Role In Risk Management Research Articles

Navigating Uncertainty: Assessing the Impact of Financial Risk Management on Organizational Performance: Evidence from Insurance Companies in Pakistan

In an era marked by economic volatility and global uncertainties, the effectiveness of financial risk management strategies is crucial for businesses, particularly in the financial services industry. This research investigates the relationship between financial risk management (FRM) practices and organizational performance, specifically within the insurance sector in Pakistan. Moreover, the study aims to explore the role of financial risk management in enhancing the performance of insurance companies operating in the Pakistani market. It establishes a research model based on Financial Risk Management (Market Risk, Operational Risk, Liquidity Risk, and Legal Risk) and Organizational Performance (ROE). Researchers surveyed 13 insurance companies in Pakistan using non-probability and convenience sampling techniques and analyzed the data using EViews. The results indicate a significant and meaningful link between organizational success and financial risk management. This study seeks to incorporate best practices and focuses on developing a comprehensive understanding of the influence of organizational performance and financial risk management strategies.

Success and Risks Related to Housing Projects in Pakistan: A Moderating Role of Project Management Risk Management

This study investigates the critical success factors (CSFs) and their interplay with project risks in determining the success of housing projects in Karachi, Pakistan, while considering the moderating role of risk management. Analyzing data from 432 project management professionals within Karachi-based housing societies, the study establishes significant relationships between CSFs and various project success dimensions, including time, cost, quality, project profile objectives, impact, stakeholder satisfaction, and preparation for the future. Additionally, the research reveals that project risks exert substantial influence on these success dimensions. Risk management is a strong moderator, increasing risk variable’s influence on project results. The findings provide insightful information for decision-makers, recommending the creation of sector-specific rules that place a strong emphasis on the highest standards in risk management and project execution. The construction industry benefits from this since it encourages long-term development and leads to better results for projects.

Financial Risk Management of FK Coal Group: Basis for Policy and Strategy Formulation

This study delves into the financial risk management of FK Coal Group in China, focusing on aspects such as credit risk, liquidity risk, stability risk, and profitability risk. The research aims to provide a comprehensive profile of FK Coal Group, including its organizational structure, years in operation, number of employees, geographical scope, and asset size as of 2022. The financial risks are primarily associated with debt payments, asset management, the company's capacity to meet expenses, and production costs and outputs. The financial risk management process encompasses risk identification, analysis, evaluation, and treatment. Challenges are encountered in each phase, such as uncertain adverse events, risk assessment accuracy, risk mitigation strategies, and risk reduction and avoidance measures. The survey results from the employees of FK Coal Group reveal that it is a state-owned enterprise with a long operational history, a substantial workforce, and strong assets, primarily operating in the domestic and international markets. The study underscores the critical role of financial risk management in ensuring the company's operational continuity and competitiveness. Effective risk planning and monitoring are highlighted as crucial elements to improve profitability and prevent excessive spending or losses. The proposed financial risk management policy or strategy aims to address these challenges comprehensively, focusing on proactive risk identification, thorough risk analysis, robust risk evaluation, and appropriate risk treatment strategies to minimize adverse impacts on FK Coal Group's financial health and performance.

Navigating global markets: The role of enterprise risk management and human resource management in SME international expansions

Research background: Since SMEs lack the financial and human resources required for internationalization, they face more obstacles than large firms. In this regard, their internal/controllable risk management capabilities based on the Resource-based View (RBV) theory might help them overcome internationalization barriers. Purpose of the article: This study aims to investigate the positive impact of internal/controllable risk management capabilities, such as strategic, operational, and personnel risk capabilities, on the export intention of SMEs. Moreover, this paper finds out whether the impacts of these risk management capabilities on export intention differ depending on the countries where SMEs operate. Methods: This research uses a random sampling method and shares an online questionnaire with survey respondents. It includes research samples from Czech, Slovakian, and Hungarian SMEs. The researchers analyze the effects of enterprise risk management capabilities on export by performing Binary Logistic Regression analyses. Findings & value added: While personnel risk management does not affect the export intention of SMEs, strategic risk management and operational risk management positively affect the export intention of SMEs, depending on the countries where SMEs are located. For this reason, there are international differences in the impacts of strategic and operational risk management capabilities on the export intention of SMEs. This paper provides a more holistic approach to managing internal/controllable risk factors and investigates this construct’s effect on internationalization. A country comparison in this specific effect has been only performed in this study. Moreover, the construct of managing internal/controllable risk factors is also linked with the dynamic capability of RBV only in this paper.

