Gotowe bibliografie tematyczne / Credit scoring

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Gotowa bibliografia na temat „Credit scoring”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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Artykuły w czasopismach na temat "Credit scoring"

Crook, J. N., D. E. Edelman, and L. C. Thomas. "Credit Scoring." Journal of the Operational Research Society 56, no. 9 (2005): 1003–5. http://dx.doi.org/10.1057/palgrave.jors.2602037.

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Xia, Yufei, Zijun Liao, Jun Xu, and Yinguo Li. "FROM CREDIT SCORING TO REGULATORY SCORING: COMPARING CREDIT SCORING MODELS FROM A REGULATORY PERSPECTIVE." Technological and Economic Development of Economy 28, no. 6 (2022): 1954–90. http://dx.doi.org/10.3846/tede.2022.17045.

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Conventional credit scoring models evaluated by predictive accuracy or profitability typically serve the financial institutions and can hardly reflect their contribution on financial stability. To remedy this, we develop a novel regulatory scoring framework to quantify and compare the corresponding regulatory capital charge errors of credit scoring models. As an application of RegTech, the proposed framework considers the characteristic of example-dependence and costsensitivity in credit scoring, which is expected to enhance the ability of risk absorption of financial institutions and thus ben

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Purbayati, Radia, Muhammad Muflih, and Rosma Pakpahan. "Credit Scoring Modelling For Corporate Banking Institutions." Journal Integration of Management Studies 2, no. 1 (2024): 18–28. http://dx.doi.org/10.58229/jims.v2i1.125.

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This research aims to build a credit scoring modeling simulation of bank corporate loans. The credit scoring model is used in assessing creditworthiness in credit decisions. This model determines whether or not a company is eligible for the corporate credit facility it proposes. Observations were made of 100 companies included in the list of Kompas100 Index formers on the Indonesia Stock Exchange (IDX) that have the potential to apply for loans/credits to Bank Financial Institutions (IKB) in optimizing the corporate capital structure through bank debt facilities in the period 2022. Analysis wa

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Lanzarini, Laura Cristina, Augusto Villa Monte, Aurelio F. Bariviera, and Patricia Jimbo Santana. "Simplifying credit scoring rules using LVQ + PSO." Kybernetes 46, no. 1 (2017): 8–16. http://dx.doi.org/10.1108/k-06-2016-0158.

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Purpose One of the key elements in the banking industry relies on the appropriate selection of customers. To manage credit risk, banks dedicate special efforts to classify customers according to their risk. The usual decision-making process consists of gathering personal and financial information about the borrower. Processing this information can be time-consuming, and presents some difficulties because of the heterogeneous structure of data. Design/methodology/approach This paper presents an alternative method that is able to generate rules that work not only on numerical attributes but also

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Santana, Patricia Jimbo, Laura Lanzarini, and Aurelio F. Bariviera. "Fuzzy Credit Risk Scoring Rules using FRvarPSO." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 26, Suppl. 1 (2018): 39–57. http://dx.doi.org/10.1142/s0218488518400032.

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There is consensus that the best way for reducing insolvency situations in financial institutions is through good risk management, which involves a good client selection process. In the market, there are methodologies for credit scoring, each analyzing a large number of microeconomic and/or macroeconomic variables selected mostly depending on the type of credit to be granted. Since these variables are heterogeneous, the review process carried out by credit analysts takes time. The objective of this article is to propose a solution for this problem by applying fuzzy logic to the creation of cla

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Berger, Allen N., and W. Scott Frame. "Small Business Credit Scoring and Credit Availability*." Journal of Small Business Management 45, no. 1 (2007): 5–22. http://dx.doi.org/10.1111/j.1540-627x.2007.00195.x.

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Medina, Rosa Puertas, and Maria Luisa Martí Selva. "Análisis del credit scoring." Revista de Administração de Empresas 53, no. 3 (2013): 303–15. http://dx.doi.org/10.1590/s0034-75902013000300007.

