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Journal articles on the topic 'Assessment of credit risk'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Gustafson, Cole R., Glenn D. Pederson, and Brent A. Gloy. "Credit risk assessment." Agricultural Finance Review 65, no. 2 (2005): 201–17. http://dx.doi.org/10.1108/00214660580001173.

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2

Hamraevich, Tashmatov Shuhrat. "ASSESSMENT OF CREDIT RISK OF A COMMERCIAL BANK." International Journal Of Management And Economics Fundamental 3, no. 12 (2023): 92–97. http://dx.doi.org/10.37547/ijmef/volume03issue12-16.

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This article presents a method for assessing the risks of commercial banks, mainly analyzing the credit risk, interest rate risk and capital risk faced by commercial banks. A model for assessing the credit risk of commercial banks in Uzbekistan is also reviewed.

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3

LARIONOVA, K., and T. DONCHENKO. "ANALYSIS AND ASSESSMENT OF CREDIT RISK OF BANKS OF UKRAINE." Herald of Khmelnytskyi National University. Economic sciences 278, no. 1 (2020): 233–40. https://doi.org/10.31891/2307-5740-2020-278-1-41.

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The negative consequences of the financial crisis, political instability, which significantly weakened the banking system of Ukraine, revealed the unwillingness of most banking institutions to promptly and adequately adjust credit policy to find the optimal balance between customer needs for credit resources, lending risks, liquidity requirements, collateral requirements credit funds of business entities with real assets, etc. The processes of internationalization and globalization in the financial market exacerbate the need to reassess the role and place of credit risk management of banking i

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Redondo, Helena, and Elisa Aracil. "Climate‐related credit risk: Rethinking the credit risk framework." Global Policy 15, S1 (2024): 21–33. http://dx.doi.org/10.1111/1758-5899.13315.

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AbstractClimate change and the challenges associated with the transition to a zero‐carbon economy pose significant financial risks. Climate‐related risks (CRR) indirectly impact banks through their loan portfolios. To examine the integration of CRR into banks' credit risk assessment and monitoring, this article reviews academic and institutional literature using quantitative bibliometric techniques and content analysis of 145 academic documents from policymakers and financial supervisors. A framework emerges that incorporates CRR into credit risk management. We find four thematic areas in the

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Xie, Xiaofeng, Fengying Zhang, Li Liu, Yang Yang, and Xiuying Hu. "Assessment of associated credit risk in the supply chain based on trade credit risk contagion." PLOS ONE 18, no. 2 (2023): e0281616. http://dx.doi.org/10.1371/journal.pone.0281616.

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Assessment of associated credit risk in the supply chain is a challenge in current credit risk management practices. This paper proposes a new approach for assessing associated credit risk in the supply chain based on graph theory and fuzzy preference theory. First, we classified the credit risk of firms in the supply chain into two types, namely firms’ “own credit risk” and “credit risk contagion”; second, we designed a system of indicators for assessing the credit risks of firms in the supply chain and used fuzzy preference relations to obtain the fuzzy comparison judgment matrix of credit r

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Luo, Na, Jiayi Yang, Yuanfeng Zhu, and Yu Zhang. "The Risk Management of Commercial Banks——Credit-Risk Assessment of Enterprises." International Journal of Economics and Finance 8, no. 9 (2016): 69. http://dx.doi.org/10.5539/ijef.v8n9p69.

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With the diversified developments of the financial market, commercial banks are confronted with various risks, among which the credit risk is the core, and thus the assessment of enterprises’ credit risks is especially important in the credit process of the commercial banks. Based on the relevant researches about commercial banks’ credit risk management, the paper carries out a deep analysis on the factors that may affect the credit risk assessment and then establishes a relatively comprehensive credit risk assessment system. In this paper, we apply our risk assessment model, which is establis

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Moolchandani, Sanjay. "Advanced Credit Risk Assessment Using Markov Chain Monte Carlo Techniques." International Journal of Science and Research (IJSR) 12, no. 12 (2023): 2160–63. http://dx.doi.org/10.21275/sr23127095329.