The impact of risk management on the performance of small medium enterprises amid the crisis: the case of Lebanon

This quantitative research study investigates the impact of risk management practices, particularly risk identification, on the financial performance and resilience of small and medium enterprises (SMEs) amid crisis situations, with a specific focus on the case of Lebanon. The research involves 283 respondents, consisting of SME owners, managers, and key decision-makers, and employs a structured questionnaire for data collection. The primary objectives of this research are to assess the extent to which SMEs in Lebanon implement risk management practices, examine the relationship between risk identification and financial performance, and determine the role of risk management in enhancing financial resilience during times of crisis. The study acknowledges the unique challenges faced by SMEs in Lebanon, given the recent economic and political crises that have significantly impacted the business environment. The research strategy includes the use of descriptive statistics, correlation analysis, multiple regression analysis, and hypothesis testing to analyze the collected data. Preliminary findings reveal the prevalence of risk management practices among Lebanese SMEs, with risk identification being a crucial component. The analysis aims to elucidate whether robust risk identification processes positively correlate with financial resilience and improved financial performance. The outcomes of this study have significant implications for SMEs in Lebanon, providing insights into the importance of effective risk management as a means to enhance financial resilience during crises. The results will assist SME owners and managers in making informed decisions regarding risk management strategies. Furthermore, policymakers and stakeholders will gain valuable insights into the role they can play in supporting SMEs in building financial resilience in challenging economic environments.

Impact of Digital Finance on New Venture Performance Along with the Moderating Role of Entrepreneurial Finance and Risk Management

This research seeks to unravel the influence of digital finance on the performance of emerging ventures and delve into the moderating roles of risk management and entrepreneurial finance in shaping this dynamic relationship. This study tried to answer the question if digital finance plays a role in enhancing the performance of new ventures and whether entrepreneurial finance and risk management moderate this relation. The data used in our study is primary data, collected directly from the respondents through a structured questionnaire using convenience sampling techniques. The sampling unit were managers, owners, and employees of new ventures. The sample size was 370 individuals. A partial least square tool is used to analyze the data. Confirmatory factor analysis is conducted in which reliability and validity are examined by composite reliability, Cronbach alpha, AVE, and discriminant validity were measured using heterotrait-monotrait ratio. PLS structural model was used to investigate the relationship between variables. The study outcomes unveiled a noteworthy positive correlation between digital finance and the performance of new ventures. Additionally, a significant and positive association between entrepreneurial finance and new venture performance emerged, while an analogous positive relationship between risk management and new venture performance was also evident upon data analysis. Furthermore, risk management and entrepreneurial finance also play a moderating role between new venture performance and digital finance. This study will contribute to filling the gap in the literature and will help the researchers, teachers, students, and practitioners to better understand the direct impact of digital finance, entrepreneurial finance, and risk management and moderating effect of entrepreneurial finance and risk management on new venture performance. Practical implications of the study are that the study will help practitioners of newly initiated businesses to place importance on implementing each of the variables while running the business. The higher success rate of new ventures will motivate more people to start a new business and help to improve the economy. The cross-sectional design of the study, coupled with the utilization of a convenient sampling technique and a restricted sample size, introduces certain constraints and limitations to our research.