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El problema de la morosidad está cobrándose una gran importancia en los países desarrollados. En este trabajo realizamos un análisis de la capacidad predictiva de dos modelos paramétricos y uno no paramétrico abordando, en este último, el problema del sobreaprendizaje mediante la validación cruzada que, muy habitualmente, se obvia en este tipo de estudios. Además proponemos la distinción de tres tipos de solicitudes dependiendo de su probabilidad cumplimiento: conceder, no conceder (de forma automática), y dudoso y, por consiguiente, proceder a su estudio manual por parte del personal bancario

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Velykoivanenko, Halyna, Svitlana Savina, Dmitriy Kolechko, and Vladyslav Ben’. "Building the ensembles of credit scoring models." Neuro-Fuzzy Modeling Techniques in Economics 7, no. 1 (2019): 21–43. http://dx.doi.org/10.21511/nfmte.7.2018.02.

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The article is devoted to solving the actual problem of increasing the efficiency of assessing the credit risks of individual borrowers by finding the optimal combination of the results of calculations of specific scoring models. The principles of the formation of an ensemble of models are given and the existing approaches to the construction of ensemble structures are analyzed. In the process of experimental research has been applied one of the modifications of the boosting algorithm and implemented the author's algorithm for constructing an ensemble of models based on the specialization of e

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Madzova, Violeta, and Nehat Ramadini. "Can credit scoring models prevent default payments in the banking industry in the period of financial crisis?" International Journal of Business & Technology 2, no. 1 (2013): 32–38. http://dx.doi.org/10.33107/ijbte.2013.2.1.05.

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Credit scoring is a scientific method of assessing the credit risk associated with new credit applications as well as for monitoring of the credit risk in the process of the loan payment. Therefore credit scoring models developed in the banks based on their internal information system, as well as the credit info system in the country, can help the banks to ensure more consistent underwriting and can provide management with a more insightful measure of credit risk. While credit scoring could be a valuable risk management tool in virtually any bank setting, it is probably not wide accepted in th

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Abhishek Kumar, Abhijeet Kumar, Aditya Kumar Singh, and Ms. Nikita. "Credit Scoring System Using Machine Learning." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 3 (2024): 376–80. http://dx.doi.org/10.32628/cseit2410334.

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This research paper offers a comprehensive examination of credit scoring systems. We will explore the current methodologies employed, the challenges encountered, and potential avenues for improvement. Credit scoring plays a critical role in financial decision-making, impacting both individual access to credit and lender risk management strategies. The paper will analyze traditional credit scoring models alongside emerging trends designed to enhance the accuracy and fairness of credit assessments. Additionally, we will discuss the ethical considerations surrounding credit scoring and propose re

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Rozprawy doktorskie na temat "Credit scoring"

MacNeill, Ann. "Mortgage credit scoring." Thesis, University of Edinburgh, 2000. http://hdl.handle.net/1842/23108.

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The application of credit scoring techniques to assess the credit worthiness of borrowers is a well-established practice in consumer credit. However, the use of credit scoring to provide credit decisions for mortgage loans is a new application of this method of assessment. This thesis is concerned with whether the application of credit scoring to a mortgage loan portfolio affords business benefits. A review of the literature relevant to both credit scoring and mortgage default is presented. Following a review of the literature, the methodology and research design are described. Thereafter this

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Finlay, Steven. "Modelling issues in credit scoring." Thesis, Lancaster University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.437280.

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Whitehead, Christopher David. "Statistical techniques in credit scoring." Thesis, Lancaster University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.443518.

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The credit industry requires continued development and application of new statistical methodology that can improve aspects of the business. The first part of the thesis suggests a new diagnostic, derived from Kalman filtering, to assess model performance. It allows systematic updating of tracked statistics over time by incorporating new observations with the previous best estimates. It has benefits that current industry practices do not possess and we illustrate its worth on a mortgage application database. The second part of the thesis is concerned with regression analysis of financial data.

