounting, financial, and tax reporting within relationships with tax authorities. In addition, it is determined how the implementation of the Standard Audit File (SAF-T) could affect the taxpayer's cooperation with the government and insurers in assessing tax risks.

OPTIMALISASI MANAJEMEN RESIKO DALAM PENGADAAN SARANA DAN PRASARANA PENDIDIKAN DI SMP INTAN AL-SALI

This study aims to analyze the optimization of risk management in efforts to procure facilities and infrastructure to improve the quality of education at Intan Al-Sani Junior High School. Risk management has become an important topic in various sectors, including in the education sector. In the context of education, risk management is concerned with identifying, evaluating, and controlling risks that can affect the achievement of educational goals. This research uses descriptive analysis methods and literature reviews to collect relevant data and information about risk management in the procurement of educational facilities and infrastructure. The results of this study show that the effective implementation of risk management can make positive optimization in the procurement of educational facilities and infrastructure. The optimization includes increasing the security of educational facilities and infrastructure, reviewing priorities in the procurement of educational facilities and infrastructure, and more effective strategic planning. However, challenges faced in implementing risk management in education also need to be overcome, such as limited resources, resistance to change and the complexity of the educational environment. This research contributes to the understanding of risk management in the procurement of educational facilities and infrastructure and provides recommendations for educational practitioners and decision-makers to improve the quality of education through an effective risk management approach.

Risk Stratification and Treatment of Obesity for Primary and Secondary Prevention of Cardiovascular Disease.

In this review, we discuss contemporary and emerging approaches for risk stratification and management of excess adiposity for the primary and secondary prevention of cardiovascular disease. Obesity is simultaneously a pandemic-scale disease and major risk factor for the incidence and progression of a wide range of cardiometabolic conditions, but risk stratification and treatment remain clinically challenging. However, sex-, race-, and ethnicity-sensitive anthropometric measures, body composition-focused imaging, and health burden-centric staging systems have emerged as important facilitators of holistic risk prediction. Further, expanding therapeutic approaches, including comprehensive lifestyle programs, anti-obesity pharmacotherapies, device/endoscopy-based interventions, metabolic surgery, and novel healthcare delivery resources offer new empowerment for cardiovascular risk reduction in individuals with obesity. Personalized risk stratification and weight management are central to reducing the lifetime prevalence and impact of cardiovascular disease. Further evidence informing long-term safety, efficacy, and cost-effectiveness of novel approaches targeting obesity are critically needed.

Earthquake Disaster Risk Reduction Strategies in Pakistan

The site of Pakistan in a region which is seismically active lends urgency to earthquake disaster risks. Seismic susceptibility implied by historical earthquake tremors continues to emphasize needs for sound disaster risk management approaches. This study seeks to critically analyze the current earthquake risk picture in Pakistan, a detailed appraisal of existing Disaster Management mechanisms and systems practice as well as implementation of policies. Using global standards, guides and practices as well as case studies, the study relative to best practice in earthquake risk reduction will be identified with a focus on application potential within Pakistan. Additionally, seismic zoning, vulnerability assessment and exposure analysis will be studied in the research to perform a comprehensive risk assessment of earthquake for the country. Through analyzing the existing challenges and gaps in the system, this paper is going to propose specialized risk reduction strategies for earthquake disaster that will integrate early warning systems implementation into building code implementing communities. The aim is as such to provide workable recommendations for the policymakers and stakeholders in order to lay down a resilient prepared response strategy towards an earthquake disaster risk reduction plan in Pakistan.

Risk Management Strategy for International Investment Projects of an Innovative Enterprise in the Context of Industry 4.0

"The significance of risk management within the framework of Industry 4.0 is on the rise, particularly within the realm of international investment projects. Contemporary technological advancements, such as artificial intelligence and blockchain, are not only ushering in fresh prospects but are also instigating the emergence of novel categories of risks. The primary objective of this investigation is to formulate an all-encompassing strategic risk management framework tailored to innovative enterprises operating within the purview of Industry 4.0. The envisaged research endeavour encompasses a thorough scrutiny of existing methodologies and models, along with an exploration of contentious dimensions within this domain. The study’s findings encompass criteria for the evaluation and quantification of risks, in addition to pivotal risk factors that exert notable influence on the efficacy of international investment projects. The pragmatic import of this investigation resides in its potential utility for enhancing managerial decisionmaking across various tiers of the organizational hierarchy. The presented material seeks to facilitate the alignment of conventional risk management approaches with the intricacies inherent in Industry 4.0. Nonetheless, it is imperative to acknowledge that the efficacy of the formulated model may hinge upon contextual nuances and might necessitate supplementary adaptations."