INTEGRATION OF ARTIFICIAL INTELLIGENCE INTO THE BLOCKCHAIN AND CRYPTOCURRENCY MARKET

This article explores the transformative potential of integrating artificial intelligence (AI) into blockchain technology and the cryptocurrency market. Highlighting the growing attention towards blockchain and cryptocurrencies, emphasizing their decentralized, secure, and transparent nature. However, challenges, including the need to address scalability issues and ensure responsible usage, prompt a focus on AI integration as a viable solution. The study delves into the benefits of AI in the cryptocurrency market, showcasing its ability to predict trends, identify risks, and optimize trading strategies. The research emphasizes the relevance of investigating the integration of AI into blockchain and its specific applications, particularly in detecting and preventing fraud. The article recognizes the potential for increased efficiency, reduced costs, and improved security in transactions through this integration. Acknowledging potential challenges such as AI decision-making implications and technical hurdles, the article advocates for ongoing research and development. It highlights the need for responsible AI adoption to maximize benefits while addressing concerns like algorithmic biases and potential market manipulation. Also explores AI's role in risk management, fraud detection, and investment management within the financial sector. It underscores the importance of ethical considerations, transparency, and accountability to ensure AI's integration aligns with the best interests of all stakeholders. As the article concludes, it emphasizes the dynamic and transformative potential of the integration of AI into blockchain and the cryptocurrency market. It advocates for a collaborative approach among stakeholders, policymakers, and developers to ensure responsible usage, compliance with regulations, and ongoing innovation. The synthesis of AI and blockchain technologies has the potential to revolutionize industries, enhance security, and contribute to a more efficient, transparent, and innovative future.

Neural network-based liquidity risk prediction in Indian private banks

Liquidity risk stands as a pivotal financial concern within the Indian banking landscape, particularly in the context of Basel Recommendation. This recommendation underscores the vital role of liquidity risk management, urging banks to establish robust information systems for the assessment, anticipation, and control of liquidity risks. Recognizing the nuanced nature of this risk, banks across India employ diverse tools and methodologies in managing liquidity risk. This study delves into the efficacy of artificial neural networks (ANNs) in forecasting liquidity risk within the private banking sector in India. Drawing from prior research and leveraging accounting data, a unique structure and architecture for a multilayer perceptron neural network was developed. Subsequently, the study harnessed this ANN framework, along with Matlab software, to forecast liquidity risk in private Indian banks over the period spanning 2009 to 2019. The research outcomes shed light on the promising potential of artificial neural networks as a valuable tool for predicting liquidity risk in private Indian banks. This discovery holds significance in enhancing the resilience and stability of the Indian banking sector by providing more precise and timely insights into liquidity risk management.

Predicting Malaysian University Performance Through Intranet Quality: A Review on Mediating Role of Enterprise Risk Management

Predicting Malaysian University Performance Through Intranet Quality: A Review on Mediating Role of Enterprise Risk Management

Impact of Financial Literacy on Firm Performance of SMEs in Sri Lanka: Mediating Role of Enterprise Risk Management

Small and medium enterprises (SMEs) can be recognized as a main role player in a country’s economy. This study mainly focuses on identifying a few factors that could lead to identifying a viable business strategy for SMEs in Colombo District, Sri Lanka. As a developing country, Sri Lanka needs to have lucrative business potential and sustainability of the business. It has been identified that financial literacy (FL) plays a crucial role in enhancing business performance, and enterprise risk management (ERM) is considered to be another important factor in the betterment of business performance. Therefore, in this deep study, it was mainly hypothesized that higher FL leads to the high performance of SMEs in Sri Lanka. Same time, ERM was formed to mediate the relationship between FL and SME performance. FL found three constructs which are Financial Knowledge (FK), Financial Behavior (FB) and Financial Attitude (FA), and ERM following the same decomposed into three constructs, namely Identification of Risk (IR), Facing Uncertainties (FU) and Risk Reaction (RR). Results confirmed that there is a positive and direct relationship between FL and SME performance and ERM has a mediate relationship between FL and SME performance.

Fight COVID Milwaukee protective behaviors and risk communications associated with the COVID-19 pandemic

The COVID-19 pandemic has had a major impact on society, causing significant disruptions to everyday life. Risk communication strategies can play an important role in risk management as they allow individuals to prepare for and respond to public health emergencies appropriately. The aim of this study is to investigate public risk behaviors, perceptions of risk and risk communication, and experiences with COVID-19 to better understand the impact of COVID-19 on our community and to better inform public health decisions about communicating and reducing personal risk. Nine virtual focus groups were conducted with 79 residents of Milwaukee County. Audio transcripts of focus group recordings were qualitatively analyzed using MAXQDA. Predominant themes identified include public risk protective behaviors, the emotional toll associated with lockdown measures, and risk communication. Our findings provide a better understanding of how adults, African American and Hispanic groups in particular, viewed the risk communications and protective behaviors associated with COVID-19, how their lives were impacted by the pandemic, and how to effectively communicate public information about personal risk. These findings can help guide risk communication efforts and public health policy interventions for potential infection outbreaks in the future.