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Черниченко, Светлана, Svetlana Chernichenko, Роман Котов, Roman Kotov, Светлана Гильмулина, and Svetlana Gilmulina. ". Experimental synthetic approach to segment assessment of aggregate credit risk." Food Processing: Techniques and Technology 48, no. 1 (2019): 184–89. http://dx.doi.org/10.21603/2074-9414-2018-1-184-189.

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Multifaceted, multifactor and multicomponent nature of credit risk makes it possible to consider it as an integral hypothetical unit which consists of the autonomous diverse segments specifying risky situations. As the given article is focused on the mechanism of loan fund circulation within foreign currency loan the author considers the combination of credit, interest rate, foreign exchange and inflation risks within the aggregate (total, combined) credit risk. Foreign exchange and inflation risks generate special interest in relation to evaluation procedures as there can be statutory regulat

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Barth, Mary E., Gaizka Ormazabal, and Daniel J. Taylor. "Asset Securitizations and Credit Risk." Accounting Review 87, no. 2 (2011): 423–48. http://dx.doi.org/10.2308/accr-10194.

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ABSTRACT This study examines the sources of credit risk associated with asset securitizations and whether credit-rating agencies and the bond market differ in their assessment of this risk. Measuring credit risk using credit ratings, we find the securitizing firm's credit risk is positively related to the firm's retained interest in the securitized assets and unrelated to the portion of the securitized assets not retained by the firm. Measuring credit risk using bond spreads, we find the securitizing firm's credit risk is positively related to both the firm's retained interest in the assets an

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10

Arunkumar, Paramasivan. "Revolutionizing Credit Risk Assessment AI in Card Transaction Analytics." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 6, no. 1 (2020): 1–9. https://doi.org/10.5281/zenodo.14551643.

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This Article examines the transformative impact of artificial intelligence (AI) on credit risk assessment within card transaction analytics. By harnessing AI's advanced capabilities in data analysis and pattern recognition, financial institutions can now conduct more nuanced and accurate evaluations of an individual’s creditworthiness, identify potential defaults, and detect early signs of financial distress in real time. Traditional credit scoring models, previously limited by static data and periodic assessments, are significantly enhanced by AI’s ability to analyze vast transact

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11

M., Moses Antony Rajendran. "Credit Risk Management and Insurance Practices - An Overview." Journal of Research in Business, Economics and Management 2, no. 2 (2015): 89–96. https://doi.org/10.5281/zenodo.3965327.

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In this article it mentioned about credit risk, explosion of credit risk, credit risk raisers, inclusion of credit risks, default probability of credit risks, Evaluation Factors Credit Risks, Altman’s Z Score of credit scoring, Credit Rating, Functions of Credit Ratings, Benefits of credit instruments, Disadvantages of credit rating, Types of credit rating, Sovereign Vs. Corporate Credit Rating, Credit Risk Management & Techniques and Principles for the Assessment of Banks’ Management of Credit Risk.

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12

Chatzoglou, Prodromos D., Ioannis Eleftheriades, and Evdokia Tsifora. "Credit risk assessment: a field research." International Journal of Economic Policy in Emerging Economies 2, no. 4 (2009): 372. http://dx.doi.org/10.1504/ijepee.2009.030938.

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13

Bonsall, Samuel B., Eric R. Holzman, and Brian P. Miller. "Managerial Ability and Credit Risk Assessment." Management Science 63, no. 5 (2017): 1425–49. http://dx.doi.org/10.1287/mnsc.2015.2403.

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14

Pinson, Suzanne. "Credit risk assessment and meta-judgment." Theory and Decision 27, no. 1-2 (1989): 117–33. http://dx.doi.org/10.1007/bf00133991.

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15

Louangrath, P., and W. Vongsilpawattana. "Credit Risk Assessment for Commercial Loans." Inter. J. Res. Methodol. Soc. Sci 2, no. 3 (2016): 58–73. https://doi.org/10.5281/zenodo.1321652.

