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The activities of the leading international credit rating agencies Standard&Poor’s (S&P), Moody’s and Fitch, peculiarities of their work and existing differences have been considered. The relevance of the study lies in the comparative characterization of the listed agencies in the current conditions of the anti-Russian sanctions expansion and their strengthening after the beginning of the special military operation in Ukraine. The purpose of the study is to compare the activities of the world’s leading rating agencies S&P, Moody’s and Fitch in modern conditions. The subject of the study is the peculiarities of these agencies’ activities and changes that have occurred in the conditions of the anti-Russian sanctions. To achieve the set goal the following tasks have been set: the leading credit rating agencies’ activities analysis, comparison of their methodological approaches and the scales used to assess the credit rating of countries. The conclusions about the place and important role of international credit rating agencies in the modern world financial system, differences in the assessments made by the agencies, the specific attitude of S&P, Moody’s and Fitch to the credit rating of Russia in the current conditions of the anti-Russian sanctions expansion and the special military operation have been presented.

Introduction. The impact of multidirectional external macroeconomic and regional factors of the economic environment in conditions of uncertainty and increased risks causes significant difficulties in diagnosing, assessing and forecasting the creditworthiness of financial and credit support recipients and borrowers (micro, small and medium-sized enterprises) in the Russian Federation. Theoretical analysis. The author systematized the basic mathematical methods and models for assessing and forecasting the level of creditworthiness of micro, small and medium-sized businesses in foreign and Russian practice, using modern systems and technologies of artificial intelligence and machine learning methods. Empirical analysis. The author proposed the results of approbation of methodological approach for express diagnostics of the financial and economic condition and forecasting the creditworthiness of SMEs in the Krasnodar krai for the period of 2014–2017, based on expert assessment methods, economic analysis and fuzzy logic systems, which form the credit rating of SMEs considering their industry affiliation. Results. In this study, the author has determined the advantages and disadvantages of methods and models for diagnosing creditworthiness and credit scoring from the perspective of their application for various categories of SMEs. As it is shown that the most promising and mathematically reliable models for credit scoring and risk assessment of financial support and lending to various enterprises in the SME sector at different stages of their life cycle both in Russia and abroad are computerized models and expert systems, based on such methods and technologies of Artificial Intelligence, as fuzzy logic systems, artificial neural networks, support vector machines, ensemble methods (random forest method), as well as intelligent information systems, hybrid models and hybrid systems. The study reveals that their combination with each other will allow to achieve synergistic and system effects in the interaction between lenders and borrowers (SMEs) and timely avoid their bankruptcy.

This study examines the existing credit rating methodology proposed in the literature to explore the development of a new credit rating model based on the financial variables of the enterprise. The focus is on the period after the financial crisis of 2018. This study aims to develop a credit rating model using neural networking and tests the same for its accuracy. The goal of this study is to address the issue brought up by previous research on subjectivity in the data used to determine creditworthiness. The database for the study includes financial data up to July 2022 from December 2018. A model is created to assess an enterprise's creditworthiness using neural networking. This study first evaluated the existing credit rating models proposed in the literature. Next, based on financial data and neural networking, a model is developed. It was evident that the model developed in this study has an accuracy of 85.16% and 76.47% on train and test data respectively. There exist several models to determine the creditworthiness of borrowers but all failed to address the concern of subjectivity in the data. The model created in this study made use of cutting-edge technology such as neural networking and financial data. This paper's unique approach and model construction based on a comparison of existing models is rare in the literature and justify the originality of this paper with a practical value at the global level.

This study examines the existing credit rating methodology proposed in the literature to explore the development of a new credit rating model based on the financial variables of the enterprise. The focus is on the period after the financial crisis of 2018. This study aims to develop a credit rating model using neural networking and tests the same for its accuracy. The goal of this study is to address the issue brought up by previous research on subjectivity in the data used to determine creditworthiness. The database for the study includes financial data up to July 2022 from December 2018. A model is created to assess an enterprise's creditworthiness using neural networking. This study first evaluated the existing credit rating models proposed in the literature. Next, based on financial data and neural networking, a model is developed. It was evident that the model developed in this study has an accuracy of 85.16% and 76.47% on train and test data respectively. There exist several models to determine the creditworthiness of borrowers but all failed to address the concern of subjectivity in the data. The model created in this study made use of cutting-edge technology such as neural networking and financial data. This paper's unique approach and model construction based on a comparison of existing models is rare in the literature and justify the originality of this paper with a practical value at the global level. [ABSTRACT FROM AUTHOR]

Nowadays, due to the worldwide crisis, there is a necessity to predict and make a prognosis for the future development of the companies from the view of their financial health. Mainly mining organizations in Slovakia face a problem with creditworthiness when a number of companies declared bankruptcy. The paper has the ambition to predict development in chosen Slovakian mining organization through the most used bankruptcy and bonity models, which brings results, serving for comparing with companies in time and sector. The results were evaluated according to the individual areas of financial position that influenced the development the most. Such results are profitable for the sustainable development of mining organizations together with providing the energetic independence of the countries. [ABSTRACT FROM AUTHOR]

This study investigates the effect of interest rates on credit access for small and medium-sized enterprises (SMEs) in South Africa. The study employs a quantitative research design, using data collected from 200 SMEs in South Africa. The data was analyzed using descriptive statistics, Pearson’s correlation coefficient analysis, and multiple regression analysis. An inverse relationship between interest rate and credit accessibility was found using the Pearson correlation coefficient (r = –.199, p < 0.05). The results show that interest rates have a significant negative effect on credit access for SMEs in South Africa. Moreover, the study finds that SMEs experience considerable obstacles in obtaining affordable credit, and that interest rates play a crucial role in this. The study recommends that policymakers in South Africa should consider reducing interest rates and relaxing collateral requirements to improve credit access in SMEs. Furthermore, the study suggests that SMEs should focus on building a good credit history to improve their creditworthiness and increase their chances of accessing credit. Overall, the findings of this study contribute to the existing literature on the effect of interest rates on credit access for SMEs and provide insights for policymakers and SME owners in South Africa.