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Fernandes, António Francisco de Melo. "Credit scoring : uma análise econométrica." Master's thesis, Instituto Superior de Economia e Gestão, 2017. http://hdl.handle.net/10400.5/14342.

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Mestrado em Métodos Quantitativos para a Decisão Económica e Empresarial<br>Com intenção de melhorar os serviços de análise e gestão de crédito, as instituições financeiras desenvolveram o modelo credit scoring. Este modelo é utilizado por estas instituições para previsão do risco de crédito no processo da tomada de decisão de concessão de crédito. O objetivo deste trabalho, é desenvolver um modelo de credit scoring a partir de uma amostra de 1000 solicitantes de créditos extraídos da carteira de crédito de um banco alemão. Para tal, estimou-se um modelo probit, considerando-se 25 variáveis i

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Zajíčková, Miroslava. "Credit scoring a jeho nástroje." Master's thesis, Vysoká škola ekonomická v Praze, 2011. http://www.nusl.cz/ntk/nusl-112781.

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The aim of this thesis is to compare the scoring models of banking and non-banking institutions when using specific outcomes of the request for a loan of CZK 100 000,- of several natural persons with varying credibility (the credibility of credit). The theoretical part is divided into two chapters, the first deals with the explanation of basic terms (credit, the applicant, bank and non-bank institutions, credit scoring, rating, review on the software used and legislation in the CR). The second chapter is devoted to describe the process of credit scoring, scoring models and a scoring function.

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Henley, W. E. "Statistical aspects of credit scoring." n.p, 1994. http://ethos.bl.uk/.

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Henley, William Edward. "Statistical aspects of credit scoring." Thesis, Open University, 1994. http://oro.open.ac.uk/57441/.

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This thesis is concerned with statistical aspects of credit scoring, the process of determining how likely an applicant for credit is to default with repayments. In Chapters 1-4 a detailed introduction to credit scoring methodology is presented, including evaluation of previous published work on credit scoring and a review of discrimination and classification techniques. In Chapter 5 we describe different approaches to measuring the absolute and relative performance of credit scoring models. Two significance tests are proposed for comparing the bad rate amongst the accepts (or the error rate)

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Martinez, John Brett. "Credit card credit scoring and risk based lending at XYZ Credit Union." CSUSB ScholarWorks, 2000. https://scholarworks.lib.csusb.edu/etd-project/1752.

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Iscanoglu, Aysegul. "Credit Scoring Methods And Accuracy Ratio." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606502/index.pdf.

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The credit scoring with the help of classification techniques provides to take easy and quick decisions in lending. However, no definite consensus has been reached with regard to the best method for credit scoring and in what conditions the methods performs best. Although a huge range of classification techniques has been used in this area, the logistic regression has been seen an important tool and used very widely in studies. This study aims to examine accuracy and bias properties in parameter estimation of the logistic regression by using Monte Carlo simulations in four aspect which are di

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Glasson, Samuel, and sglas@iinet net au. "Censored Regression Techniques for Credit Scoring." RMIT University. Mathematical and Geospatial Sciences, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080212.151610.

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This thesis investigates the use of newly-developed survival analysis tools for credit scoring. Credit scoring techniques are currently used by financial institutions to estimate the probability of a customer defaulting on a loan by a predetermined time in the future. While a number of classification techniques are currently used, banks are now becoming more concerned with estimating the lifetime of the loan rather than just the probability of default. Difficulties arise when using standard statistical techniques due to the presence of censoring in the data. Survival analysis, originating from

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Książki na temat "Credit scoring"

Siddiqi, Naeem. Intelligent Credit Scoring. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119282396.

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Crook, J. N. Credit scoring: An overview. University of Edinburgh, Management School, 1996.

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Berger, Allen N. Small business credit scoring and credit availability. Federal Reserve Bank of Atlanta, 2005.

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Elizabeth, Mays, ed. Handbook of credit scoring. Glenlake Pub. Co., ltd., 2001.