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The purpose of this research is to provide practical tool for credit evaluation of firms by using cash flow statements as the basis. A predictive function for firm credit score is introduced using the expected cash flow adjusted for internal and external state effects.  The data used in this research came from 10 publicly traded companies in SP500 for in-sample testing. Out-of-sample test was accomplished by an additional 10 companies from NASDAQ. We assert that the Cash Flow Base (CFB) distribution method as a tool for commercial loan assessment is inadequate. This research introduces th

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16

Naumenkova, Svitlana, Ievgen Tishchenko, Svitlana Mishchenko, Volodymyr Mishchenko, and Viktor Ivanov. "Assessment and mitigation of credit risks in project financing." Banks and Bank Systems 15, no. 1 (2020): 72–84. http://dx.doi.org/10.21511/bbs.15(1).2020.08.

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Lending to long-term investment projects in fragile countries requires additional financial instruments to control the sustainability of project cash flows and to increase the borrower’s financial discipline in debt servicing. This paper analyzes the special aspects of using financial covenants as credit risk mitigation instruments in project financing in Ukraine. It also argues that regulatory requirements to maintain financial strength indicators at the appropriate level have an indirect impact on the change in project finance loan rates. The study primarily aims at developing approaches to

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17

Yang, Zhen. "Utilization of Quantization Method on Credit Risk Assessment." Applied Mechanics and Materials 472 (January 2014): 432–36. http://dx.doi.org/10.4028/www.scientific.net/amm.472.432.

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Credit risk assessment is critical factor in credit risk management, which has played a key role in financial and banking industry. Many classification methods are used in credit risk assessment aiming to establish classifiers to predict the credit state of the corporate (good or bad). However, most of classification methods can not handle continuous variables. So, continuous variables must be quantified. In this paper, we first propose an improved quantization method, namely IDM, based on the statistical independence; then we use data mining techniques, i.e., C4.5 decision tree, Naive-Bayes a

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18

Singh, Puneet, Shubha Mishra, Gargi Porwal, Prakhar Saxena, and Rishabh Tripathi. "Credit Risk Model: Research on Credit Risk Categorization model using XGBoost." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43005.

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Machine Learning is a subset of Artificial Intelligence technology that enables systems to learn and make decisions on their own. These systems can make accurate decisions by analyzing datasets and information without the need for explicit programming. This paper mainly introduces the application of machine learning algorithm (XGBoost) in credit risk assessment in the financial industry. Credit risk assessment is a significant challenge for banks to assess credit worthiness among many applicants and plays a very crucial role in the profitability of banks. Our research paper addresses the limit

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19

Iyer, K. C., and Dhruba Purkayastha. "Credit Risk Assessment in Infrastructure Project Finance:Relevance of Credit Ratings." Journal of Structured Finance 22, no. 4 (2017): 17–25. http://dx.doi.org/10.3905/jsf.2017.22.4.017.

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20

Kuang, Xianhua, Chaoqun Ma, and Yi-Shuai Ren. "Credit risk: A new privacy-preserving decentralized credit assessment model." Finance Research Letters 67 (September 2024): 105937. http://dx.doi.org/10.1016/j.frl.2024.105937.

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21

Lustro, Roman Alex F. "Credit Risk Assessment for Motorcycle Loan Application: A Fuzzy Logic Approach." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12-SPECIAL ISSUE (2019): 1246–52. http://dx.doi.org/10.5373/jardcs/v11sp12/20193332.

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22

Chaudhary, Khyati, and Gopal Chaudhary. "A Decision Support System for Credit Risk Assessment using Business Intelligence and Machine Learning Techniques." American Journal of Business and Operations Research 10, no. 2 (2023): 32–38. http://dx.doi.org/10.54216/ajbor.100204.

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Credit risk assessment is a critical task for financial institutions to determine the creditworthiness of their potential customers. Business intelligence (BI) and machine learning (ML) techniques have gained popularity in recent years as effective tools for credit risk assessment. In this paper, we propose a decision support system (DSS) for credit risk assessment that integrates BI and ML techniques. The proposed DSS employs BI tools to extract and transform data from various sources, and ML techniques to analyze the data and generate predictive models for credit risk assessment. We evaluate

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23

Poudel, Shiva Raj. "ASSESSMENT OF CREDIT RISK IN NEPALI COMMERCIAL BANKS." Journal of Applied and Advanced Research 3, no. 3 (2018): 65. http://dx.doi.org/10.21839/jaar.2018.v3i3.137.