In today's financial landscape, traditional banking institutions rely extensively on customers' historical financial data to evaluate their eligibility for loan approvals. While these decision support systems offer predictive accuracy for established customers, they overlook a crucial demographic: individuals without a financial history. To address this gap, our study presents a methodology for a decision support system that is intended to assist in determining credit risk. Rather than solely focusing on past financial records, our methodology assesses customer credibility by generating credit risk scores derived from psychometric test results. Utilizing machine learning algorithms, we model customer credibility through multidimensional metrics such as character traits and attitudes toward money management. Preliminary results from our prototype testing indicate that this innovative approach holds promise for accurate risk assessment. [ABSTRACT FROM AUTHOR]

The article considers the role of liabilities in the formation of assets used as resources of creditors for carrying out economic activities of an enterprise. A content analysis of the existing definitions of the content of liabilities in the legal and accounting aspects at the level of national and international regulations and in the scientific literature is carried out. Based on the terminological analysis, it is substantiated that a liability is a present obligation of an entity arising from past events to perform a certain action in favor of another party to transfer assets to it which embody economic benefits corresponding to the value of liabilities reflected as a credit balance of analytical accounting bills. The life cycle of an enterprise’s liabilities in terms of the processes of their formation, investment and repayment is studied. In order to assess the creditworthiness of an enterprise, it is important to properly account for liabilities and reflect them in the balance sheet in accordance with the classification features of determination in time and the characteristics of the subjects of relations. The author determines the types of liabilities that should be classified when assessing creditworthiness as an important factor in the economic security of an enterprise. The author substantiates the need to improve accounting and financial reporting on the company's liabilities in the context of streamlining the content of accounting bills in accordance with the first section of the balance sheet liability, changing the structural composition of information of accounts 47 «Provision for future expenses and payments» and 43 «Reserve capital», reflecting tax differences on income tax in the calculation of financial results and disclosing data on liabilities in the Notes to the financial statements in terms of the list and amounts of liabilities included in the balance sheet items.

Firm innovativeness and financing capacity are critical signals to stakeholders as they are key drivers of firm performance and competitiveness and indicate the firm's ability to fund its operations and growth initiatives. Based on signaling theory, this study investigates the signaling effect of a firm's innovativeness and creditworthiness and examines its signaling effectiveness. Using Korean innovation data and Korea Investors Service financial data for nine years, the findings indicate that a firm's technological innovation has a negative impact on its credit ratings, while non-technological innovation has a positive impact. Furthermore, a firm's credit ratings positively impact its performance. The current study contributes to the literature on signaling theory by exploring the signaling effect of a firm's innovativeness and creditworthiness. The findings provide insights for managers on how to send and monitor signals to stakeholders.

The proposal for the Artificial Intelligence regulation in the EU (AI Act) is a horizontal legal instrument that aims to regulate, according to a tailored risk-based approach, the development and use of AI systems across a plurality of sectors, including the financial sector. In particular, AI systems intended to be used to evaluate the creditworthiness or establish the credit score of natural persons are classified as “high-risk AI systems”. The proposal, tabled by the Commission in April 2021, is currently at the center of intense interinstitutional negotiations between the two branches of the European legislature, the European Parliament and the Council. Without prejudice to the ongoing legislative deliberations, the paper aims to provide an overview of the main elements and choices made by the Commission in respect of the regulation of AI in the financial sector, as well as of the position taken in that regard by the European Parliament and Council.

Purpose: The motivation of this study is to explore the significant determinants of consumers’ creditworthiness which support the development of a credit scoring model as non-performing loans are a major problem in lending institutions. Design/Methodology/Approach: Data were collected from four branches of a leading Commercial Bank in the Gampaha District under the convenience sampling technique with 130 personal loan borrowers as the study sample. Findings: The logit model test resulted that age, level of education, and monthly income, are positively influencing the creditworthiness of the borrowers. Increasing the number of dependents and the tenure of the loan have more chances of default. 39% to 56% of the dependent variable was explained by the independent variables in the regression model and the model predicted default correctly by 85.4%. Originality: The study contributes to the existing literature in terms of identifying important predictors for developing a credit-scoring model while helping lenders to assess the creditworthiness of personal loan applicants. Hence the study will assist in taking effectual measures to enhance the quality of the credit approval process and ultimately reduce the losses of lending institutions from bad debt.

