Efficiency Of Risk Management Research Articles

Big Data Analytics for Financial Decision Making in Malaysia

Purpose: To aim of the study was to analyze the big data analytics for financial decision making in Malaysia. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Big Data Analytics (BDA) significantly enhances financial decision-making in Malaysia by extracting valuable insights from large datasets. It helps improve risk management, customer segmentation, and operational efficiency for financial institutions. BDA enables predictive analysis of market behavior, supports personalized financial services, and enhances fraud detection capabilities. Despite its benefits, challenges like data privacy issues and the need for skilled data professionals persist. Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, technology acceptance model (TAM) & network externalities theory may be used to anchor future studies on big data analytics for financial decision making in Malaysia. Prioritize investments in scalable and secure data infrastructure to support the deployment and integration of BDA technologies across financial institutions. This includes upgrading data storage systems, enhancing data processing capabilities, and implementing robust data integration frameworks. Develop and enforce clear regulatory frameworks that govern the ethical use of BDA in financial decision-making. Regulatory guidelines should address data privacy, security standards, transparency in algorithmic processes, and consumer rights protection.

The Impact of Awareness and Training on Enterprise Risk Management: A Case Study on Enhancing the Role of Training and Raising Awareness in Improving the Efficiency of Risk Management in Industrial Companies and Government Institutions in Jubail Industrial City, Saudi Arabia

The Impact of Awareness and Training on Enterprise Risk Management: A Case Study on Enhancing the Role of Training and Raising Awareness in Improving the Efficiency of Risk Management in Industrial Companies and Government Institutions in Jubail Industrial City, Saudi Arabia

Extreme spillovers across carbon and energy markets: A multiscale higher-order moment analysis

The complexity of carbon market mechanisms and the uncertainty in market conditions raise questions on how carbon and energy markets interact. The majority of existing studies focused on the lower-order moment spillover across carbon and energy markets, thereby posing limitations on carbon risk management and carbon market efficiency. This paper analyzes multiscale skewness and kurtosis spillovers across carbon and energy markets under different market conditions. It is found that carbon market is a short-term net skewness spillover receiver, but becomes a medium-term risk source under all market conditions, capable of transmitting skewness risk to the natural gas market. The carbon market acts as a short- and medium-term kurtosis risk source for the natural gas and electricity markets in the lower probability of extreme returns, but bears the kurtosis risk from the natural gas and coal markets when the extreme risk is high. These results indicate that policymakers should take measures to adjust carbon prices when facing long-term skewness risk and a lower probability of extreme returns in the carbon market, to prevent further spread of risk to the energy markets.

Construction of Risk Prediction Models for Enterprise Finance Sharing Operations Using K-Means and C4.5 Algorithms

The evaluation of financial sharing centres in enterprises typically relies on outdated financial data, lacks comprehensive assessment, and presents risks such as employee misconduct. To address these challenges, we propose a risk prediction model for enterprise financial sharing operations based on the K-means clustering algorithm for performance evaluation and the C4.5 algorithm for managing employee risks. Our approach enhances the accuracy and objectivity of performance evaluation while improving the efficiency of personnel risk management. Results indicate that the K-means algorithm classifies employee performance into five levels, facilitating comprehensive performance evaluation. Furthermore, through risk management optimisation, accuracy and recall rates increase to 0.905 and 0.890, respectively. The proposed risk prediction model achieves high accuracy rates of 90.5% and 92.4% in the training and test sets, respectively. Practical application of our methodology and model in A Group's financial sharing centre demonstrates their effectiveness and potential for enhancing the operation and management of enterprise financial sharing centres.