Research on Influencing Factors and Countermeasures of Risk Control of State-Owned Enterprises under the Background of Big Data

This study explores the risk control and response strategies of state-owned enterprises in the context of big data. Global economic uncertainty poses new challenges to state-owned enterprises, necessitating innovative risk management approaches. This article proposes response strategies from four key aspects: establishing a proactive risk management culture, building a foundation in technology and data, conducting big data-driven risk analysis, and implementing predictive analysis and real-time monitoring. State-owned enterprises can foster a proactive risk management culture by cultivating employee risk awareness, demonstrating leadership, and establishing transparency and open communication. Additionally, data integration and analysis, leveraging the latest technology, are crucial factors that can help companies better identify risks and opportunities.

EFFECT OF USING DERIVATIVES ON RISK MANAGEMENT, FINANCIAL PERFORMANCE AND FIRM VALUE

This paper examines the effects of using derivatives on risk management, financial performance, and firm value of non-financial firms for the period of 2016-2021. The paper also aims to observe the tendency of firms to use derivatives for pre-pandemic period and during pandemic period and measure the effectiveness of using derivatives in such high risky times. To this end, firstly derivatives usage intensity of firms observed and annual report deepnotes have been examined, thereafter direct impact of using derivatives analyzed through panel data analysis method. According to derivatives usage intensity observations, it is seen that the approach of firms to use derivatives hasn't changed based on the pandemic. Approximately %30 of firms stated that they use derivatives in risk management while other firms stated they use in-house methods in risk management. In addition to this, panel data analysis results show that derivatives usage decreases financial performance and increases risk significantly. On the other hand, any relationship between derivatives usage and firm value hasn't been found to be statistically significant. According to findings, it can be inferred that derivatives usage is not an effective way to control risk for the firms in the sector. Therefore, firms should revise their risk management approaches. In addition, the firms should find answers to the question why derivatives couldn’t help them to control risk.

Revolutionizing cardiovascular risk prediction: A novel image-based approach using fundus analysis and deep learning

Worldwide, CVDs continue to stand as a prominent contributor to mortality. Detecting and assessing CVD risk early is paramount for effective prevention and management. Medical imaging has gained prominence as a tool for evaluating CVD risk factors. In this study, a novel Inception v3 with a VGG16 is proposed to forecast cardiovascular risk rates via readily available and non-invasive fundus images. This approach harnesses advanced image analysis techniques encompassing contrast enhancement and noise reduction. The blood vessel segmentation and Optic disc detection of the pertinent features are extracted from the fundus images. In this context, the Inception v3 architecture is initially employed to capture intricate hierarchical patterns within the images. Alternatively, explore the utilization of the VGG16 architecture. By integrating these features with clinical data, the model is then trained to predict cardiovascular risk rates. Empirical findings underscore the method's remarkable accuracy in risk rate prediction. This non-invasive, image-based methodology holds transformative potential for reshaping early diagnosis and risk management approaches for cardiovascular diseases. Ultimately, this innovation stands to enhance patient care and outcomes.

Enhancing public sector enterprise risk management through interactive information processing.

Federal agencies are increasingly expected to adopt enterprise risk management (ERM). However, public sector adoption of ERM has typically focused on the economic efficiency of tax-financed activities based on control-based practices. This reflects an emphasis on quantifiable concerns that invariably directs attention to risk, that (by definition) relates to identifiable and measurable events, thereby downplaying uncertain and unknown aspects of public exposures. This is a potentially serious shortcoming as government entities often act as society's risk managers of last resort. When extreme events happen what were previously considered private matters can quickly turn into public obligations. Hence, there is a need for proactive assessments of the evolving public risk landscape to discern unpredictable-even unknowable-developments. The article reviews recent empirical studies on public risk management practices, effects of digitalization in public sector institutions, current strategic management research, and insights uncovered from a recent study of risk management practices in federal agencies. On this basis, the article explains how the ability to generate value from ERM can be enhanced when it intertwines with local responsive initiatives and central strategic risk analyses. It can form a dynamic adaptive risk management process where insights from dispersed actors inform updated risk analyses based on local autonomy and open exchange of information. This approach builds on specific structural features embedded in culture-driven aspirations to generate collaborative solutions. Its functional mode is an interactive control system with open discussions across levels and functions in contrast to conventional diagnostic controls that monitor predetermined key performance indicators (KPIs) and key risk indicators (KRIs). Backed by theoretical rationales and empirical research evidence, it is found that applications of ERM frameworks can produce positive results but is unable to deal with a public risk landscape characterized by uncertain unpredictable conditions with potentially extreme outcome effects. It is shown how interactive exchange of fast local insights and slow integrated strategic risk analyses supported by digitized data processing can form a dynamic adaptive system that enable public sector institutions to deal with emergent high-scale exposures. It is explained how the requirement for conducive organizational structures and supportive values require a new strategic risk leadership approach, which is contrasted to observed practices in federal agencies that are constrained by prevailing public governance requirements. The need to deal with uncertainty and unknown conditions demands a cognitive shift in current thinking from a primary focus on risk to also appraise complexity and prepare for the unexpected where data-driven methods can uncover emergent exposures through dynamic information processing. This requires strategic risk leaders that recognize the significance of complex public exposures with many unknowns and a willingness to facilitate digitalized information processing rooted in a collaborative organizational climate. If handled properly, adoption of ERM in public risk management can consider emergent dimensions in complex public exposures applying interactive information processing as a dynamic adaptive risk management approach incorporating digitized methods to solicit collective intelligence for strategic risk updating.