This project focuses on automating the credit card application assessment process using advanced machine learning techniques, including Random Forest, Gradient Boosting, SVMs, Logistic Regression, Regularization Methods, and Hyperparameter Tuning. The objective is to improve the efficiency, accuracy, and fairness of credit card approval decisions. Historical credit card application data, comprising applicant demographics, financial history, and employment details, is collected and pre-processed. Feature engineering and exploratory data analysis (EDA) enhance the dataset’s predictive power. Three machine learning algorithms, Random Forest, Logistic Regression, and Gradient Boosting are applied. Regularization techniques (L1 and L2) and hyperparameter tuning are used to prevent overfitting and optimize model performance. The project assesses model performance by employing metrics such as accuracy, precision, recall, F1-score, and ROC-AUC metrics, and conducts feature importance analysis to identify key factors influencing approval decisions. The project aims to deliver robust, accurate, and fair credit card approval models, benefitting both financial institutions and applicants

The subject of the research are the companies of the IT sector, as a strategically important sector in the information age. Their development of companies in the IT sector is associated with high risks and requires large volumes of investments, including attracting bank loans. In this regard, the purpose of the study was to develop an adequate sectoral methodology for rating companies in the IT sector by the level of creditworthiness risks using mathematical and statistical tools that make it possible to reliably assess the potential risks of investors. To achieve this goal, the study proposes a methodology for assessing the creditworthiness of IT companies based on a system of risk factors, which makes it possible to quantify the exposure of companies to two generalized risk groups: financial risk and business risks. Based on the cluster analysis, a rating table has been developed, according to which, depending on the calculated score, the category of the company’s creditworthiness is determined. The study concluded that the key factors affecting the creditworthiness of companies are: indicators of financial stability, return on assets, liquidity ratio, online advertising market size, as well as the share of intangible assets in the structure of assets and the amount of research costs. development and capital investments. The constructed scoring model was tested on the Mail.ru Group company (from 12.10.2021 — VK). Practical significance of the research results includes in the fact that the developed model can be applied not only for assessing creditworthiness, but also as one of the express methods of risk management in an organization.

In today’s financial landscape, traditional banking institutions rely extensively on customers’ historical financial data to evaluate their eligibility for loan approvals. While these decision support systems offer predictive accuracy for established customers, they overlook a crucial demographic: individuals without a financial history. To address this gap, our study presents a methodology for a decision support system that is intended to assist in determining credit risk. Rather than solely focusing on past financial records, our methodology assesses customer credibility by generating credit risk scores derived from psychometric test results. Utilizing machine learning algorithms, we model customer credibility through multidimensional metrics such as character traits and attitudes toward money management. Preliminary results from our prototype testing indicate that this innovative approach holds promise for accurate risk assessment.

This article considered the problem of determining the creditworthiness of an enterprise operating in the field of trade and services. The assessment of the creditworthiness of borrowers, particularly small businesses, needs to be more careful: the level of development of small enterprises and their specific activities must be considered, as well as the uncertainty in obtaining any financial result. A method for assessing the creditworthiness of enterprises (trade and services) is proposed, based on the use of the mathematical apparatus of the theory of fuzzy sets. This article analyzes the indicators of industry and regional specifics, indicators of the activity of a small enterprise, and financial and economic indicators typical for the service sector and trade. The rules on the basis of which decisions are made are formed in the form of logical formulas containing parameters. In its most general form, one parameter is predicted, called the creditworthiness index, which varies from 0 to 1 and has a natural interpretation. On the basis of the proposed method, examples of calculating the assessment of the creditworthiness of enterprises operating in the field of trade and services are given. The proposed scientific approach can be used as a basis for creating expert decision support systems for lending to small businesses. [ABSTRACT FROM AUTHOR]

Aim: To analyze the accuracy of Support Vector Machine (SVM) algorithms over Logistic Regression (LR) used to approve bank loans. Materials and Methods: The existing model uses a Logistic Regression algorithm and the proposed model employs a Novel Support Vector Machine. The 20 sample values are used to find out the mean, std. deviation, std. error means. The sample size was measured as 40 for both the groups using G power (80%). Results: The resultant graph explains the comparison of the mean accuracy values of algorithms Novel Support Vector Machine and Logistic Regression where the mean accuracy of the Support Vector Machine is about 69.5% and the mean accuracy value of the Logistic Regression is about 66.5%. The independent sample T-test shows that p=1.0, since p>0.05 there exists insigniface between the SVM and LR. Conclusion: The mean accuracy rate of the Novel Support Vector Machine algorithm has been improved to 69.5% compared to Logistic Regression which is having around 66.5% mean accuracy. [ABSTRACT FROM AUTHOR]

It is well known that in the Croatian economy very little attention is paid to business analysis and business risk management. Also, incompetence and inadequate expertise of top management, as well as superficial and insufficiently professional supervision of business by companies' owners, continuously generate problems with liquidity, over-indebtedness, and losses within Croatian companies. Failure to (timely) address these problems is in essence the cause of a large number of bankruptcies and liquidations of business entities, which ultimately produces serious problems for the Croatian economy in general. In this paper, business analysis and the analysis of business risks of the Uljanik d.d. group. during the period from 2016. to 2017. will be evaluated in order to determine the creditworthiness of the company at that time, respectively before the announcement of the company's bankruptcy in 2019. This paper will try to determine whether the difficulties in business could be promptly identified and whether the company's management structure in the mentioned period could react faster and more decisively based on the assessment of the company's creditworthiness and the knowledge gained during the analysis. The optimal choice of financial and qualitative indicators was used in the evaluation of creditworthiness by which it is possible to make a more reliable assessment of creditworthiness taking into account internal and external factors that affect the company's operations. In the used credit score model, the final credit rating is determined by synthesis and common use of financial and qualitative indicators and provides comprehensive and credible information about the business entity and its business risks and in that way allows the correct business decisions to be taken on time.

The study analyses the changes in financing the purchase of residential real estate based on a mortgage in Poland, caused by the outbreak of the COVID-19 pandemic in its initial stage, that is, the period from March to September 2020. The research problem is the impact of the pandemic on the process of granting and repaying loans for housing purposes. Banking institutions tightened the terms of granting financing. People who received financing earlier may, however, benefit from assistance in paying off the debt. The research hypothesis assumes the negative consequences of the COVID-19 pandemic for the consumers of the housing loan market in the analysed period. The research methods used are the analysis of statistical data and facts. The author looks at the situation on the real estate market and the situation on the housing loan market in 2020. The study then highlights the actions of lending banks and the actions of buyers in the first pandemic months.