Enhancing Corporate Financial Performance Through AI: A Novel AI Model for Forecasting Organizational Risk Management, CRM, and Operational Efficiency

Enhancing Corporate Financial Performance Through AI: A Novel AI Model for Forecasting Organizational Risk Management, CRM, and Operational Efficiency

Examine the Relationship Between the Professional Competency of IA and the Effectiveness of RM in Improving Compliance in Iraq's Public Organization

The importance of internal audits has escalated in the current dynamic and intricate economic and financial landscape. It plays a crucial role in assisting management in attaining its goals by offering unbiased and independent assurance regarding the efficiency of risk management, the sufficiency of internal control, and the accuracy of financial reporting. It is widely known that Iraq has seen instances of fraud and mismanagement of public funds in recent years, highlighting a systemic flaw in the regulatory structure and the auditing profession inside public sector organizations. However, by enhancing compliance through the fundamental pillars of optimizing the efficiency of internal audits and the efficacy of risk management, we can envision a future where the misuse of public funds and various forms of corruption are minimized. This, in turn, will lead to improved adherence to relevant laws and the production of reliable financial statements that can be used for decision-making by appropriate stakeholders. It is the role of government agencies to prioritize these improvements, as they have the power and responsibility to do so. We created a questionnaire of three dimensions and 45 paragraphs to fulfil the research objectives. A sample of internal auditors in sectoral organizations was randomly selected, comprising 283 respondents. We employed both primary and multiple regression analyses to examine the hypotheses. The study concluded that there needs to be a strategy for continuing education for internal audit employees, which indicates a lack of sufficient interest in this topic and the lack of clear strategies and policies for risk management.

Analysis Of Efficiency Level In Indonesian Banking Companies Listed On The Indonesian Stock Exchange For The 2019-2022 Period

This study analyzes the efficiency levels of Indonesian banking companies listed on the Indonesian Stock Exchange (IDX) during the period of 2019-2022, encompassing the impact of the Covid-19 pandemic. Efficiency in banking is crucial for maintaining competitive advantage and ensuring financial stability, especially in the face of global challenges such as economic globalization and digitalization. The research evaluates the efficiency of banks using indicators like the CAMEL method, BOPO, NIM, NPL, LDR, and CAR ratios. The study finds that the pandemic significantly affected banks' efficiency, with notable declines in NIM and ROA ratios due to economic uncertainty and operational cost challenges. Post-pandemic, these ratios have shown signs of stabilization. The study uses descriptive quantitative methods, analyzing secondary data from annual financial reports of eight banking companies listed on IDX. The results indicate that strategic measures in risk management and cost efficiency are imperative for banks to navigate future uncertainties.

Institutional vulnerability to urban flood risk management: A case study of the 7·20 flooding disaster in Zhengzhou, China

AbstractThe ‘vulnerability’ approach highlights institutional factors as the primary drivers of flooding risk. However, the intricate connections between institutional vulnerability and urban flood disaster risk have not been extensively explored until now. This study aims to address this gap by examining the case of the 7·20 heavy rainfall disaster in Zhengzhou. Employing a hybrid analysis methodology that integrates text coding, fault tree analysis and analytic hierarchy process methods, the study seeks to identify and assess the distinct contributions of various institutional factors and their interplay, culminating in ineffective urban flood risk management. In contrast to previous research findings that emphasize the determinant role of policy‐making and organizational coordination, this study demonstrates that inadequate legislative compliance constitutes the root cause determining the ineffectiveness of urban flood risk management. It is further exacerbated by insufficient policy attention, resulting in inadequate allocation of resources that ensure the legislative quality, risk coping skills and knowledge and stakeholders' coordination and collaboration. By emphasizing the significance of legislative compliance and policy attention, the study offers a fresh conceptual perspective to understand the factors influencing the efficiency of urban flood risk management. It provides valuable insights to develop targeted countermeasures for mitigating similar urban flood risks in the future.

A mechanism for efficiently controlling the safety risks of pumped storage power station construction

Abstract During the construction process of pumped storage power station, the management levels of the participating parties are uneven, and problems such as inaccurate risk identification and unreasonable control measures often occur, which affect the effective operation of the dual prevention mechanism. In order to improve the efficiency and effectiveness of risk management, the author has established a risk database covering the entire life cycle of engineering construction based on the recognition of dangerous and harmful factors in the construction process of pumped storage power station. This greatly enhances the comprehensiveness of risk identification and the effectiveness of measures. At the same time, based on the reasonable allocation of risk management resources according to the task level, information technology tools are used to approve and publish construction work plans, timely release, track and close-loop risk management tasks. Through online processes, the investigation and rectification of hidden dangers are automatically counted, analyzed and tracked, thereby reducing the risk level, eliminating accident hidden dangers, and improving work efficiency.