Digitalization enhancement in the pharmaceutical supply network using a supply chain risk management approach

One major issue in pharmaceutical supply chain management is the supply shortage, and determining the root causes of medicine shortages necessitates an in-depth investigation. The concept of risk management is proposed in this study to identify significant risk factors in the pharmaceutical supply chain. Fuzzy failure mode and effect analysis and data envelopment analysis were used to evaluate the risks of the pharmaceutical supply chain. Based on a case study on the Malaysian pharmaceutical supply chain, it reveals that the pharmacy node is the riskiest link. The unavailability of medicine due to unexpected demand, as well as the scarcity of specialty or substitute drugs, pose the most significant risk factors. These risks could be mitigated by digital technology. We propose an appropriate digital technology platform consisting of big data analytics and blockchain technologies to undertake these challenges of supply shortage. By addressing risk factors through the implementation of a digitalized supply chain, organizations can fortify their supply networks, fostering resilience and efficiency, and thereby playing a pivotal role in advancing the Pharma 4.0 era.

The effect of thiazolidinediones on body fat redistribution in adults: A systematic review and meta-analysis of randomized controlled trials.

Visceral adiposity is a strong predictor of cardiometabolic risk. Thiazolidinediones (TZDs) are associated with a shift in fat redistribution from visceral adipose tissue (VAT) to subcutaneous adipose tissue (SAT). We aimed to compare the effects of TZD and other interventions on fat remodeling in adults in randomized controlled trials. Among the 1331 retrieved studies, 39 trials with 1765 participants were included in the meta-analysis. The standardized mean difference in VAT change was not significantly different between TZD and comparators across the overall studies. Intriguingly, TZD treatment resulted in significant decreases in VAT compared with placebo and sulfonylureas (p < 0.05), although recombinant human growth hormone was superior to TZD regarding VAT reduction (p < 0.05). Data from 216 participants showed TZD leading to a greater reduction in liver fat percentage than comparators (p < 0.05). Compared with the controls, TZD significantly increased SAT, total body fat, weight, waist circumference, and body mass index (p < 0.05). However, TZD pronouncedly improved glucose control, insulin resistance, adiponectin, and lipid profile (p < 0.05). TZD provides a favorable effect on fat redistribution and benefits insulin sensitivity, suggesting a potentially valuable approach in cardiometabolic risk management.

The Current State and Challenges of Financial Risk Management

Financial risk management is a critical discipline in today's dynamic and interconnected financial landscape. This article explores various facets of financial risk management, including the challenges posed by rapid technological advancements, globalization, cybersecurity threats, regulatory changes, and economic uncertainty. It also discusses strategies for effective risk management, such as integrated risk management approaches, advanced analytics, stress testing, risk culture, and communication. Additionally, the article highlights the importance of continuous research and innovation in risk management and offers insights into future trends, including emerging technologies, regulatory reforms, and adaptation to economic and market changes. The conclusion emphasizes the need for proactive, adaptive risk management practices to navigate the complexities of the financial risk landscape successfully.