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Elizabeth, Mays, ed. Handbook of credit scoring. Glenlake Pub. Co., 2001.

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Coyle, Brian. Measuring credit risk. CIB Publishing, 2000.

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Lewis, Edward M. An introduction to credit scoring. Athena Press, 1990.

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Byers, Ann. First credit cards and credit smarts. Rosen Pub., 2010.

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Kiefer, Nicholas M. Specification and informational issues in credit scoring. Office of the Comptroller of the Currency, 2004.

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Banasik, J. Does scoring a subpopulation make a difference? University of Edinburgh, Management School, 1995.

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Części książek na temat "Credit scoring"

Doumpos, Michael, and Constantin Zopounidis. "Credit Scoring." In Multicriteria Analysis in Finance. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05864-1_4.

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Abrahams, Clark, Bradley Bender, and Charles Maner. "Validating Generic Vendor Scorecards." In Intelligent Credit Scoring. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119282396.ch13.

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Wagner, Hendrik. "Default Definition under Basel." In Intelligent Credit Scoring. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119282396.ch7.

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Anderson, Billie. "Big Data." In Intelligent Credit Scoring. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119282396.ch9.

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Naamani, O. "Credit Scoring System." In Operations Research Proceedings. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73778-7_98.

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Popovych, Bohdan. "Credit Scoring Methodologies." In Application of AI in Credit Scoring Modeling. Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-40180-1_3.

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Wu, Desheng Dash, and David L. Olson. "Bank Credit Scoring." In Enterprise Risk Management in Finance. Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137466297_8.

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Roncalli, Thierry. "Credit Scoring Models." In Handbook of Financial Risk Management. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781315144597-15.

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Stanghellini, Elena. "Il credit scoring." In Unitext. Springer Milan, 2009. http://dx.doi.org/10.1007/978-88-470-1081-9_1.

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Witzany, Jiří. "Rating and Scoring Systems." In Credit Risk Management. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49800-3_3.

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Streszczenia konferencji na temat "Credit scoring"

Ziaur Rahaman, Shah Md, Afra Anika, and A. K. M. Muzahidul Islam. "Credit Scoring Model Development: Taking Advantage of Credit History." In 2024 IEEE Region 10 Symposium (TENSYMP). IEEE, 2024. http://dx.doi.org/10.1109/tensymp61132.2024.10752316.

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B S, Darshan, Vishal Kumar I B, Ashwini Kodipalli, and Trupti Rao. "Default credit card scoring using ML." In 2024 International Conference on Computational Intelligence for Security, Communication and Sustainable Development (CISCSD). IEEE, 2024. https://doi.org/10.1109/ciscsd63381.2024.00022.

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Qaisar, Rabia, Saeed Mian Qaisar, and M. G. Abbas Malik. "Credit Approval Scoring Using Ensemble Learning." In 2025 Eighth International Women in Data Science Conference at Prince Sultan University (WiDS PSU). IEEE, 2025. https://doi.org/10.1109/wids-psu64963.2025.00048.

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Putri, Velani Febriana, and Tora Fahrudin. "Exploring Deep Learning Algorithm for Credit Scoring." In 2024 International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA). IEEE, 2024. https://doi.org/10.1109/icicyta64807.2024.10913258.

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Rai, Anjali, and Mehfooza M. "Comparative Prediction Analysis for Credit Scoring System." In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2025. https://doi.org/10.1109/idciot64235.2025.10914846.

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Nayaka, Premkumar, Anusha Hegde, and Biswajit Bhowmik. "Advancements in Credit Scoring, Profit Scoring, and Portfolio Optimization for P2P Lending." In 2024 International Conference on Communication, Control, and Intelligent Systems (CCIS). IEEE, 2024. https://doi.org/10.1109/ccis63231.2024.10932068.

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Shukla, Deepa, and Sunil Gupta. "Feature Engineering Techniques to Enhance Credit Scoring Models." In 2024 6th International Conference on Electrical, Control and Instrumentation Engineering (ICECIE). IEEE, 2024. https://doi.org/10.1109/icecie63774.2024.10815675.