Examining the Distributional Characteristics of Daily Returns of Nifty 50: Normality Assessment and Implications

This paper investigates the distributional characteristics of daily returns of the Nifty 50 index, a benchmark index comprising 50 large-cap stocks traded on the National Stock Exchange of India. Utilizing historical data spanning a specified time period, we conduct normality testing to assess the adequacy of the normal distribution assumption underlying many financial models. Our analysis provides insights into the departure from normality. As a result of departure from Normality, it may affect the tail behaviour, and volatility dynamics of Nifty 50 daily returns, offering implications for risk management, option pricing, portfolio management, and market efficiency. By synthesizing empirical findings with theoretical considerations, this research contributes to a deeper understanding of the statistical properties of Nifty 50 daily returns and informs practitioners in finance about the challenges associated with Modeling and analysing stock market data.

Research on Entity Relationships in the Knowledge Graph of Disease Monitoring in Grotto Temples

Abstract. The grotto temple, carved into cliffs and widely distributed, is a significant cultural heritage in China. However, it faces severe damage and collapse threats due to natural disaster risks in its environment. Nearly seventy percent of grotto temples are located in regions prone to earthquakes and water hazards, leading to varying degrees of damage to cultural artifacts. Therefore, preventive measures are necessary to reduce the impact of natural disasters on grotto temples. A knowledge graph, a structured semantic knowledge base describing concepts and their relationships in the physical world, plays a crucial role in knowledge organization and content representation. Entity relationships are the core of knowledge, serving as both foundational data and a key task in constructing knowledge graphs and processing unstructured text. In the field of grotto temple disease monitoring, while data continues to grow, research on the correlation between textual data remains underexplored. This paper adopts the BiLSTM-CRF method to extract entity relationships, matching them with the grotto temple monitoring knowledge graph. Finally, the Neo4j software is utilized to program and display the knowledge graph, aiming to enhance the efficiency of natural disaster risk management and cultural heritage protection for grotto temples.

Fintech's influence on green credit provision: Empirical evidence from China’s listed banking sector

We explore the impact of financial technology (fintech) advancements on green credit provision, investigating publicly traded banks in China from 2007 to 2022. We particularly focus on credit modelling innovation, examining the non-linear dynamics between fintech evolution and green credit distribution. Results reveal a positive U-shaped correlation. Initial stages of fintech are associated with increased green credit risk, negatively affecting the volume of green credit. However, more established fintech infrastructures significantly enhance green credit volumes by improving resource allocation and credit risk assessment. Utilizing a multiple linear regression approach, we highlight the transformative nature of fintech in advancing sustainable banking practices, particularly through innovations in credit modeling that enhance green credit risk management and resource allocation efficiency.

Sharia Compliance and Profitability in Financial Performance Islamic Banks in Indonesia

This study sought to evaluate the financial performance of Islamic commercial banks in Indonesia from 2018 to 2022, with a specific focus on Sharia compliance and profitability. Secondary data taken from the annual reports of commercial Islamic banks during the specified period were used and a descriptive quantitative approach was adopted. The study population consisted of all 13 Islamic commercial banks registered with the Financial Services Authority in 2022; eight banks were chosen as the research sample. The analysis method used encompasses both Sharia compliance and profitability and is called the Sharia Compliance and Profitability Model. For further analysis, the research findings were divided into four quadrants. The results of the study showed how different Islamic commercial banks in Indonesia performed in terms of Sharia compliance and profitability. It identified banks that showed strong adherence to Sharia principles and high profitability, which could be used as benchmarks by other banks to improve their performance. In more detail, The findings of this research included 3 aspects, namely 1) Identification of Sharia Compliance, this study identified the parameters used to assess Sharia compliance, such as compliance with the principles regulated by the Indonesian Ulema Council (MUI) or equivalent Sharia regulatory institutions. The results of this research revealed banks that consistently complied with Sharia principles in their operations and products. This could include banks that had a financial structure that complied with Islamic law, avoids usury, and adhered to the principles of fairness in transactions. 2) Profitability Assessment. Financial analysis has been carried out to assess the profitability of the investigated banks. This could include profitability ratios such as ROA (Return on Assets) and ROE (Return on Equity), as well as the profit growth from year to year. 3) Factors that influenced the Performance as well as the analysis also included an exploration of factors that contributed to bank performance in terms of Sharia compliance and profitability. This could include internal factors such as risk management and operational efficiency, as well as external factors such as market conditions and regulations. The research has practical implications for commercial banks for enhancing performance and adhering to Islamic values in highly profitable banks that strictly adhere to Sharia principles. By examining the connection between Sharia compliance and profitability, this study added to the understanding of the financial performance of Indonesia's Islamic commercial banks.