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.

Decentralized Federated Learning-Enabled Relation Aggregation for Anomaly Detection

Anomaly detection plays a crucial role in data security and risk management across various domains, such as financial insurance security, medical image recognition, and Internet of Things (IoT) device management. Researchers rely on machine learning to address potential threats in order to enhance data security. In the financial insurance industry, enterprises tend to leverage the relation mining capabilities of knowledge graph embedding (KGE) for anomaly detection. However, auto insurance fraud labeling strongly relies on manual labeling by experts. The efficiency and cost issues of labeling make auto insurance fraud detection still a small-sample detection challenge. Existing schemes, such as migration learning and data augmentation methods, are susceptible to local characteristics, leading to their poor generalization performance. To improve its generalization, the recently emerging Decentralized Federated Learning (DFL) framework provides new ideas for mining more frauds through the joint cooperation of companies. Based on DFL, we propose a federated framework named DFLR for relation embedding aggregation. This framework trains the private KGE of auto insurance companies on the client locally and dynamically selects servers for relation aggregation with the aim of privacy protection. Finally, we validate the effectiveness of our proposed DFLR on a real auto insurance dataset. And the results show that the cooperative approach provided by DFLR improves the client’s ability to detect auto insurance fraud compared to single client training.

Developing the Balanced Scorecard to Assess Investment Risks in Islamic Banks: The Yemeni Environment as a Model

this research aims to provide a Proposal Approach to developing a balanced scorecard for the use of assessing investment risks in Islamic banks. The researchers focused on the risks that are unique to Islamic banks from traditional banks, represented by the risk of the rate of return, the displaced commercial risk, the Fiduciary risk, Al- shari’a non-compliance risk, and the risk of investment in capital. To achieve this, the researchers relied on applying the Proposal Approach for three Islamic banks in Yemen due to the availability of their data in the annual reports. The results showed that there is a deficiency in the assessment of investment risks in Islamic banks with regard to the risks the Islamic banks are unique for, such as risk of the rate of return , the displaced commercial risk, the Fiduciary risk, Al- shari’a non-compliance risk, and the risk of investment in capital, It focused its attention on common risks between Islamic and commercial banks, such as credit risks, market risks, liquidity risks, and operational risks. The results also showed an effective role for the balanced scorecard in assessing investment risks in Islamic banks, which enhances the role of risk management in controlling and monitoring of the investment risks and taking appropriate procedures to reduce these risks to the acceptable levels. Keywords: Investment Risks, Risk Assessment, Balanced Scorecard.

Analyzing the health supply chain risks during COVID-19 pandemic: The moderating role of risk management

Analyzing the health supply chain risks during COVID-19 pandemic: The moderating role of risk management

Research on the Pricing Model of DerivativesBased on the Black-Scholes Model and Binomial Model

The pricing of financial derivatives plays a significant role in risk management and financial transactions. The models of pricing are mainly divided into Black-Scholes Model and Binomial Model. The two models have different characteristics and distinguishing the models is helpful for price makers. The paper aims to analyse the distinction between the two models on the Huaxia Stock Exchange 50ETF by using Python. This paper selected the stock price, the volatility and the option price from the Eastmoney database. First, edit the formulas of two models and then introduce the parameters into the formula by Python and get the model calculated price. Comparison between the calculated prices helps to compare the two pricing models. The result is that Black-Scholes Model is more accurate and Binomial Model is more simplified which is suitable for new officers.

Determinants of Poultry Egg Farmers’ Participation in Livestock Insurance in Rivers State, Nigeria

Poultry farming is exposed to several hazards caused by epidemic diseases, climate change and marketing among others which lead to losses of revenue. Yet, majority of poultry farmers do not neither shared nor transferred their hazards rightly. In the light of this, this study examined the determinants of poultry egg farmers’ participation in livestock insurance in Rivers State, Nigeria. Primary data were obtained with the aid of questionnaire and interview schedules from 120 farmers drawn through multistage sampling procedure. Descriptive statistics and Probit regression model were used to analyze the data. Results shows the mean age of the poultry egg farmers to be 45.21 years, years spent in formal education mean of 14.87 years, mean household size of five (5), and stock size of 1721 birds were obtained. 60.8% were aware of livestock insurance and about 35% of the farmers insured their farms. Probit regression result shows that access to credit facilities, stock size and household size were statistically significant determining the poultry egg farmers’ participation in livestock insurance scheme. Poor agricultural extension service delivery and delay in indemnity payment among others were constraints encountered in participating livestock insurance. The study recommends that extension agents in collaboration with the insurance company providers should educate poultry farmers on livestock insurance role in risk management. Also, insurance companies should endeavor to keep terms of contractual arrangements not to delay in indemnity payment to the farmers.

