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Copyright of Journal of Economic Administrative & Legal Sciences is the property of Arab Journal of Sciences & Research Publishing (AJSRP) 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.)

This article provides an overview of modern machine learning methods in the context of their active use in credit scoring, with particular attention to the following algorithms: light gradient boosting machine (LGBM) classifier, logistic regression (LR), linear discriminant analysis (LDA), decision tree (DT) classifier, gradient boosting classifier and extreme gradient boosting (XGB) classifier. Each of the methods mentioned is subject to careful analysis to evaluate their applicability and effectiveness in predicting credit risk. The article examines the advantages and limitations of each method, identifying their impact on the accuracy and reliability of borrower creditworthiness assessments. Current trends in machine learning and credit scoring are also covered, warning of challenges and discussing prospects. The analysis highlights the significant contributions of methods such as LGBM classifier, LR, LDA, DT classifier, gradient boosting classifier and XGB classifier to the development of modern credit scoring practices, highlighting their potential for improving the accuracy and reliability of borrower creditworthiness forecasts in the financial services industry. Additionally, the article discusses the importance of careful selection of machine learning models and the need to continually update methodology in light of the rapidly changing nature of the financial market. [ABSTRACT FROM AUTHOR]

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.

Lower-income consumers who seek to increase their perceived social status or to emulate their wealthier peers may be motivated to purchase conspicuous luxury goods. Using a vehicle financing dataset, we find that non-prime consumers value vehicle prestige more than the average consumer. The stronger preferences for prestige lead non-prime consumers to purchase more expensive vehicles than they otherwise would. The preferences for prestige are driven both by status signaling and peer emulation motives. Furthermore, we show that larger vehicle purchases financed by auto loans lead to worse loan performance and credit standing for non-prime consumers. [ABSTRACT FROM AUTHOR]

As iii many other areas of financial consumer law, the European Court of Justice heralds a strong protective approach to the financier's duty to assess the creditworthiness of the consumer. But enhancing deterrence above all by means of contract law does not set afair and efficient balance between the quite diverse incentives of both parties in adjusting to the policy. [ABSTRACT FROM AUTHOR]

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.

Nowadays people are increasingly inclined to buy motorized vehicles because in addition to a light down payment, finance companies also provide convenience for the public in purchasing motorized vehicles. Even though the income level of the people in Indonesia is still relatively low, with a low down payment, the people are not too concerned about it. Honda showrooms carry out various forms of promotion and marketing so that the vehicles they sell get a response from consumers so they want to buy them. However, in fact there are still many forms of promotion that are not appropriate for consumers, so that in the motor vehicle loan process there are often obstacles caused by various factors. This study aims to create an analyst that can later be applied to computer systems, so that it can be said that by testing the system based on existing criteria, it will provide a definite answer in determining the creditworthiness of motorcycles to consumers. [ABSTRACT FROM AUTHOR]

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]

Copyright of Revista Română de Drept European is the property of Wolters Kluwer Romania 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.)

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.

Copyright of Pandectele Române is the property of Wolters Kluwer Romania 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.)

This article examines the engagement of North Korea in the Eurocurrency market in the 1970s. In the Cold War regime competition for economic prosperity, the communist regime turned to the City of London to raise capital for economic development. Despite the diplomatic efforts of South Korea against its rival, the judges were international banks. The failure to manage its indebtedness resulted in the retreat of the North in the Western financial market. Lost creditworthiness was hard to restore. The divergence in the access to foreign capital resulted in the different paths of economic growth of the two Koreas. [ABSTRACT FROM AUTHOR]

The academic thesis analyses the obligations that are connected with the topic of assessing the consumer's ability to repay a consumer loan. The author interprets the obligations in assessing the consumer's ability to repay the loan in the sense of the current de lege lata legislation, which is supplemented by the current case law. The chosen topic is set in the context of Directive 2008/48/EC of the European Parliament and of the Council of 23 April 2008 on credit agreements. The author presents arguments indicating the inadequate implementation of individual directives and decisions of the Court of Justice in the courts' decision-making. [ABSTRACT FROM AUTHOR]