Algorithm of innovation project risk management

Aim. To develop a risk management algorithm that can be used in risk management of innovations.Objectives. On the basis of the current international standards of risk management and scientific publications to study the existing practice of risk management, highlighting the main elements of the risk management process into a single generalized risk management algorithm; based on the results of the first task, as well as taking into account the identified features of the process of innovation implementation, to develop an algorithm for risk management of innovation projects.Methods. The work is based on the study of scientific publications in the field of risk management and analysis of international standards on risk management in order to adapt them to the peculiarities of innovation management.Results. The problem of risk management of innovation projects is one of the most difficult in project management because of the significant degree of uncertainty inherent in innovation. The article reflects the problem of investors’ acceptance of increased risk during the realization of an innovation project. The peculiarity of risk management of innovations is the need to accept increased risk, and in return the opportunity to present a new product or technology to the market. Taking into account the considered peculiarities, an algorithmic approach to risk management of innovation projects is given. The practical significance of the study lies in the possibility of applying the developed algorithm of innovation project risk management by economic entities, which will improve the quality of corporate risk management and increase the efficiency of resource utilization. The authors point out the necessity of selecting a project with positive NPV (discounted cash flow, DCF) and accepting the increased risk of the innovation project in the process of innovation management.Conclusions. The authors have created a comprehensive methodological approach to innovation project risk management in the form of a sequence of actions algorithm. The purpose of its application is to increase the efficiency of risk management and to achieve a successful result of innovation implementation.

Shareholders' political hierarchy and regulatory enforcement: Evidence from corporate risk management

In our study, we investigate the impact of shareholders' political hierarchy on regulatory enforcement within the context of a Chinese local state-owned enterprise where multiple government shareholders, each with different political hierarchical levels, coexist. Our research reveals that the presence of higher-level noncontrolling government shareholders, specifically noncontrolling government shareholders with a higher hierarchical level than the controlling government shareholders, enhances the risk-reducing effect of derivative use. This indicates that higher-level noncontrolling government shareholders play a role in facilitating the enforcement of derivative-related regulations. Additionally, we find that these higher-level noncontrolling government shareholders enhance the efficiency of derivative use through two primary channels: their superior access to regulatory information and their monitoring of corporate regulatory compliance. We also explore the impact of higher-level noncontrolling government shareholders in regulatory enforcement by examining the 2009–2010 top-down derivative-related regulation. In summary, our analysis underscores the positive influence of higher-level noncontrolling government shareholders on risk management efficiency, primarily due to their information advantage regarding regulations, which subsequently strengthens the enforceability of regulatory measures.

Artificial Intelligence in stock broking: A systematic review of strategies and outcomes

Artificial Intelligence (AI) has emerged as a transformative force in the field of stock broking, revolutionizing traditional trading strategies and reshaping financial markets. This systematic review delves into the diverse array of AI-driven strategies employed in stock broking and assesses their outcomes, shedding light on the evolving landscape of algorithmic trading. The study encompasses a comprehensive analysis of various AI models, including machine learning algorithms, deep neural networks, and natural language processing techniques, that have been harnessed to analyze market data, predict stock movements, and optimize trading decisions. By synthesizing existing literature, the review offers insights into the effectiveness and limitations of these strategies, providing a nuanced understanding of their impact on market dynamics. Key findings reveal that AI applications in stock broking exhibit a wide spectrum of approaches, ranging from predictive modeling for price forecasting to sentiment analysis for gauging market sentiment. The review also explores the integration of reinforcement learning in algorithmic trading, highlighting the adaptive nature of AI systems in responding to dynamic market conditions. Furthermore, the outcomes of AI-driven strategies are evaluated in terms of risk management, profitability, and overall market efficiency. The review identifies trends indicating increased efficiency and reduced human biases, but also acknowledges challenges related to model interpretability, ethical considerations, and the potential for algorithmic-driven market volatility. This systematic review contributes to the evolving discourse on the role of AI in stock broking, offering a holistic examination of strategies and outcomes. As financial markets continue to embrace technological advancements, understanding the nuances of AI applications becomes paramount for market participants, regulators, and researchers alike. This study serves as a valuable resource for stakeholders seeking to navigate the complex interplay between artificial intelligence and the stock broking landscape.

