Enterprise Risk Assessment Research Articles

Fuzzy Logic and Neural Network-based Risk Assessment Model for Import and Export Enterprises: A Review

With the rapid growth in foreign trade business and the continuous expansion of customs functions, the amount of data obtained by customs monitoring systems has drastically increased, and risk management techniques have been widely used in the customs field. Risk assessment, as an important part of risk management, can help departments of customs to understand the possibility and impact of risks in advance. Assessing the risks of import and export enterprises is an important task for customs. However, the assessment is challenging because of the large-scale of data, rapidly changing information, and inherent fuzziness. To address this issue, this survey summarizes the existing risk assessment methods, combines the preliminaries of fuzzy logic and neural networks, and applies the representative risk assessment models in the risk assessment of import and export enterprises. The risk assessment models based on fuzzy logic and neural networks can handle vague and uncertain data and improve the efficiency of risk assessment through adaptive and learning capabilities. The combination of theoretical and simple case suggests that the risk assessment method based on fuzzy logic and neural networks has good feasibility for import and export enterprises and provides valuable references for developing effective risk assessment models of import and export enterprises in the future.

Research on credit risk assessment of e-commerce enterprises based on improved multi-objective clustering algorithm

Research on credit risk assessment of e-commerce enterprises based on improved multi-objective clustering algorithm

Research on credit risk assessment of e-commerce enterprises based on improved multi-objective clustering algorithm

Research on credit risk assessment of e-commerce enterprises based on improved multi-objective clustering algorithm

ASSESSMENT OF PRODUCTION AND TECHNOLOGICAL RISKS IN THE ACTIVITIES OF VETMEDICINE ENTERPRISES

Risk is the possibility of a negative discrepancy and the likely scale of its consequences over a period of time. Giving producers of products for veterinary medicine and animal husbandry (such as veterinary drugs, protective equipment, feed, feed additives, etc.) responsibility for the production of quality products obliges them to clearly and systematically control all hazards that may occur in the production. The essence of effective product quality control is to ensure that measures to produce quality products are adequate and meet the task - not to harm animals and birds, provided the correct way of their use, as well as people. Accordingly, all production processes must be clearly regulated and periodically reviewed in the light of experience and the situation on the product market. Systematic control of the stability of production for veterinary medicine and animal husbandry according to the parameters defined in the quality specification is justified. It should be borne in mind that the activities of enterprises may be influenced by factors that hinder the implementation of the goals. G. Verbytska (2004) notes that the existing theory of both quantitative and qualitative risk assessment requires relevant information, although the information market on these issues is still underdeveloped. Therefore, the target methods of quantitative risk analysis are not developed, and the existing ones are not universal. Therefore, there is a need to combine methods, or their individual elements.
 Analysis of dangerous factors is always specific to each process and production as a whole, so it cannot be "borrowed" from another manufacturer, even if it produces similar products. The experience of leading domestic and foreign veterinary companies shows that the analysis of dangerous facts (risks) consists of a series of consistent, logically interrelated actions.
 The risk factors of the manufacturer should be considered as those under the direct influence of management. Therefore, their identification, analysis results, development of measures to minimize risks are considered one of the priority tasks of enterprise management, which is carried out by its management. Analysis of the activities of a number of enterprises, in particular the results of monitoring and selective control of product quality, confirms that there is a significant level of risk for veterinary enterprises. Their timely assessment and prompt application of appropriate measures can neutralize the identified risks, or eliminate the possibility of their occurrence Chornyi (2017). Inability or untimely assessment of risks excludes the possibility of their control. Therefore, the study of industrial and technological risks are relevant, and their timely detection warns of various threats and economic security of enterprises. Risk assessment of veterinary enterprises in modern conditions is unresolved, not specified in the methodological support of their assessment, which determines the importance of such studies.

Analysis of the Impact of Equity Incentive on Performance Management Based on Balanced Integral Method

Improving the equity incentive system has a certain role in promoting the development of the company, but at present, there are relatively few studies on the equity incentive methods of listed companies under the balanced scorecard theory. This paper analyzes the role of equity incentive method of D listed company in enterprise performance management through the balanced scorecard theory and constructs an enterprise financial management evaluation system with qualitative and quantitative characteristics. The evaluation system includes five target layers, two dimensions, and 27 evaluation indicators. The results show that the number of researchers, the number of graduate students, market share, management fee rate, the number of chip patent applications and other internal business processes, customers, and learning and growth are better. Before the implementation of equity incentive method, the evaluation value range of primary indicators is 6–8 points. After the implementation of equity incentive method, the evaluation value range of primary indicators is 2–6 points. This study analyzes the impact of equity incentive methods on performance management through the balanced integral method, which has high application value in subsequent enterprise financial management and risk assessment.