In most countries, the main financing source for local governments for infrastructure projects comes from the central government’s budget. However, considering the growing urban population, this source will be insufficient to meet the likely increasing infrastructure expenditures in the future as it is now. Local governments must explore alternative financing methods, including accession to capital markets. Nevertheless, most local governments, especially the small and medium-sized ones, do not have access to capital markets, due to their low creditworthiness and insufficient technical and administrative capacity. For this reason, some supportive mechanisms to increase the creditworthiness of local governments within the scope of the conventional financial system have been developed in many countries. Aspired by the well-proven methods of the conventional financial system, this paper proposes some supportive financing mechanisms for local governments based on Islamic financial principles. These mechanisms could help local governments to access capital with higher creditworthiness and contribute to the development of Islamic capital markets. In order to lower the borrowing costs for local governments by giving them direct or indirect access to capital markets, this study suggests five possible Islamic financing methods. However, because to the novelty of the subject, it attempts to lay the groundwork for future research that will yield fresh perspectives on this issue. [ABSTRACT FROM AUTHOR]

How was Romania able to borrow cheaply on the international capital markets before World War I? We explore this within the context of its three southeast European neighbours, Bulgaria, Greece and Serbia, using a novel dataset of monthly bond prices from the Berlin and London stock exchanges. A comparison of country characteristics and panel data analysis suggests that Romania was able to borrow under more favourable conditions due to its abundant natural resources and desirable exports. [ABSTRACT FROM AUTHOR]

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.

From roughly 1988 to 2007, global banks faced strong regulatory incentives to lend to members of the OECD and those of the IMF's General Arrangements to Borrow (GAB), along with disincentives to lend to countries that belonged to neither of these groups. The culprit was the Basel Accord, also known as Basel I, a piece of banking regulation designed by the Basel Committee on Banking Supervision (BCBS) to regulate global banks' capital levels. Why did the BCBS create a 'club' of riskless sovereign borrowers, and why did it use OECD and GAB membership to do so? Relying on the archives of the BCBS, I first argue that the Basel Committee chose to design a club in response to European Community (EC) policies, which threatened a number of non-EC states on the Committee. Second, I argue that the BCBS chose to define the group through OECD membership because its members embodied a key set of criteria that it associated with creditworthiness. I conclude by outlining the implications of these findings for our understanding of the BCBS, as well as for our models of how central banks construct international hierarchies. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Economic Administrative & Legal Sciences is the property of Arab Journal of Sciences & Research Publishing (AJSRP) 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.)

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.

Just as status generates inequalities between organizations and individuals, it also structures relations among states in the global field. In an effort to elevate their status vis‐à‐vis peers, states pursue a range of status‐seeking strategies. This article asks whether states that invest more heavily in these strategies are rewarded by relevant global audiences and receive tangible material benefits. To examine this question, I study two prominent status displays—bidding to host the Olympic Games and holding a temporary seat on the United Nations Security Council (UNSC)—and analyze their relationship with upgrades in sovereign credit ratings. I find that Olympic bidders and UNSC members are 10% more likely to receive a rating upgrade within two years. Further analyses reveal that these rewards disproportionately accrue to "overachievers" who are distinct from the typical members of these elite clubs: large, upper‐middle‐income states for Olympic bidding and small states for the UNSC. To explain these findings, I propose that in a highly uncertain market environment status displays bolster impressions of states' creditworthiness by signaling a commitment to international cooperation and obligations. The study contributes to our understanding of how status shapes global inequalities, and demonstrates that status‐seeking actions yield concrete material rewards. [ABSTRACT FROM AUTHOR]

Just as status generates inequalities between organizations and individuals, it also structures relations among states in the global field. In an effort to elevate their status vis‐à‐vis peers, states pursue a range of status‐seeking strategies. This article asks whether states that invest more heavily in these strategies are rewarded by relevant global audiences and receive tangible material benefits. To examine this question, I study two prominent status displays—bidding to host the Olympic Games and holding a temporary seat on the United Nations Security Council (UNSC)—and analyze their relationship with upgrades in sovereign credit ratings. I find that Olympic bidders and UNSC members are 10% more likely to receive a rating upgrade within two years. Further analyses reveal that these rewards disproportionately accrue to "overachievers" who are distinct from the typical members of these elite clubs: large, upper‐middle‐income states for Olympic bidding and small states for the UNSC. To explain these findings, I propose that in a highly uncertain market environment status displays bolster impressions of states' creditworthiness by signaling a commitment to international cooperation and obligations. The study contributes to our understanding of how status shapes global inequalities, and demonstrates that status‐seeking actions yield concrete material rewards. [ABSTRACT FROM AUTHOR]

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.

Copyright of Quantitative Methods in Economics / Metody Ilościowe w Badaniach Ekonomicznych is the property of Warsaw University of Life Sciences, Faculty of Applied Informatics & Mathematics 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.)