In order to develop the present article, we selected a transport entity listed on the Bucharest Stock Exchange and financially diagnosed over three consecutive years, from 2018 to 2020, through structural ratios, creditworthiness indicators (liquidity and solvency) and, last but not least, financial balance indicators.

Creditworthiness assessment was one of the first areas to apply machine learning techniques in economics. The creditworthiness of retrofit protection was vital for ESCO in determining the credit scoring. This study aimed to develop a retrofitting assessment model to utilize machine learning with multinomial logistic (MNL) and life cycle cost analysis (LCCA). This study aims to provide an evaluation of creditworthiness models from the evaluation of financing alternative in Indonesia's energy efficiency industry. The goal was to reduce the total of prediction error, which comprised bias, variance, and fundamental error. The findings demonstrated that machine learning approaches might yield significantly greater prediction accuracy. In addition, machine learning is also expected to automatically capture the nonlinear relationship between input features and selected results. This study is also expected to draw on ideas from machine learning to develop an enhanced model for retrofitting creditworthiness research and suggest new research directions.

Copyright of Ruch Prawniczy, Ekonomiczny i Socjologiczny (0035-9629) is the property of Adam Mickiewicz University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Approval of credit lending to cooperative members without proper feasibility analysis can cause credit problems, cooperatives such as late payment of installments, and an increase in bad credit which can threaten the survival of the cooperative as a provider of lending services. As a solution to minimize the creditworthiness assessment errors for loan funds, research is carried out to analyze the feasibility of loan funds from the data of cooperative members using the data mining method approach and the algorithm used using the K-Nearest Neighbor. The purpose of this research is to predict the feasibility of granting credit with the right decision and to find out the level of evaluation, accuracy, and validation of the effectiveness of the k-NN algorithm on processing creditworthiness application data classifications. After the prediction research was carried out, the data on the eligibility of credit lending applications were conducted at the Bakti Berkah Sukaraja Savings and Loan Cooperative, The data obtained from the accuracy value of the k-nearest neighbor algorithm before being validated has an accuracy of 87.78% with AUC 0.95, after validation with split validation the accuracy decreased slightly by 2% to be 85.71%, while the AUC value in the ROC Curve was 0.836%. Even though there was a decline, it can still be categorized as a good classification. The impact of this research is that besides the accuracy of the k-NN algorithm being validated, the Bakti Berkah Sukaraja Savings and Loan cooperative can predict the feasibility of applying for credit funds, as an effort to reduce the threat of bad credit risk

Credit rating agencies have a significant impact on reducing asymmetric information between lenders and borrowers. The purpose of this paper is to provide a comprehensive analysis of the role of credit rating agencies in the capital market, how to play this role and the effects of these institutions on the economy. For this purpose, using a library-based method, the first step is to review the research background in this field. Then, by presenting the historical development of rating agencies and the concept of credit ratings, and evaluating the need for these institutions, we focused on the general realms of influencing credit rating agencies on the economy. These areas included domestic and foreign investment, market efficiencies, corporate competition, economic justice, and meritocracy. in section 5, we examined how these institutions influence the economy. In addition, with respect to concepts like asymmetric information and transparency, the impact of credit rating agencies on these two subjects was discussed, and finally the conclusion was presented. The results show that with the existence of credit rating agencies, because of the reduction of asymmetric information and the ease of analyzing the creditworthiness, investing will be increased, and as a result, we'll see the growth of production, employment, export, and economic welfare.

This article highlights a brief financial diagnosis of a transnational company, defined by visibility, transparency, credibility and performance. At the same time, such a company is characterized by a structural diversity in terms of wealth and performance. Among the aspects considered there is the creditworthiness, which is the sum of the liquidity and the solvency, the profitability, which is a reflection of the financial performance, and also the cash flow as an actual expression of the financial balance and the efficiency of the activity.

Native communities in the United States have been persistently underserved by traditional financial institutions. To fill this gap, in recent years both Native and Non-Native Community Development Financial Institutions (CDFIs) have emerged on or near American Indian reservations. Yet to date, no comprehensive evidence exists on the effect of CDFIs on credit outcomes in Indian Country. We combine a large-scale dataset on individual-level credit bureau records with Census block group-level measures of CDFI activity to explore how the presence of Native and Non Native CDFIs affects Indian Country residents' credit risk scores, a key summary measure of individuals' credit performance and creditworthiness. Using multiple empirical approaches and addressing endogeneity concerns, we uncover a positive association between Native CDFI presence and credit risk scores for the subsample of individuals initially deemed least creditworthy. We do not find consistent evidence of a similar effect due to Non-Native CDFIs. To the extent that CDFIs have indeed causally impacted individuals' credit risk scores, our findings indicate that the corresponding improvements have arisen primarily because of Native, rather than Non-Native, CDFI activity. Our analysis thereby offers the first systematic empirical evidence suggestive of the importance of a cultural fit for credit outcomes in Indian Country. [ABSTRACT FROM AUTHOR]