Application of Machine Learning in Enterprise Risk Management

With the development of China’s economic globalization, more and more enterprises have realized the importance of risk management. As a new technology, machine learning has injected new vitality into enterprise risk management. This paper analyzes the factors of enterprise risk from the perspective of risk management, explores and analyzes the application of machine learning in enterprise risk management, and points out the direction for scientific enterprise risk management. This paper first establishes a set of credit evaluation factors and then builds a model based on this evaluation factor set, which ensures the accuracy of the evaluation and also adds pertinence. With the increase of the number of samples, the accuracy of the prediction results also increases. Also, basically, with the increase of training samples and realistic samples, the accuracy rate continues to increase. In the data of the experimental results, it is shown in the process of increasing the number of samples from 400 to 600. The accuracy of the traditional method is 0.81 and 0.8316, respectively. The accuracy of the improved method is 0.842 and 0.905, respectively. This paper studies the process of enterprise risk assessment from the perspective of risk management and attempts to build an enterprise risk management model based on industry risk coefficients, which have certain practical significance.

The Construction of Corporate Financial Management Risk Model Based on XGBoost Algorithm

Corporate financial management is a tedious task, and it is a complicated thing to rely solely on the human resources of financial personnel to manage. With the continuous development of intelligent algorithms and machine learning algorithms, new ideas have been brought to enterprise financial risk assessment. This method will not only save a lot of financial and material resources but also improve the accuracy of enterprise financial risk assessment. Compared with machine learning algorithms such as random forests and support vector machines, the extreme gradient boosting (XGBoost) algorithm is more widely used, and it has unique advantages in terms of speed and accuracy. This study selects the XGBoost learning algorithm to predict the risk assessment in corporate finance. In this study, the data preprocessing method is used to preprocess and classify the enterprise financial data source effectively, and then the XGBoost algorithm is used to assess the risk of enterprise financial data, and finally a set of enterprise financial risk assessment model is established. The research results show that the XGBoost model selected in this paper has high reliability in predicting the financial risk assessment of enterprises, and the prediction errors are all within 3%. The largest forecast error is only 2.68%, which comes from the profit and loss of the enterprise’s financial situation. The smallest error is only 0.56%, which is a trustworthy enough error for corporate financial forecasting. There is a high correlation between the type of enterprise financial risk assessment and the actual type of risk. At the same time, this paper also has a good dependence on the preprocessing method of enterprise financial data.

Mathematical methods of risk assessment of agricultural enterprises

Methods. The results are obtained through the use of methods: abstraction – in determining the nature of the category «risk»; analysis and synthesis – in highlighting the nature of the risks of agricultural enterprises; logical and historical – in the study of the evolution of approaches to determining the risks of agricultural enterprises; method of classifications – when summarizing the existing approaches to mathematical methods of risk assessment in groups; general and special - in establishing the unity of existing methods of risk assessment; comparison – to determine the advantages and disadvantages of the types of mathematical assessment of the magnitude of risks; abstract-logical analysis – to generalize and formulate conclusions. Results. It is established that against the background of a large number of definitions of risk in the scientific literature there is no established understanding of it. The essential features of agricultural production are analyzed and their influence on the formation of risks of agricultural enterprises is determined. The essential signs of risks of agricultural enterprises and their features are revealed. There is analyzed the essence of the main modern methods of risk assessment and modeling in relation to agro-industrial enterprises (deterministic method, statistical method, probabilistic-statistical method, theoretical-probabilistic method, logical-linguistic method, simulation method, expert method, especially fuzzy sets method). The advantages of using fuzzy logic methods to assess the risks of agricultural enterprises are shown. An algorithm for risk assessment based on the fuzzy logic method is presented. Novelty. On the basis of theoretical and analytical generalizations on mathematical methods of risk assessment of agricultural enterprises, there is substantiated the possibility of using the mathematical apparatus of fuzzy logic and logical-linguistic modeling to assess the source information, which has a fuzzy, uncertain and probabilistic nature. Practical value. The development of methods for identifying and describing sources of danger, as well as the conditions of their manifestation during the operation of these facilities is crucial to the development and implementation of measures to prevent risks on agricultural sites. The limitations of available scientific and methodological materials does not meet practical needs. Therefore, the use of logical-linguistic modeling to assess risks seems promising. This assessment of the presentation of fuzzy information is the most acceptable, as it allows to formalize the knowledge of experts in a convenient semantic form.