Machine Learning Approach for Catastrophe Risk Assessment and Management Using Remote Sensing Data

With emergency programs for disaster preparedness and warning phases for earthquakes, landslides, and floods in recent years, remote sensing and geographic information systems have played a crucial role in Catastrophe Risk assessment and management. It has also been a key focus in the field of technology. Without the right tool for organizing massive volumes of data and gathering information from many sources, such maps or measurement channels, it would not be feasible to employ sensory data. In order to identify and assess areas affected by floods, earthquakes, avalanches, landslides, and wildfires, this study employs machine learning approaches. Following the application of filters to enhance image quality, the images are segmented through thresholding technique and classified using supervised and unsupervised classification methods. Images from before and after disasters are gathered from MRSAC Nagpur and processed using Python-based tools, ArcGIS, ERDAS, and QGIS for the purpose of analyzing devastation.

An analytic solution and an approximate solution for log‐return variance swaps under double‐mean‐reverting volatility

Variance swaps is a kind of financial instrument that plays an important role in volatility risk management. In this paper, we study the pricing problem of log‐return variance swaps under the double mean reversion DMR (Heston‐CIR) model. Compared with Kim's work, we introduce the square‐root process into the diffusion term of the long‐term mean and present a stochastic approach that greatly simplify the solution of the problem without solving PDEs. An analytical solution and approximate solution are obtained. Some numerical examples show that the exact solution and MC simulation fit well. It is worth mentioning that the difference between the approximate solution and the exact solution is small when the parameters are selected appropriately. By the mean time, the parameter of the long‐term mean has an important impact on the solution, which implies that the introduction of a multi‐factor model is necessary.

Role of ICT Risk Management on Insider Fraud Prevention in Commercial Banks in Nairobi County

Purpose: The main objective of the research was to investigate the role of ICT risk management on reduction of insider frauds in the Kenyan banks in Nairobi County. The research explored ICT risks management practices incorporated in the banking industry to mitigate and control insider fraud.
 Methodology: Explorative research design and inferential statistics were used. The unit of analysis was the ICT risk management professionals in all the commercial banks in Nairobi County. 42 commercial banks formed the population of the study. The unit of observation were the ICT security, audit and risk professionals mandated with implementing ICT risk management. The targeted number was at least one respondent from each bank from the three departments identified: internal audit, Information technology, and security managers. In total, the study targeted 42 respondents and a total 29 responses were received which formed 69% of the targeted population. Responses from the distributed questionnaires were analyzed using statical packages for social science (SPSS). The open-ended questions were listed, analysed and reported by descriptive narrative with such statistics as mean and standard deviation. The ANOVA test was used to test the results.
 Findings: The findings revealed that there is a positive but insignificant correlation between ICT risk assessment, ICT awareness, Information security implementation and a significant positive correlation between information security audits and Insider fraud prevention. This study recommends that ICT risk assessment; ICT risk awareness; information security audits; and information security policy are important for preventing insider fraud in commercial banks, but there is need to implement them in conjunction with other stringent measure to increase efficiency
 Unique Contribution to Theory, Practice and Policy: The findings give a theoretical basis for validating the effectiveness of ICT risk management practices in Banks in Nairobi County, this can be adopted by other counties in Kenya. For policy implementation, the study will be important to the central bank of Kenya and any other institution that regulates the banking sector in Kenya in reviewing the current ICT Risk management guidelines. The study shows the adoption of the selected ICT risk management practices by different banks and gives a measure of their effectiveness that ICT Security professionals can use. The study gives the banking sector ICT security professionals a perspective on how ICT risk management can help them improve their ability to curb insider fraud. The study adds to the pool of knowledge for to scholars and academicians.