Copyright of Polish Statistician / Wiadomości Statystyczne is the property of State Treasury - Statistics Poland and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bioeconomics & Agrarian Business / Bìoekonomìka ta Agrarnij Bìznes is the property of National University of Life & Environmental Sciences of Ukraine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recent increase in peer-to-peer lending prompted for development of models to separate good and bad clients to mitigate risks both for lenders and for the platforms. The rapidly increasing body of literature provides several comparisons between various models. Among the most frequently employed ones are logistic regression, Support Vector Machines, neural networks and decision tree-based models. Among them, logistic regression has proved to be a strong candidate both because its good performance and due to its high explainability. The present paper aims to compare four pairs of models (for imbalanced and under-sampled data) meant to predict charged off clients by optimizing F1 score. We found that, if the data is balanced, Logistic Regression, both simple and with Stochastic Gradient Descent, outperforms LightGBM and K-Nearest Neighbors in optimizing F1 score. We chose this metric as it provides balance between the interests of the lenders and those of the platform. Loan term, debt-to-income ratio and number of accounts were found to be important positively related predictors of risk of charge off. At the other end of the spectrum, by far the strongest impact on charge off probability is that of the FICO score. The final number of features retained by the two models differs very much, because, although both models use Lasso for feature selection, Stochastic Gradient Descent Logistic Regression uses a stronger regularization. The analysis was performed using Python (numpy, pandas, sklearn and imblearn).

The article is aimed at developing a methodical approach to the integral assessment of the financial status of communal enterprises using the multidimensional statistical analysis method, since a comprehensive assessment of the activities of communal enterprises in the industry, which top the rating, will allow to use their achievements to solve the problems of enterprises-outsiders. It is concluded that the methods of calculating integral indicators presented in the Regulations developed by the NBU and the Ministry of Finance of Ukraine do not adequately reflect the true financial status and level of creditworthiness of enterprises. An approach to the integral assessment of the financial status is proposed, which involves building up a model of an integral index of the financial status of the enterprise; introduction and improvement of the information system for monitoring the financial-economic activities of enterprise; organization of a continuous comprehensive assessment and monitoring the main indicators that characterize the enterprise’s financial potential. The main stages of building up an integral index model using the main component method are described. An integral index, which characterizes the financial status of communal enterprises, is calculated and an industry rating of water supplying communal enterprises is compiled, allowing to respond in a timely manner to the deterioration of financial potential in the development of anti-crisis management solutions for enterprises-outsiders. Further research suggests improving the model through repeated testing for communal enterprises in various industries. This will allow to determine the final set of indicators to form the integral index.

Municipal bonds are an option available to many cities to raise long-term financing to finance the infrastructure projects. This study aims to develop and find local measures of creditworthiness that are suitable and applicable for local government units in Palestine. Different variables are recognized to see the effect on the issuance of municipal bonds, macroeconomic variables measured by revenues and expenditures per capita, cost of capital, and unemployment rate. Municipal status variables have subgroup variables of municipality size, financial reporting quality, outstanding debt, and financial distress. Various financial ratios, comparative and cross-sectional analysis, horizontal and vertical analysis were used. These ratios and analysis have been used to determine the municipal status variable. The results of the study were limited to the largest 11 sample municipalities; each is the central local government unit at the governorates and was not generalized for all municipalities in Palestine. The study found that macroeconomic and municipality status affects the issuance of municipal revenue bonds. Based on the study results, municipal bonds are highly recommended. Also, instructions from the Ministry of Local Government need to be established and to enforce municipalities about the declaration date of publishing audited financial statements.

Currently in the domestic economy, a significant role is played by bank lending, which provides commercial organizations with the means to expand and develop their business. Crediting is the basis of the bank’s activity, its main income item. Lending contributes to increasing the economic potential of organizations, accelerating the turnover of resources. Thus, lending to legal entities contributes to the economic growth of the whole country.

This article considered the problem of determining the creditworthiness of an enterprise operating in the field of trade and services. The assessment of the creditworthiness of borrowers, particularly small businesses, needs to be more careful: the level of development of small enterprises and their specific activities must be considered, as well as the uncertainty in obtaining any financial result. A method for assessing the creditworthiness of enterprises (trade and services) is proposed, based on the use of the mathematical apparatus of the theory of fuzzy sets. This article analyzes the indicators of industry and regional specifics, indicators of the activity of a small enterprise, and financial and economic indicators typical for the service sector and trade. The rules on the basis of which decisions are made are formed in the form of logical formulas containing parameters. In its most general form, one parameter is predicted, called the creditworthiness index, which varies from 0 to 1 and has a natural interpretation. On the basis of the proposed method, examples of calculating the assessment of the creditworthiness of enterprises operating in the field of trade and services are given. The proposed scientific approach can be used as a basis for creating expert decision support systems for lending to small businesses.

The main issue for all creditors in the Republic of Uzbekistan is how to effectively lend to companies with an adequate increase of the rate of return. Specifically, commercial banks use traditional credit analysis methods, which are very slow, expensive and straightforward, or use one of the world's most developed models for assessing a company's creditworthiness, which is still not adapted to the characteristics and performance of the company national business ventures. From a professional and practical standpoint, it is emphasized that the models for assessing a company's creditworthiness developed for the individual market give significantly better results than other "standardized models". It represents the basic assumption of this research. The financial statements of national companies, together with their analytical indicators, in the best way, reflect their characteristics. As such, they are the basis for developing a model for assessing companies' creditworthiness, which is the main goal of the research. The hypothesis of the paper is "Respecting the characteristics of national companies and current procedures of creditworthiness assessment. It is possible to develop a creditworthiness analysis model for the Republic of Uzbekistan that will be more efficient than generally known models". The result of the research is a model that can be practically applied, which contributes to increased efficiency in decision making, which can meet the requirements of the digital economy. [ABSTRACT FROM AUTHOR]