Enterprise Counterparty Risk Assessment Principles

The purpose of this article is to describe in detail the principles of risk assessment of counterparties of enterprises and to analyze methods of minimizing risks when interacting with them. The risk assessment of counterparties is a section of the enterprise risk assessment system developed by the authors of the article on the full range of emerging risks, which provides for risk analysis using the developed tools used in assessing the effectiveness of both project and current activities of enterprises. The risk assessment system of counterparties is a detailed classification of risks of counterparties of enterprises, which includes fifteen risk subgroups, each of which is represented by a wide range of risks. Analysis of counterparty risks using the developed risk assessment system allows you to optimize interaction with problematic and potentially problematic counterparties of enterprises and, if necessary, exclude the conclusion of knowingly unprofitable contracts. The article materials can be used by enterprises when analyzing counterparties, both during the current interaction with existing counterparties, and planning the conclusion of contracts with new counterparties, as well as when developing risk assessment and management systems at enterprises. In addition, the materials of the article can be used by scientists in the course of studying the principles of conducting risk analysis of enterprises and all interested persons. In general, the application of the risk assessment system of counterparties, in particular, and the general risk assessment system and the developed tools allows to significantly optimize the risk assessment process of enterprises and significantly reduce the cost of analysis.

Credit risk assessment of small and medium-sized enterprises in supply chain finance based on SVM and BP neural network

Credit risk assessment of small and medium-sized enterprises in supply chain finance based on SVM and BP neural network

Establishment of business risk information value assessment model based on RAROC

Aiming at the problem of low precision of current enterprise risk assessment methods, a RAROC based enterprise risk information value assessment model is proposed. The crawler search method with topic search is adopted to collect the business information data, and the extended tree structure is introduced to clean up the collected data, transformation of basic information data and business value. RAROC is used to process the information with commercial value and realise the value evaluation of commercial risk information. According to the model and KMV model, the risk adjustment coefficient was calculated. The results of the RAROC value evaluation are reflected through statements. The experimental results show that the goodness of fit index, standard fit index and comparison fit index of the model are all close to 1, and the approximate root-mean-square error is less than 0.02, which proves the effectiveness and accuracy of the method.

Enterprise Financial Leverage and Risk Assessment Based on Mobile Payment under Artificial Intelligence

To better promote the healthy and long-term development of corporate financial management, the basement is established on the perspective of artificial intelligence (AI). Initially, based on the theories of modern mobile payment (MP) and corporate financial leverage, the corresponding data set is obtained through the questionnaire method as the research data. The reliability coefficients obtained after the test are all above 0.65, indicating that the reliability and stability of the entire data are relatively good. Besides, it is also found from the data of the questionnaire that some residents believe that MP will bring harm such as information leakage. Next, a new multilevel evaluation analysis method is introduced. After evaluating the financial management risk, operation risk, and network security risk existing in enterprise MP, it is found that the financial management risk accounts for the largest proportion of the three, with a risk weight of about 0.54, and the capital risk occupies the main position in the financial management risk. Finally, through the analysis of the risks existing in the whole operation process of the enterprise, it is found that about 50% of the financial management risk of the enterprise in the market belongs to the advanced risk, about 30% of the operational operation risk belongs to the low risk, and about 20% of the network security risk belongs to the advanced risk, which indicates that the financial management risk and network security risk are the top priority of the enterprise MP risk. Although the operational operation risk belongs to the low risk, it cannot be ignored. Subsequently, feasible suggestions and opinions are put forward on these phenomena from the perspectives of the government, enterprises, and residents. Therefore, there is great reference significance for the current financial risk assessment of enterprises based on MP.