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The main objective of the study is to identify the major indicators of credit risk among the Nepali commercial banks. The study is conducted using the sample of 15 commercial banks operated in Nepali economy. One way Fixed Effect Model (FEM) of panel data analysis is used as a major tool of analysis. All the data for the study were obtained from the database of Nepal Rastra Bank for bank specific variables and database of World Bank for macroeconomic variables for the year 2002/03 to 2014/15. The credit risk among the commercial banks in Nepal was regressed on bank specific variables such as l

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24

Hrytsenko, Larysa, Liudmyla Pavlenko, Iryna Kozhushko, and Onur Erişen. "COMPREHENSIVE CREDIT RISK ASSESSMENT OF UKRAINIAN BANKS." Socio-economic relations in the digital society 2, no. 56 (2025): 20–33. https://doi.org/10.55643/ser.2.56.2025.605.

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Credit risk is an integral element of banking activities, the identification and effective management of which is essential in today’s environment. The financial stability of both individual banks and the banking sector as a whole depends on the feasibility and effectiveness of risk management. The purpose of the study is to systematise the approaches approved at the state level to assessing the credit risk of banks, analyse the current state of the loan portfolio of the Ukrainian banking sector, and identify the most promising methods of optimising potential risks to its quality.The study exa

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25

Ferreira, Fernando A. F., Ieva Meidutė-Kavaliauskienė, Edmundas K. Zavadskas, Marjan S. Jalali, and Sandra M. J. Catarino. "A Judgment-Based Risk Assessment Framework for Consumer Loans." International Journal of Information Technology & Decision Making 18, no. 01 (2019): 7–33. http://dx.doi.org/10.1142/s021962201850044x.

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Credit to personal consumption is an important activity of the financial system and crucial to the socioeconomic development of a country. It is important, therefore, that the methods and techniques used to evaluate consumer credit risk be as efficient and informative as possible, in order to strengthen decisions to approve or reject credit and promote sustainable economic growth. This study aims to create a multiple criteria expert system which integrates cognitive maps and the measuring attractiveness by a categorical-based evaluation technique (MACBETH) to create a complementary framework f

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26

Yan, Gai. "Research on the Application of Alternative Data in Credit Risk Management." Highlights in Business, Economics and Management 40 (September 1, 2024): 1156–60. http://dx.doi.org/10.54097/vn32pp64.

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With the development of financial technology, traditional credit assessment models have gradually shown their limitations. Especially in assessing borrowers with no credit history or weak credit records. The rise of alternative data provides a new dimension for credit risk prediction, including but not limited to social media behavior, online transaction records, geographic location data, etc. This paper explores the current application status, challenges, and future development trends of alternative data in personal credit risk assessment, and explores the application and effects of various f

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Merkulova, Inna V., Natalya V. Wiederker, Sofya V. Akhanova, and Anastasia S. Udodova. "ASSESSMENT OF THE BORROWER’S CREDIT RISK USING SCORING." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 1/13, no. 154 (2025): 135–43. https://doi.org/10.36871/ek.up.p.r.2025.01.13.015.

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With the growth of lending volumes, the credit risks faced by banks and financial institutions are also increasing. Therefore, managing these risks is of paramount importance. Scoring, as one of the main methods of assessing creditworthiness, plays an important role in the credit risk management process. Currently, credit institutions automate scoring using artificial intelligence technologies, thereby increasing customer loyalty.

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Azharbayeva, A. Y., та M. Kh Abdinova. "УПРАВЛЕНИЕ КРЕДИТНЫМИ РИСКАМИ АО «НАРОДНЫЙ БАНК»: ПРОБЛЕМЫ И ПУТИ РЕШЕНИЯ". INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGIES 4, № 3(15) (2024): 8–23. http://dx.doi.org/10.54309/ijict.2023.15.3.001.