The actual scientific research of development of information models of representation of fuzzy knowledge at an estimation of creditworthiness of the enterprises of the coal industry of Ukraine and perfection of a fuzzy mathematical model of an estimation of creditworthiness of the enterprises is carried out. Based on settheoretic analysis, a set of 11 criteria for assessing the creditworthiness of enterprises is formed and divided into 3 groups: indicators of financial stability, analysis of profits and losses, the efficiency of enterprise management. A membership function is constructed for each criterion, which will reveal the uncertainty in the input data, leading to a normalized form for comparison. To construct membership functions, a study was conducted to determine their type and type, as well as the parameters of membership functions based on the experience of credit experts and data sets on indicators, using real financial reports of Ukrainian coal industry enterprises for 2020. The improved fuzzy mathematical model for assessing the creditworthiness of enterprises, which reveals the vagueness of input data, derives the assessment of creditworthiness based on the reasoning of the decision-maker (DM), determines the linguistic level of ability to repay financial obligations on time. Based on the built model, a general six-step algorithm is constructed, which can be quickly implemented in the software product. The developed information model and the improved fuzzy mathematical model were tested on real data of credit assessment of the Lvivugol State Enterprise of Ukraine. Outcome, models, and approaches to presenting fuzzy knowledge for indicators of creditworthiness assessment of Ukrainian coal industry enterprises, which will be a model for other countries to follow. [ABSTRACT FROM AUTHOR]

• We present a new method to explore the contagion mechanism among the same bank customers. • We detect the presence of critical clients for the propagation of external shocks. • This is useful in improving the information set in assessing customers' creditworthiness. • Our method is based on a weighted, directed complex network. • We employ, as an example, a unique dataset that considers customers' mutual transactions. The analysis of monetary flows' correlation resulting from clients' mutual transactions is crucial for small/local banks in assessing customers' creditworthiness. This paper offers a new method based on a complex network (customers are the nodes and their mutual financial flows the links). We detect the presence of vulnerable and dangerous clients within the contagion and propagation of external shocks mechanisms and exploit the informative content of the in- and out-paths of the network, with specific reference to those associated with the geodesic patterns. We test the model over a high-quality dataset referred to 2021. The results might support banks' customers' creditworthiness analysis. [ABSTRACT FROM AUTHOR]

Finance and indebtedness remain major problems for most countries, especially developing countries it therefore requests to stand in front of them to reduce them and prevent their exacerbation, Global rating agencies are a reflection of the urgent need for investors and borrowers to identify factors affecting creditworthiness to bridge the existing information gap. Iraq was approved for the period 2004-2017 using a model (Toda Yamamota Causility) The desire to provide information on the level of risk before lending to the government or financial and banking institutions to assess the solvency and the desire to meet debts to meet current and future financial obligations in a timely manner, and the degree of rating is not an absolute fact, but expect to the current financial situation may err and may infect, However, creditworthiness indicators remain the most important criteria for assessing the likelihood of the borrower not repaying the loan amount. The researcher concluded that the most important variable affecting the sovereign creditworthiness of Iraq is debt service, especially in the short term On the other hand, the availability of liquidity gives great flexibility to Iraq in the payment of its obligations.

The article is devoted to the development of the methodology for assessing the creditworthiness of exporting enterprises based on the fuzzy logic method. The author studies the dominant foreign methods of creditworthiness complex analysis of potential bank borrowers. On its basis the stages of rapid analysis and the basic factors determining the creditworthiness of potential borrowers of the bank, the main for which are export activity, are distinguished. These factors include: FEA factor (term of export activity, share of profit from FEA in operating profit of enterprise, dynamics of profitable activity from FEA), borrower’s reputation (quality of management), borrower's financial condition (profit activity, solvency, net cash flow), loan collateralization (type and possibility of realization of collateral, method of credit risk insurance), loan repayment ability (essential terms of the loan agreement: purpose, term, loan amount, interest rate and fees, etc.). These factors allowed to create a model, the feature of which is the use of expert and analytical information to predict the level of creditworthiness of potential bank borrowers, the main one for which is export activity. The output parameter Z obtained in the model makes it possible to assess the creditworthiness of the exporting borrower as following: H - high level of creditworthiness, A – average (acceptable) creditworthiness, L – low level of creditworthiness. The loan officers must reject the borrower’s loan application in the case of obtaining a low level of creditworthiness during the express analysis. In case of assigning an average or high level of creditworthiness, a further in-depth analysis of the borrower’s creditworthiness is required. The use of express analysis makes it possible to avoid the loss of time and other resources for a detailed analysis of the creditworthiness of potentially insolvent borrowers.

The purpose of the article is to reveal the peculiarities of a comprehensive analysis of the financial sustainability of a production enterprise, namely that which could become the basis of optimization decisions. It has been determined that according to traditional approaches to the analysis of financial stability, an entity is not able to assess its available reserves for development, under conditions of acceptable level of risk (that is, with the provision of an optimal level of financial stability). In the article the features of complex analysis of the financial stability of a production enterprise have been disclosed, namely, that which can become the basis of optimization decisions. The outline is achieved at the expense of the following research tasks: the definition of the variables that contribute to the complex analysis of the financial sustainability of the production enterprise; characteristic of changing properties of the internal state of financial stability of the enterprise and the peculiarities of their analysis. The author's approach is to provide requirements for the complex analysis of the financial stability of a production enterprise based on the use of its variables, namely: solvency, creditworthiness, financial resources. In the study, coefficient methods and methods of balance-sheet agreement, within the organization of data processing and the comparison of systems interrelated variables of the properties of financial sustainability of the enterprise have been applied. According to the results, it has been proved that the ball form of concretization of variables in the analysis of financial stability quite effectively summarizes the results concerning: the limits of the use of liquid assets; conditions that determine the financial capacity of the entity; level of provision of assets and liabilities. This approach distinguishes the possibility of compiling a portrait of financial sustainability of the enterprise, as an abstract description of its main properties. The practical significance of the results lies in the possibility of their use for the development of optimization solutions, the formation of a forecast scenario of optimization (within the formalization of the target function of the process of optimization changes). Further research will be aimed at adapting the results of the research to the practical activities of manufacturing enterprises in the region