Implications of disclosure and non-disclosure of flood hazard maps – a synthesis for the Canadian context

Flood is an increasingly costly and impactful hazard in Canada. Risk management approaches need to be applied to stem rising costs and impacts of floods. The foundational tool that supports many risk management strategies is the development of flood mapping products. In Canada, however, there is only a patchwork of flood mapping available, and there is further variability in the accessibility of this information to private and public sectors. This article draws on published studies to synthesize the potential benefits and disbenefits of making flood maps more available and accessible in the Canadian context, with a focus on real-estate transactions, but also with consideration of implications to land use planning, flood insurance uptake, and social equity impacts. The review highlights that accessibility and regulated disclosure of flood maps reduce property values marginally, but not to the full discount that should be applied if flood risks were fully accounted for or realized. There are also substantial benefits of making flood mapping products more accessible, including greater social equity, by removing the challenge of data asymmetry (where some buyers and sellers have better information than others), better emergency preparedness, and increased insurance uptake to manage residual risks.

The Role of Risk Management in Competitiveness at PT Bank Central Asia TBK with an ERM (Enterprise Risk Management) Approach using Likelihood Table

The key to BCA's success has survived to this day. Because BCA is able to seize opportunities for bank development that arise from time to time and turn them into competitive advantages. The purpose of this study is to determine the role of risk management in competitiveness at PT Bank Central Asia Tbk. with an ERM (Enterprise Risk Management) approach using a likelihood table so that it can easily handle risks that occur in the future, and the integrity of BCA can be maintained consistently in banking world’s the research method is a survey through structured interviews with questionnaires. Observation data collection techniques, interviews and document studies. The sample in this study is the entire population of 26 people. Data analysis techniques with Fishbone and heatmap. The results showed that there were 21 KTD risk categories with a risk score of 5.03 to 10.92. With the implementation of Operational risk management, BCA has become stronger at the level of competition in the banking world.

Environmental Risk Assessment of Hydrocarbon-Rich Sludge of a Gas Refinery Using the Integrated Approach of PMBOK Standard Risk Management and FMEA Technique: A Case Study

Background: The present study aimed to assess the potential hazards of hydrocarbon-rich gas refinery sludge from the South Pars Gas Complex-First Refinery, Iran, and to analyze how these risks could impact the project goals based on the Project Management Body of Knowledge (PMBOK) guidelines. Methods: Statistical population of this analytical, cross-sectional survey was 31 health, safety, and environment (HSE) experts from the study refinery selected by purposeful sampling. Environmental, health, safety, and technical risks were categorized and identified using the PMBOK guidelines. Delphi method and 5-point Likert scale questionnaire were employed to assess the significance of each risk. Priority one risks within each category were determined using Primavera Risk Analysis version 8.7 software. Results: Following three Delphi steps, 17 out of 77 primary risks were identified and finalized. The first rank with the highest weight corresponded to time and cost group (the risk of soil contamination via sludge stored in the burn pit due to aliphatic and aromatic hydrocarbons), human resources group (radioactive materials accumulated on the inner surfaces of pipes, valves, pumps, heat exchangers, tanks, boilers and other equipment and the risk of these materials being hazardous for human health), and quality group (failure to supply equipment and quality control systems and personal protective equipment). Conclusion: The research findings pinpointed 17 risks within various domains, encompassing time and cost, human resources, quality, contract, scope, and communication. These identified risks necessitate a comprehensive, integrated approach to risk management for effective mitigation and resolution.

Performance evaluation of technical indicators for forecasting the moroccan stock index using deep learning

&lt;span&gt;Navigating the complex terrain of financial markets requires accurate forecasting tools, underscoring the need for effective forecasting methods to assist investors and policymakers alike. This paper explores deep learning techniques for forecasting the Moroccan all shares index (MASI), a prominent indicator of the Moroccan stock market. The study aims to evaluate the performance of technical indicators in enhancing the accuracy of MASI predictions. A comprehensive dataset of daily closing prices of the MASI index is collected and 26 technical indicators are computed from the historical price data. Deep learning models based on artificial neural networks (ANNs) are trained and optimized using the dataset. The performance of the models is evaluated using standard metrics such as mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), and root mean square error (RMSE). Additionally, feature selection techniques are employed to identify the subset of technical indicators that contribute most significantly to the prediction accuracy. The findings provide insights into the effectiveness of deep learning models and the impact of technical indicators on MASI prediction accuracy. This research has important implications for investors, financial analysts, and policymakers, enhancing investment strategies and risk management approaches.&lt;/span&gt;

Are Artificial Intelligence and Machine Learning Shaping a New Risk Management Approach?