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The purpose of the research work is to reveal the essence of the credit risk system, study the causes of its occurrence, and conduct a detailed analysis of the loan portfolio and credit risk management methods on the example of the commercial bank "Halyk Bank of the Republic of Kazakhstan" JSC. The authors analyze the loan portfolio of the bank using CAMELS, Z - score methods and build a regression analysis using R-studio, identify the main problems in credit risk management in a commercial bank and propose some solutions. In this article such problems in credit risk management as incomplete c

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Mohammed Azmath ulla khan, Mohammad Musaddiq, Mohammed Faizan, and Heena Yasmeen. "Ai Based Credit Scoring System With Dynamic Risk Assessment." International Journal of Information Technology and Computer Engineering 13, no. 2s (2025): 425–34. https://doi.org/10.62647/ijitce2025v13i2spp425-434.

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Credit cards are now potentially the most popularmode of payment for both offline and onlinepurchases thanks to new developments inelectronic commerce systems andcommunication technology; as a result, there ismuch more fraud involved with suchtransactions. Every year, fraudulent credit cardtransactions cause businesses and individuals tolose a lot of money, and con artists are constantlylooking for new tools and techniques to commitfraud. Researchers face a difficult task whentrying to identify credit card theft since criminalsare quick-thinking and inventive. The datasetprovided for credit ca

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30

Yamanaka, Suguru. "Credit risk assessment using purchase order information." International Journal of Financial Engineering 05, no. 04 (2018): 1850041. http://dx.doi.org/10.1142/s242478631850041x.

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This paper proposes advanced credit risk assessment and lending operations using purchase order information from borrower firms. Purchase order information from a borrower firm is useful for financial institutions to evaluate the actual business conditions of the firm. This paper shows the application of purchase order information to lending operations and credit risk assessment, and reveals its effectiveness. First, we propose a “purchase order based” credit risk model for real-time credit risk monitoring of firms. Financial institutions can monitor the actual business conditions of borrower

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Tarissa Seshita Hadi and R. Yuniardi Rusdianto. "Mekanisme Penilaian Pemberian Kredit Dalam Meminimalisir Risiko Kredit Pada Bank BJB KCP Mojokerto." Journal of Management and Social Sciences 3, no. 1 (2024): 47–58. http://dx.doi.org/10.55606/jimas.v3i1.971.

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Bank BJB KCP Mojokerto which is located on Jl Majapahit No. 333C-333D, Kranggan, Mojokerto City is one of the banks that provides credit facilities for the public. This research aims to determine the credit assessment mechanism of Bank BJB KCP Mojokerto in minimizing credit risk. This research uses qualitative methods with observation, interview and documentation techniques. The research results show that bank BJB KCP Mojokerto has implemented good credit assessment using the 5C principles with character and capacity as the bank's main assessment in minimizing credit risk. This research sugges

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Niklis, Dimitrios, Michalis Doumpos, and Constantin Zopounidis. "Credit Risk Modelling." International Journal of Sustainable Economies Management 7, no. 3 (2018): 50–64. http://dx.doi.org/10.4018/ijsem.2018070105.

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The assessment of businesses' credit risk is a difficult and important process in the area of financial risk management. In a classical multivariate model, financial ratios are combined in order to achieve a credit risk score, which signals if a loan application is approved or discarded. Despite their good performance, the developed multivariate models using statistical methods have been widely criticized. They are based on models that use accounting data, which have the disadvantage of being static and so often fail to follow the changes in the economic and business environment. In recent yea

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Zong, Zihe. "Credit Risk Assessment Model Based on XGBoost." Advances in Economics, Management and Political Sciences 193, no. 1 (2025): 170–79. https://doi.org/10.54254/2754-1169/2025.lh24569.