The concept of increasing the efficiency of financial risk management based on the application of new digital technologies

Aim. Development of the author’s concept of increasing the efficiency of financial risk management on the basis of application of the technology of creating and using the digital twin of the enterprise.Objectives. To formalize the problems of existing approaches to solving the problems of financial risk management of enterprises of the real sector of economy; to identify trends in the development of digital twins, to formulate the advantages of their practical application and to give the author’s interpretation of the concept of “digital twin of the enterprise”; to build and substantiate a multilayer system of indicators of the digital twin of the enterprise in the context of improving the efficiency of financial risk management; to develop the principles of building a digital twin of the enterprise, focused on solving the problems of financial risk management.Methods. The research was carried out on the basis of system approach, in the process of realization of which the methods of synthesis, logical comparative, factor and graphical analysis, basic provisions of financial risk management and the results obtained in domestic and foreign scientific literature on the problems of realization of approaches and methods of financial risk management were used.Results. The author’s concept of increasing the efficiency of financial risk management based on the application of new technologies for creating digital twins is proposed. In particular, by means of formalizing the problems of existing approaches to solving the problems of financial management, identifying trends in the development of methodology and technologies for creating digital twins, the idea of increasing the efficiency of financial risk management on the basis of creating a digital twin of the enterprise is outlined. The multilayer system of indicators defining the structure of this digital twin is proposed and substantiated, the tasks are defined and the principles of its construction oriented to solving the tasks of financial risk management are developed. The financial kernel of the digital twin of the enterprise is constructed, containing the contours of formation of financial indicators common for different enterprises, determining the achievement of the objectives of financial risk management and its efficiency. The qualitative substantiation of increasing the efficiency of financial risk management with the application of the methodological basis for the creation and use of digital twins proposed in the article is given.Conclusions. Conceptual provisions of creation and use of digital twins for the purposes of financial risk-management, given in the article, create a theoretical and methodological basis for increasing the efficiency of financial risk-management taking into account the use of new digital technologies and can serve as a basis for practical construction of effective systems of financial risk-management at enterprises of the real sector of economy.

Implementasi Good Corporate Governance (GCG) Dalam Pengelolaan Manajemen Risiko Di PT. Bank Sumut KCP Syariah Marelan Raya

This research aims to investigate in depth the implementation of Good Corporate Governance (GCG) in company risk management. By analyzing how GCG principles are implemented, this research will identify their impact on organizational risk management. The focus involves concrete evaluation of policies, organizational structures and monitoring mechanisms related to GCG. This research uses a qualitative method which is a natural research design, meaning that the researcher does not try to manipulate the research setting, but rather carries out a study of a phenomenon. The research results are expected to provide in-depth insight into how the implementation of GCG can improve the efficiency of risk management and overall corporate welfare.

Efficiency of price risk management - the case of futures markets for metals and energy in India

Efficiency of price risk management - the case of futures markets for metals and energy in India

Strategic Analysis of Informatization of International Trade Enterprises’ Economic Management Models Based on Game Theory Models

Abstract Exploring the economic management models of international trade enterprises, this research highlights the significant role of informatization strategies informed by game theory. Through the prism of game theory, we analyze the interplay among various stakeholders, including risk takers, managers, and executors. Our study reveals that appropriate risk-taking can drastically lower operational risks. A case in point is Company A, which witnessed a notable improvement in financial stability, with its Z-score rising from 2.6785 in 2005 to 4.6314 in 2022. As theAnalysis shows, effective risk management plays a pivotal role in boosting economic efficiency, evident through enhanced total asset turnover and gross operating margins. This study underscores the utility of a game theory-based economic management model in improving risk management and financial efficiency, thereby advancing the implementation and refinement of informatization strategies within enterprises.