Research on the Risk of Social Stability of Enterprise Credit Supervision Mechanism Based on Big Data

The study aims to establish a platform-based enterprise credit supervision mechanism, and combined with big data, accurately evaluate the credit assets of enterprises under the influence of social stability risk, and improve the ability of enterprises to deal with risks. Using descriptive statistical methods, the study shows that most local enterprises exist in the form of micro loans, which promotes the development of local economy to a certain extent, but it is a vicious cycle of economic development; The overall prediction accuracy of the single enterprise risk assessment model under the influence of social stability risk is 65%. Compared with the single algorithm, the prediction accuracy of the integrated algorithm model is significantly improved, and the prediction accuracy can reach 83.5%, the standard deviation of data prediction is small, and the stability of the model is high.

Strategic Analysis of Export Activities of Enterprises to Ensure Sustainable Development

In the article necessity and significance to study of financial risks, which are a constant factor in the activity of any enterprise, are substantiated. It is established that for a broader understanding of the essence of financial risk it is necessary to consider it as a cumulative risk of the enterprise. The growing impact of financial risks on the results of financial and economic activities is associated with the uncertain economic situation, the instability of financial market conditions, the rapid introduction of new financial technologies and financial instruments, expanding the scope of financial relations of the enterprise. Methods of financial risk assessment are investigated, which involve the identification of risks, determining the causes of their occurrence and potential risk areas, as well as the negative consequences of making a risky decision or possible benefits. Comparing the methods of quantitative assessment of financial risk, it was found that the level of risk may vary depending on the chosen method of assessment. A system of financial ratios has been formed to quantify the risk of enterprises of different sizes, namely for large, medium and small enterprises, which most accurately reflect the current financial situation. An assessment of the financial results of enterprises operating in the field of wholesale trade. The financial results before the taxation of activity as an indicator that characterizes the potential for profit generation of wholesale trade enterprises by size distribution are studied. The value of financial results (balance) before taxation of wholesale trade enterprises is forecasted. It is established that the growing pressure of the crisis and insecurity on the part of the state will lead to the transition from the crisis in which enterprises are today, to a state of economic bankruptcy. This situation requires the formation and use of financial resources of enterprises, based on the system and complexity of making and implementing management decisions. Quantitative and qualitative assessment of financial risks of wholesale trade enterprises belonging to groups of large, medium and small enterprises was carried out. Based on these calculations, the ranking of priorities in the management of financial stability. It is established that the first place among the blocks of financial coefficients of risk assessment of enterprises is occupied by the block of business activity, then the block of profitability indicators, the block of liquidity indicators, the final block of capital structure. For each of the defined blocks the directions of increase, or optimization that will provide financial stability of the enterprise are presented.

Research on the Application of Machine Learning Algorithms in Credit Risk Assessment of Minor Enterprises

Under the influence of COVID-19, minor enterprises, especially the manufacturing industry, are facing greater financial pressure and the possibility of non-performing loans is increasing. It is very important for financial institutions to reduce financial risks while providing financial support for minor enterprises to promote industrial development and economic recovery. In order to understand the function of machine learning algorithms in predicting enterprise credit risk, the research designs five models, including Logistic Regression, Decision Tree, Naïve Bayesian, Support Vector Machine and Deep Neural Network, and adopts SMOTE and Undersampling to process imbalanced data. Experiments show that machine learning algorithms have high accuracy for both large-scale data and small-scale data.

Occupational Health Risk Assessment of Benzene, Toluene, and Xylene in Shanghai

ObjectiveThis study was designed to conduct a retrospective and systematic occupational health risk assessment (OHRA) of enterprises that used benzene, toluene, and xylene (BTX) in Shanghai, China. MethodsAll data for the study were obtained from 1,705 occupational health examination and evaluation reports from 2013 to 2017, and a semiquantitative model following Chinese OHRA guidelines (GBZ/T 298-2017) was applied for the assessment. ResultsThe selected enterprises using BTX were mainly involved in manufacturing of products. Using the exposure level method, health risk levels associated with exposure to BTX were classified as medium, negligible, or low. However, the risk levels associated with benzene and toluene were significantly different according to job types, with gluers and inkers exhibiting greater health risks. For the same job type, the health risk levels assessed using the comprehensive index method were higher than those using the exposure level method. ConclusionOur OHRA reveals that workers who are exposed to BTX still face excessive health risk. Additionally, the risk level varied depending on job categories and exposure to specific chemicals. Therefore, additional control measures recommended by OHRA guidelines are essential to reduce worker exposure levels.