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Against the backdrop of uncertainty increasing global economic, Credit risk has become one of the core risks faced by financial institutions, enterprises and individuals. This study focuses on credit risk assessment in the field of financial risk and introduces machine learning models to address issues such as poor model interpretability and imbalanced data in existing models. By processing the German credit dataset in various aspects, including handling missing values, label encoding, standardization, data balancing, and analyzing the basic characteristics of the data to discover the advantag

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34

Oline, Emma. "Role of Project Risk Assessment on Performance of Credit Products at Bank of Africa, Rwanda." Journal of Entrepreneurship & Project management 7, no. 12 (2023): 69–81. http://dx.doi.org/10.53819/81018102t4211.

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The study examined the role of project risk assessment on performance of credit products at the Bank of Africa in Rwanda. The study was guided by the following specific objectives: To determine the contribution of technical feasibility on performance of credit product at Bank of Africa, to establish the contribution of project financial viability on performance of credit product at Bank of Africa and to evaluate the contribution of client’s credit rating on performance of credit product at Bank of Africa. This study applied descriptive design to get results related to the study, the target pop

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35

Li, Yixuan, Charalampos Stasinakis, and Wee Meng Yeo. "A Hybrid XGBoost-MLP Model for Credit Risk Assessment on Digital Supply Chain Finance." Forecasting 4, no. 1 (2022): 184–207. http://dx.doi.org/10.3390/forecast4010011.

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Supply Chain Finance (SCF) has gradually taken on digital characteristics with the rapid development of electronic information technology. Business audit information has become more abundant and complex, which has increased the efficiency and increased the potential risk of commercial banks, with credit risk being the biggest risk they face. Therefore, credit risk assessment based on the application of digital SCF is of great importance to commercial banks’ financial decisions. This paper uses a hybrid Extreme Gradient Boosting Multi-Layer Perceptron (XGBoost-MLP) model to assess the credit ri

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36

Krichene, Aida. "Using a naive Bayesian classifier methodology for loan risk assessment." Journal of Economics, Finance and Administrative Science 22, no. 42 (2017): 3–24. http://dx.doi.org/10.1108/jefas-02-2017-0039.

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Purpose Loan default risk or credit risk evaluation is important to financial institutions which provide loans to businesses and individuals. Loans carry the risk of being defaulted. To understand the risk levels of credit users (corporations and individuals), credit providers (bankers) normally collect vast amounts of information on borrowers. Statistical predictive analytic techniques can be used to analyse or to determine the risk levels involved in loans. This paper aims to address the question of default prediction of short-term loans for a Tunisian commercial bank. Design/methodology/app

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Pradhan, Shreya, and Ajay K. Shah. "Credit Risk Management of Commercial Banks in Nepal." Journal of Business and Social Sciences Research 4, no. 1 (2019): 27–37. http://dx.doi.org/10.3126/jbssr.v4i1.28996.

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The study is primarily focused on credit risk assessment practices in commercial banks on the basis of their internal efficiency, assessment of assets and borrower. The model of the study is based on the analysis of relationship between credit risk management practices, credit risk mitigation measures and obstacles and loan repayment. Based on a descriptive research approach the study has used survey-based primary data and performed a correlation analysis on them. It discovered that credit risk management practices and credit risk mitigation measures have a positive relationship with loan repa

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Zhang, Haiyan, Zhe Guo, and Yingying Sun. "Analysis of Bank Customer Default Risk Based on Embedded Microprocessor Wireless Communication." Security and Communication Networks 2022 (March 17, 2022): 1–11. http://dx.doi.org/10.1155/2022/5635152.

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Bank personal credit is affected by factors such as inadequate management and lagging risk information management system. Bank default risk analysis is needed to improve the ability of bank credit risk management. Therefore, a bank customer default risk analysis based on embedded microprocessor wireless communication is proposed. Firstly, it analyzes the risk assessment parameter evaluation system of personal credit, constructs the quantitative analysis model of personal credit risk, calculates the grade gradient value in the bank’s personal credit risk standard, carries out the mathematical m

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39

GERASYMENKO, S., and H. HOLUBOVA. "Statistical Assessment of Risks of Banking Activity." Scientific Bulletin of the National Academy of Statistics, Accounting and Audit, no. 3-4 (December 31, 2022): 5–14. http://dx.doi.org/10.31767/nasoa.3-4-2022.01.