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Sharma, Pradeep, and J. Logeshwaran. "AI-driven Credit Scoring System for Real-Time Credit Card Approval in Banking." In 2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT). IEEE, 2024. https://doi.org/10.1109/icaiccit64383.2024.10912352.

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Kumbhar, Tanuj, Drishti Agrawal, Lavil Saldanha, and Dipali Koshti. "AI-Driven Credit Scoring and Credit Line Solution for the Unreserved and Self-Employed." In 2024 Second International Conference on Inventive Computing and Informatics (ICICI). IEEE, 2024. http://dx.doi.org/10.1109/icici62254.2024.00039.

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Krishna, Kaivaram Sivarama, Bommala Deepak Kumar, Morumpalli Deekshith Reddy, Ch Saketh, and Manju Venugopalan. "Credit Scoring for Loan Applicants Using Machine Learning Models." In 2024 4th International Conference on Intelligent Technologies (CONIT). IEEE, 2024. http://dx.doi.org/10.1109/conit61985.2024.10626212.

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Raporty organizacyjne na temat "Credit scoring"

Sterbik-Lamina, Jaro, ed. Credit Scoring in �sterreich. Self, 2014. http://dx.doi.org/10.1553/ita-pb-a66.

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Bhardwaj, Geetesh, and Rajdeep Sengupta. Credit Scoring and Loan Default. Federal Reserve Bank of St. Louis, 2011. http://dx.doi.org/10.20955/wp.2011.040.

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Yaroshchuk, Svitlana O., Nonna N. Shapovalova, Andrii M. Striuk, Olena H. Rybalchenko, Iryna O. Dotsenko, and Svitlana V. Bilashenko. Credit scoring model for microfinance organizations. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3683.

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The purpose of the work is the development and application of models for scoring assessment of microfinance institution borrowers. This model allows to increase the efficiency of work in the field of credit. The object of research is lending. The subject of the study is a direct scoring model for improving the quality of lending using machine learning methods. The objective of the study: to determine the criteria for choosing a solvent borrower, to develop a model for an early assessment, to create software based on neural networks to determine the probability of a loan default risk. Used rese

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Agarwal, Sumit, Paige Skiba, and Jeremy Tobacman. Payday Loans and Credit Cards: New Liquidity and Credit Scoring Puzzles? National Bureau of Economic Research, 2009. http://dx.doi.org/10.3386/w14659.

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Wijaya, Trissa, and Muhammad Nidhal. International Experiences with Innovative Credit Scoring: Lessons for Indonesia. Center for Indonesian Policy Studies, 2023. http://dx.doi.org/10.35497/564013.

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Wijaya, Trissia, and Muhammad Nidhal. Pengalaman Internasional Terkait Innovative Credit Scoring: Pelajaran bagi Indonesia. Center for Indonesian Policy Studies, 2023. http://dx.doi.org/10.35497/564017.

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Berg, Tobias, Valentin Burg, Ana Gombović, and Manju Puri. On the Rise of FinTechs – Credit Scoring using Digital Footprints. National Bureau of Economic Research, 2018. http://dx.doi.org/10.3386/w24551.

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Wijaya, Trissia. Berkembangnya Sistem Innovative Credit Scoring di Indonesia Menilai Risiko dan Tantangan Kebijakan. Center for Indonesian Policy Studies, 2023. http://dx.doi.org/10.35497/560781.

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Wijaya, Trissia. The Rise of Innovative Credit Scoring System in Indonesia: Assessing Risks and Policy Challenges. Center for Indonesian Policy Studies, 2023. http://dx.doi.org/10.35497/560780.

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Mishra, Prachi, Nagpurnanand Prabhala, and Raghuram Rajan. The Relationship Dilemma: Organizational Culture and the Adoption of Credit Scoring Technology in Indian Banking. National Bureau of Economic Research, 2019. http://dx.doi.org/10.3386/w25694.

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