Dynamic risk assessment of oil and gas leakage in heating furnace: A DBT-DBN approach

The heating furnace has dynamic fluctuations of hazard factors in the actual circumstance and is the core equipment of hydrocracking unit, which is the main equipment of petrochemical enterprises. Owing to the limitation of conventional risk analysis approaches, a dynamic risk analysis approach based on dynamic Bow-tie model(short for DBT) and dynamic Bayesian network(short for DBN) was proposed. Taking the failure accident of oil and gas leakage of heating furnace as a case study, the timing sequence of equipment failure of heating furnace is analyzed first, the risk evolution process is obtained, and the dynamic tie model of accident is established. By mapping the dynamic tie model to the dynamic Bayesian network, the risk factors of oil and gas leakage in heating furnace are deduced in two-way reasoning. The dynamic risk trend of heating furnace was predicted eventually, the probability of oil and gas leakage caused by heating furnace is about 1.26E-07 after two year. Through diagnostic reasoning, 6 key factors and 3 accident paths were determined. The results are in accordance with the actual situation, proving that DBT-DBN model can effectively reflect the dynamic characteristics of accident scenarios and provide reference for the risk assessment of petrochemical enterprises.

Financial Risks in the Conditions of Unstable Market Economy

The necessity and significance of financial risks, which are a constant factor in the activity of any enterprise, are substantiated. It is established that for a broader understanding of the essence of financial risk it is necessary to consider it as a cumulative risk of the enterprise. The growing impact of financial risks on the results of financial and economic activities is associated with the uncertain economic situation, the instability of financial market conditions, the rapid introduction of new financial technologies and financial instruments, expanding the scope of financial relations of the enterprise. A system of financial ratios has been formed to quantify the risk of enterprises of different sizes, namely for large, medium and small enterprises, which most accurately reflect the current financial situation. Has been proven that the growing pressure of the crisis and insecurity on the part of the state will lead to the transition from the crisis in which enterprises are today, to a state of economic bankruptcy. It is established that the first place among the blocks of financial coefficients of risk assessment of enterprises is occupied by the block of business activity, then the block of profitability indicators, the block of liquidity indicators, the final block of capital structure. For each of the defined blocks the directions of increase, or optimization that will provide financial stability of the enterprise are presented.

CODAS methods for multiple attribute group decision making with interval-valued bipolar uncertain linguistic information and their application to risk assessment of Chinese enterprises’ overseas mergers and acquisitions

Bipolar fuzzy set theory has been successfully applied in some areas, but there are situations in real life which can’t be represented by bipolar fuzzy sets. However, all the existing approaches are unsuitable to describe the positive and negative membership degree an element to an uncertain linguistic label to have an interval value, which can reflect the decision maker’s confidence level when they are making an evaluation. In order to overcome this limit, we propose the definition of interval-valued bipolar uncertain linguistic sets (IVBULSs) to solve this problem based on the bipolar fuzzy sets and uncertain linguistic information processing models. In this paper, we extend the traditional information aggregating operators to interval-valued bipolar uncertain linguistic sets (IVBULSs) and propose some IVBUL aggregating operators. Then, we extend the CODAS method to solve multiple attribute group decision making (MAGDM) issues with interval-valued bipolar uncertain linguistic numbers (IVBULNs) based on these operators. An example for risk assessment of Chinese enterprises’ overseas mergers and acquisitions (M&As) is given to illustrate the proposed methodology.

Credit risk assessment of agricultural enterprises in the Republic of Serbia: Logistic regression vs discriminant analysis

Credit risk assessment of agricultural enterprises in the Republic of Serbia was analyzed in this research by applying discriminant analysis and logistic regressions. The aim of the research is to determine the financial indicators which financial analysts consider when analyzing a loan application that have the most influence on the decision to approve or reject a loan application. The internal determinants of credit risk of agricultural enterprises are analyzed, i.e., indicators of financial leverage, profitability, liquidity, solvency, financial stability and effectiveness. The analyzed models gave different results in significance of the observed indicators. The indicators that stood out as significant in both models are only indicators of profitability and solvency. The model of discriminant analysis has successfully classified rate 81.0%, while the logistic regression model has successfully classifies rate 89.8%. In modeling the credit risk of agricultural enterprises in the Republic of Serbia, the logistic regression model gives better results.