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The authors summarized the classification of risks by their types: external and internal (functional and financial). It was noted that to the extent of the influence of external factors (legislation, the level of socio-economic development of the country, the behavior of investors, market conditions, the stability of the national currency exchange rate, etc.), it is practically impossible to assess the magnitude of external risks. Financial risks were described and it was determined that three categories of risk are the main among them: market, credit and operational. In the management o

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40

Každailienė, Jūratė, and Dalia Daujotaitė. "Assessment of trade credit risk in business companies." Buhalterinės apskaitos teorija ir praktika, no. 15A (July 9, 2014): 133–48. http://dx.doi.org/10.15388/batp.2014.15a.11.

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The article‘s topic is relevant, because the importance of trade credit constantly increases. Trade credit can be one of the most important preconditions for the competitiveness of the enterprise and business development. There is a lack of scientific sources in the field of estimation of trade credit risk – there is no any particular, simple to use methodology to assess company trade credit risk.  The aim of the article – to compose a methodology of assessment company trade credit risk for Lithuanian small and medium enterprises. In the article, following scientific sources approa

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Guan, Shuyin, Yuang Hu, and Ziyi Xu. "Research on the Influencing Factors of Corporate Credit Default Risk." Highlights in Business, Economics and Management 35 (June 16, 2024): 130–35. http://dx.doi.org/10.54097/69k2d202.

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This paper takes corporate financial indicators and the impact of corporate credit risk as the research object. This paper firstly introduces and analyses the relevant literature on enterprise financial indicators and credit risk, then classifies and analyzes the financial indicators of enterprises, and establishes the assessment model of enterprise financial indicators and credit risk on this basis. By analyzing the financial indicators of enterprises, this paper finds that the financial indicators of enterprises, such as quick ratio, cash flow position and net interest, have an important imp

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42

Glazkova, G., and L. Hanova. "Credit Risk Assessment During the Audit of Commercial Bank Statements." Auditor 8, no. 1 (2022): 42–48. http://dx.doi.org/10.12737/1998-0701-2022-8-1-42-48.

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The lending process is one of the fundamental functions of a commercial bank. Lending is inextricably linked to credit risk. Credit risk is the most significant among all the risks of credit institutions, since it is the lending process that is the largest source of income for a credit institution. That is why it is important to be able to timely identify potential credit risks and minimize their impact on banking processes.

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Shakurov, Andrey A., and Valery P. Slanov. "MODERN APPROACHES TO CREDIT RISK ASSESSMENT IN BANKING." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 12/15, no. 153 (2024): 175–80. https://doi.org/10.36871/ek.up.p.r.2024.12.15.020.

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This scientific article is dedicated to defining current approaches to credit risk analysis within the framework of banking activities. The objectives of the research include: analyzing the concepts of credit risk and default; reviewing the recommendations of the Basel Committee; characterizing the Internal Ratings-Based (IRB) approach; and evaluating the Value at Risk (VaR) methodology. The authors employed general scientific research methods: deduction, induction, generalization, comparative analysis, synthesis (integration), study, and theoretical analysis. Minimizing credit risks is a prio

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Jin, Ruixin, and Huanyu Zhou. "Data analysis with different variables and credit risk assessment." Applied and Computational Engineering 32, no. 1 (2024): 275–84. http://dx.doi.org/10.54254/2755-2721/32/20230863.

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Nowadays, credit payment is a very common way to pay, such as credit cards, loans, many people can use their credit as a guarantee to borrow money from the bank, however some people will default. So we have to predict whether the borrower will pay on time, it is known as credit risk assessment. In this paper, we analyze a data set on credit risk to predict whether individuals will be late on their payments, helping financial firms improve their earnings and reduce their losses. We not only made predictions on the data, but also analyzed the relationship between the variables that affect the ov

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Wang, Qiping, Raymond Yiu Keung Lau, Wai Ting Eric Ngai, Jason Bennett Thatcher, and Wei Xu. "Consumers’ Opinion Orientations and Their Credit Risk: An Econometric Analysis Enhanced by Multimodal Analytics." Journal of the Association for Information Systems 25, no. 4 (2024): 1117–56. http://dx.doi.org/10.17705/1jais.00856.