Today, the main source of economic growth is the extraction of mineral oil, although in recent years there has been a significant increase in the role of consumer demand in the domestic market. In the con-text of globalization, the main indicator of the economy of oil-producing countries is oil prices. However, in a period of globalization, developing countries need not only energy, but also economic resources to develop their economic situation on the global market. Thus, establishing creditworthiness is the key to attracting foreign capital. The development of the stock market is not an indicator of the country’s stable creditworthiness. This study fills the required gap, verifying with the extent to which the development of the stock market can affect creditworthiness. Moreover, the following article has strong literature review in order to identify and summarize the main factors of the creditworthiness of oil-producing countries. Although, there is not strong empirical research, the study is based on the statistical world data.

The article analyzes the current state of the credit portfolio of domestic banks in the context of introduction of new approaches of the National Bank to assess the financial status of the debtor – a legal entity. Ukraine implements the recommendations of the Basel Committee on Banking Supervision on Credit Risk Assessment, which recommends banks to develop their own internal models in order to properly assess their clients. However, the National Bank of Ukraine has chosen another concept for the settlement of this recommendation, in particular, it developed and obliged all domestic banks to use their own methodology for assessing the level of credit risk based on a specific econometric model. Even before the full implementation of the methodology of the National Bank in practice of domestic banks, it was critically evaluated by domestic scientists with the details of deficiencies. The conducted study allowed to analyze the relationship between the introduction of the new NBU methodology for assessing the level of credit risk on corporate loans and changes in the share of non-performing loans in loan portfolios of Ukrainian banks. The obtained results confirmed the low effectiveness of the NBU methodology for identifying insolvent borrowers, as in the background of the growth of lending to legal entities in 2018, the share of non-performing loans was almost unchanged, or insignificantly, indicating that the newly issued loans subsequently went to the category of unemployed. The study summarizes the shortcomings of the current methodology and proposes to differentiate the approaches of banks to assessing the level of credit risk, namely, to allow banks with a long-term functioning in the market to develop and approve in the NBU their own methods for assessing the level of credit risk of the borrower, and for newly created banks – to use the methodology of the NBU until the moment of accumulation of the necessary experience to develop their own methodology.

The article is devoted to the development of methodology for assessing the level of credit risk of borrowers-legal entities. The authors analysed the current methodology for determining the credit risk size under active banking operations used by Ukrainian banks. By analysing the statistical data on the share of problem loans in the loan portfolios of Ukrainian banks, it was established that the implementation of this methodology did not lead to a significant change in the share of problem loans in the banking system, and, consequently, needs further improvement. The article proposes the development of an approach to credit, which at the methodological level should be expressed through a set of interrelated quantitative and qualitative indicators, which in their unity determine the degree of creditworthiness of a potential borrower. The authors suggest taking into account the factor of foreign economic activity, as having a significant impact on the financial state of the enterprise, when calculating the integral indicator of the financial status of the debtor – a legal entity by including the indicator «the share of operating profit from foreign economic activities to depreciation in the total volume of sales». In addition, in the conditions of stable tendency of hryvnia devaluation, ranges of values of the debtor PD coefficient – the legal entity-exporter is proposed to be taken into account for the minimum values, and the legal entity-importer – for maximum values in determining the size of credit risk on an asset on an individual basis. The submitted offers will allow to adjust the level of credit risk of the borrower-legal entity that carries out foreign economic activity, depending on the importance of its impact on the financial result of the enterprise.

Although banks have been conducting credit lending activities for centuries by using traditional credit risk assessment methods, the scientific art of credit risk management has evolved over the last few years along with the Basel agreement. The risks to which banks are being exposed when they fund societies keen on creative accounting techniques are considerable, the misleading attempts and the "production" of financial statements for a successful image in front of the bank in order to convince with a view to obtaining funding are often common practice. The consequences are particularly severe for the financing institution that will mark losses on the transaction concluded on the basis of these glossed over economic realities. That is why it is very important to look beyond this picture, to compare the image presented by the client with images of other reporting periods in order to identify changes in the accounting records techniques. It is best to understand the film of the last 2 or 3 years to grasp the reality of a society and not just the image that it has been thoroughly prepared to be shown to the bank in order to get credits.

The main issue for all creditors in the Republic of Serbia is how to effectively lend to companies, with adequate increase of rate of return. Namely, domestic creditors are using traditional credit analysis methods, which are very slow and very expensive, or on the other hand using one of the world most developed models for assessing company's creditworthiness, which are still not adapted to the characteristics and performance of domestic companies. It is emphasized from a professional and practical standpoint, that the models for assessing company's creditworthiness developed for individual market, give significantly better results than other "standardized models", and this represents the basic assumption of this research. Financial statements of domestic companies, together with their analytical indicators, in best way reflect their characteristics. As such, they are the basis for developing a model for assessing companies' creditworthiness, which is the main goal of the research. The hypothesis of the paper is "Respecting the characteristics of domestic companies, it is possible to develop a creditworthiness analysis model for the Republic of Serbia, that will be more efficient than generally known models". The result of the research is a model that can be practically applied, which contributes to increasing efficiency in decision making, and which should represent "condition sine qua non" of each lender in Serbia.