Digital revolution is influencing many economic sectors and for a few years banking sector is under a great transformation mainly due to the development and the use of new technologies. The most recent ones are artificial intelligence (AI) with the recourse to advanced algorithms. The main banking services, their offer, but above all, the customer relations have been significantly influenced by the this. The recourse to new channels, the monitoring of risks and the controls of frauds are only some of the applications of machine learning (ML). To manage the increase in financial and non-financial risks AI and ML seem to give a great help to banks. The survey conducted from December 2022 to May 2023 with a sample of Italian banks of different size, shows the level of awareness in the recourse to these technologies. Moreover, it aims to assess the maturity and the future perspectives in the adoption of AI in the financial system. The analysis is divided into different investigation areas that show how banks can mitigate the risks involved with the implementation of AI and how it affects the risk management process. The paper covers the gap in literature where AI and ML are mainly considered as separate tools to face specific banking projects; and Italian banks, even if with differences due to the size, are aware of the relevance of these new technologies. The research is a contribute to the discussion about the application of AI and ML in a holistic dimension.

A Study on Technical Analaysis of Indian FMCG Sector

The Indian fast-moving consumer goods (FMCG) sector plays a crucial role in the country's economy, experiencing strong growth and catering to the needs of its vast population. This research aims to conduct a close examination of the Indian FMCG sector using technical analysis techniques in order to identify patterns, trends, and potential investment opportunities. Using a quantitative approach, this study employs various technical indicators and chart patterns to assess the price and volume movements of FMCG sector stocks listed on Indian stock exchanges. Historical price data and trading volumes are collected and analyzed to identify recurring patterns and trends that can assist investors in making well-informed decisions. The study covers a time period between 26/04/2021 to 02/06/2021, allowing for a comprehensive evaluation of the FMCG sector's performance and behaviour. Few technical analysis tools such as moving averages convergence and divergence (MACD), relative strength index (RSI), on balance volume (OBV), and trendlines are utilized. By applying these techniques, the study aims to identify potential signals for buying and selling, levels of support and resistance, and price targets within the Indian FMCG sector. Additionally, the research explores the impact of market trends, economic factors, and industry-specific events on the sector's performance. The findings of this study contribute to the existing knowledge in technical analysis and provide valuable insights for investors and traders interested in the Indian FMCG sector. The results enhance understanding of the sector's price movements, volatility, and potential investment opportunities, enabling market participants to develop effective trading strategies and risk management approaches. It is important to note that this study solely focuses on the technical analysis of the Indian FMCG sector and does not consider fundamental analysis or qualitative factors that may influence investment decisions. Therefore, readers are encouraged to combine the findings of this study with additional research and analysis before making investment choices.

Integrated Attack Tree in Residual Risk Management Framework

Safety-critical cyber-physical systems (CPSs), such as high-tech cars having cyber capabilities, are highly interconnected. Automotive manufacturers are concerned about cyber attacks on vehicles that can lead to catastrophic consequences. There is a need for a new risk management approach to address and investigate cybersecurity risks. Risk management in the automotive domain is challenging due to technological improvements and advances every year. The current standard for automotive security is ISO/SAE 21434, which discusses a framework that includes threats, associated risks, and risk treatment options such as risk reduction by applying appropriate defences. This paper presents a residual cybersecurity risk management framework aligned with the framework presented in ISO/SAE 21434. A methodology is proposed to develop an integrated attack tree that considers multiple sub-systems within the CPS. Integrating attack trees in this way will help the analyst to take a broad perspective of system security. Our previous approach utilises a flow graph to calculate the residual risk to a system before and after applying defences. This paper is an extension of our initial work. It defines the steps for applying the proposed framework and using adaptive cruise control (ACC) and adaptive light control (ALC) to illustrate the applicability of our work. This work is evaluated by comparing it with the requirements of the risk management framework discussed in the literature. Currently, our methodology satisfies more than 75% of their requirements.

A Cyber-Physical All-Hazard Risk Management Approach: The Case of the Wastewater Treatment Plant of Copenhagen

The ongoing digitalization of critical infrastructures enables more efficient processes, but also comes with new challenges related to potential cyber-physical attacks or incidents. To manage their associated risk, a precise and systematic framework should be adopted. This paper describes a general methodology that is consistent with the Risk Management ISO (31000-2018) and builds on specific tools developed within the H2020 digital-water.city (DWC) project. The approach has been demonstrated for a digital solution of the DWC project that allows to visualize inflow predictions for the Wastewater Treatment Plant (WWTP) in the city of Copenhagen. Specifically, the risk assessment and risk treatment steps are demonstrated in the case of the spoofing of the web interface where misleading forecast data may turn into fallacious maintenance schedules for the operators. The adopted methodology applied to the selected use case led to the identification of convenient measures for risk mitigation.