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The rise of financial technology (fintech) has motivated practitioners and researchers to explore alternative data sources and enhanced credit scoring methods for better assessment of consumers’ credit risk. In this study, we examine whether deep-level diversity derived from consumers’ multimodal social media posts (i.e., alternative data) can enhance credit risk assessment or not. First, we propose novel lifestyle-based risk constructs (e.g., opinion risk) to capture consumers’ deep-level diversity. Second, we incorporate these lifestyle-based risk constructs into econometric models to empiri

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Martini, Tri, Husaini Husaini, and Novita Sari. "CREDIT RISK MANAGEMENT AT BANK BENGKULU MAIN BRANCH DURING COVID-19 PANDEMIC." JURNAL FAIRNESS 10, no. 1 (2021): 29–42. http://dx.doi.org/10.33369/fairness.v10i1.15230.

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This research purpose is to describe credit risk management at Bank Bengkulu during covid-19 pandemic. This research is a descriptive study that focuses on the phenomenon of activity, namely credit risk management activities done by credit analysts, branch vice leaders and branch leaders. Data collection is done through observations, interviews and documentation that are intended to test each other on the information obtained. This research concluded that credit risk management conducted by Bank Bengkulu main branch is credit risk identification, credit risk measurement and assessment, credit

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Dewi, Yuli, Harry Suharman, Poppy Sofia Koeswayo, and Nanny Dewi Tanzil. "What is the key determinant of the credit card fraud risk assessment in Indonesia? An idea for brainstorming." Banks and Bank Systems 18, no. 1 (2023): 26–37. http://dx.doi.org/10.21511/bbs.18(1).2023.03.

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This study examined the direct effect of brainstorming on fraud risk assessment at credit card issuing banks in Indonesia. Therefore, it was expected to help improve their performance in dealing with various credit card frauds. This study involved 80 participants from the credit card fraud risk management team from four major credit card issuing banks in Indonesia, consisting of the risk management team (anti-fraud specialist) and the internal auditor team. The research was analyzed using the experimental method with a 2X1 factorial design. Analysis of Variance (ANOVA) would test the experimen

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Liao, Hongyu. "Banking Risk Assessment During the COVID-19 Crisis." Advances in Economics, Management and Political Sciences 42, no. 1 (2023): 179–84. http://dx.doi.org/10.54254/2754-1169/42/20232106.

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The COVID-19 outbreak has had a profound impact on the global banking industry and poses a variety of risks. Through an analysis of the global banking industry, this paper examines in detail the impact of COVID-19 on liquidity risk, credit risk, market risk, and systemic risk. The study finds that liquidity risk is mainly related to the recent Fed's interest rate hike, which, overlaid with the impact of the epidemic on the economy, has exacerbated banks' liquidity risk. Credit risk is mainly related to the increase in non-performing loan ratio and banks' credit management while studying Credit

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Erdogan, Olcay, and Zafer Konakli. "Corporate Credit Risk Assessment of BIST Companies." European Scientific Journal, ESJ 14, no. 1 (2018): 122. http://dx.doi.org/10.19044/esj.2018.v14n1p122.

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Assessing credit risk allows financial institutions to plan future loans freely, to achieve targeted risk management and gain maximum profitability. In this study, the constructed risk assessment models are on a sample data which consists of financial ratios of enterprises listed in the Bourse Istanbul (BIST). 356 enterprises are classified into three levels as the investment, speculative and below investment groups by ten parameters. The applied methods are discriminant analysis, k nearest neighbor (k-NN), support vector machines (SVM), decision trees (DT) and a new hybrid model, namely Artif

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Attigeri, Girija V., M. M. Manohara Pai, and Radhika M. Pai. "Credit Risk Assessment Using Machine Learning Algorithms." Advanced Science Letters 23, no. 4 (2017): 3649–53. http://dx.doi.org/10.1166/asl.2017.9018.

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