The article is devoted to the approaches to analyze the solvency of companies debtors. The system of indicators and ratios is proposed to analyze the financial position of companies debtors.The subject of credit analysis is closely related to the problems of effective management of the company. Thequality of credit analysis depends on how close the relationship between a company and its stakeholders is. Mutually beneficial relationships ensure the control over the quality of products and the pricing process. Building relationships with customers implies the achievement of an acceptable level of customer satisfaction and increases the efficiency of client base management. The key to the success of credit analysis is the initial grouping of companies debtors to identify those objects of analysis relationship which differ from the pattern. The method of analysis of creditworthiness of debtors is presented in two versions: full (integrated) and abridged (express diagnostics). The author proposes to use the complex of measures to control the status of accounts receivable. Managers of the company face the problem of formalizing the debtor analysis procedures, regulating the work with receivables, including the possibility of their adaptation to the internal and external business environment changes.

Objective: The objective of this paper is to verify the hypothesis that there is a statistically significant correlation between the risk level determined on the basis of structural models and the value of the of debt capacity base, and that the value of a company’s debt capacity is determined primarily by the type and properties of its financing. Research Design & Methods: The methodology was based on the analysis of the determination of the linear regression function using the least squares method and study of the correlation between the values of the debt capacity base and the net value of enterprises (determined on the basis of the approach used in structural risk models) based on accounting data of 511 companies listed on the Warsaw Stock Exchange in 2018–2019. This includes an analysis of the level of debt capacity in the context of selected forms of financing. Findings: There is a strong and statistically significant correlation between the debt capacity base determined on the basis of book values and the determined net value of enterprises, representing the level of structural risk (constituting the difference between the value of assets and liabilities). A USD 1 bn change in the average debt capacity base leads to a USD 0.49 bn change in the average net worth of enterprises. Implications / Recommendations: The designated regression function enables forecasting, within the scope of banking practice, the value of the structural risk and the debt capacity base in terms of granting short- and long-term liabilities. Contribution: The study confirms the thesis that there is a statistically significant correlation between structural risk and the debt capacity base. It presents an approach that enables the determination of the debt capacity base, the value of structural risk, and the value of debt capacity for selected forms of financing.

Creditworthiness is acknowledged worldwide as focal point of debt processing. Credit Rate, an output of credit-rating process, reflects such creditworthiness. We reviewed literature on elements of credit rating, viz. Credit Rate, credit-rating agencies (CRAs) and credit-rating model. Credit Rating is an independent evaluation of creditworthiness. CRAs perform this evaluation to support financial institutions in processing debts. Literature added in credit-rating domain from January 2001 to November 2020 is analyzed to explore the opportunities for further research. Bibliometric analysis is used to comprehend the existing literature. Subsequently, through structured review theories, methodologies used by researchers and CRAs are explored. Further, a hybrid literature review is developed by integrating bibliometric and structured review of research papers from widely recognized databases. A sample of 153 papers is constructed and studied to identify gaps in credit-rating domain and to develop suitable remedy to fill such gaps. We found that most of the studies emerged as after-effects of financial crisis reported in 2008 and 2016. The review revealed that 48% studies focused on development of new credit-rating mechanism without evaluating existing structure in-depth. This paper contributes to existing literature by encouraging researchers and CRAs to develop a sector-specific credit-rating system by evaluating existing models and improvising them by adopting advanced techniques like Multiple Regression, Neural Networking and Artificial Intelligence. We have provided a feasible research agenda to further explore credit-rating domain. In this study we have identified that the factors determining creditworthiness are different for different sectors.

This article politicises the discourse of emerging markets in global finance. The black-boxed appearance of credit markets easily obscures the significant amount of subjective evaluation and cultural work that underpins capital flows. This article reveals the colonial, masculine, and racial imagination that informs the articulation of emerging markets as geographies of risk and profit. This brings into view the postcolonial nature of contemporary finance and how colonialism's regimes of power and knowledge remain crucial for the reproduction of the global political economy. To illustrate this point, the article highlights the sociality of credit practices. Contrary to their mathematical appearance, credit is a relationship with the future, mediated by social imaginations of trust. Focusing on emerging markets as 'risk-versus-reward' investments, this article examines the long-term colonial histories embedded in modern investment discourses. The article aims to show the continuing relevance this history plays for emerging market economies in modern financial markets and their political economies. [ABSTRACT FROM AUTHOR]

The purpose of the article is to develop a methodological approach to support the decision-making process in determining the creditworthiness of legal entities, as well as to create economic mathematical models based on this approach using the theory of fuzzy logic and fuzzy sets. Methodology. In the author's work (Palamarchuk, 2013), 49 real financial statements (Form 1 and Form 2) of Ukrainian enterprises were selected, 25 of which were potentially bankrupt and 24 were normally operating enterprises. As a result, 7 coefficients were obtained. Here we continue our modelling and building rule base. Result of the experiment is based on statistical data of domestic enterprise. The model has been constructed with the use of theory of fuzzy logic. Considering the expert knowledge, this model helps to make decisions on whether to provide the legal entity with the loan. Practical implications. The model and methodology can be used in commercial banks of Ukraine for calculating application risks. The known models do not fit to every economy. This is the reason which provides value originality of the topic of this study, which solves the problem of creating a method that would give the most sufficient assessment of creditworthiness.