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Relevant bibliographies by topics / Credit scoring model

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Academic literature on the topic 'Credit scoring model'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Credit scoring model"

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Wang, Linhui, Jianping Zhu, Chenlu Zheng, and Zhiyuan Zhang. "Incorporating Digital Footprints into Credit-Scoring Models through Model Averaging." Mathematics 12, no. 18 (2024): 2907. http://dx.doi.org/10.3390/math12182907.

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Digital footprints provide crucial insights into individuals’ behaviors and preferences. Their role in credit scoring is becoming increasingly significant. Therefore, it is crucial to combine digital footprint data with traditional data for personal credit scoring. This paper proposes a novel credit-scoring model. First, lasso-logistic regression is used to select key variables that significantly impact the prediction results. Then, digital footprint variables are categorized based on business understanding, and candidate models are constructed from various combinations of these groups. Finall

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Ershadi, Mohammad Javad, and Davood Omidzadeh. "Customer Validation Using Hybrid Logistic Regression and Credit Scoring Model: A Case Study." Quality - Access to Success 19, no. 167 (2017): 54–58. https://doi.org/10.5281/zenodo.14008551.

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In this paper a regression model is applied for validating the customers of a company.&lrm; Using a Delphi method beside the expert panel the main variables which construct the regression model are extracted.&lrm; A credit scoring system for validation of the customers is developed based on applied regression model.&lrm; Then a Newton-Raphson method is used for determining the coefficients of regression model.&lrm; Furthermore the MacFadden statistical value is calculated for approving the regression model.&lrm; A case study is presented for application of proposed model.&lrm; Three factors of

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Kumar, C. J. Anil, B. K. Raghavendra, and S. Raghavendra. "A Credit Scoring Heterogeneous Ensemble Model Using Stacking and Voting." Indian Journal of Science and Technology 15, no. 7 (2021): 300–308. http://dx.doi.org/10.17485/ijst/v15i7.1715.

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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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Wilhelmina, Afua Addy, Olusola Ajayi-Nifise Adeola, Gloria Bello Binaebi, Tubokirifuruar Tula Sunday, Odeyemi Olubusola, and Falaiye Titilola. "AI in credit scoring: A comprehensive review of models and predictive analytics." Global Journal of Engineering and Technology Advances 18, no. 2 (2024): 118–29. https://doi.org/10.5281/zenodo.10947395.

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This review provides a succinct overview of the comprehensive review exploring the integration of Artificial Intelligence (AI) in credit scoring. The analysis delves into diverse AI models and predictive analytics shaping the contemporary landscape of credit assessment. The review begins by examining the historical context of credit scoring and progresses through the transformative impact of AI on traditional credit assessment methodologies. It scrutinizes various AI models employed in credit scoring, ranging from machine learning algorithms to advanced predictive analytics. Emphasis is placed

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Chen, Keqin, Amit Yadav, Asif Khan, and Kun Zhu. "Credit Fraud Detection Based on Hybrid Credit Scoring Model." Procedia Computer Science 167 (2020): 2–8. http://dx.doi.org/10.1016/j.procs.2020.03.176.

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Chuang, Chun-Ling, and Rong-Ho Lin. "Constructing a reassigning credit scoring model." Expert Systems with Applications 36, no. 2 (2009): 1685–94. http://dx.doi.org/10.1016/j.eswa.2007.11.067.

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Lobato, Tarcísio da Costa, and Brena do Nascimento Carvalho. "PROPOSTA DE UM MODELO ENSEMBLE PARA CREDIT SCORING / PROPOSAL FOR AN ENSEMBLE MODEL FOR CREDIT SCORING." Brazilian Journal of Development 7, no. 3 (2021): 24280–97. http://dx.doi.org/10.34117/bjdv7n3-232.

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Guo, Yan Feng, Na Sun, and Yuan Yao. "An Ensemble Learning Model Based on SOM-SVM Model for Personal Credit Risk." Advanced Materials Research 271-273 (July 2011): 1286–90. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.1286.

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Credit risk problem is an essential problem in financial management area. People usually employ personal credit scoring to avoid financial risk problem. Although many methods have been proposed for evaluating the personal credit scoring and obtained good effects, most of these methods were called single model types, which would be disturbed by model self-parameter, data noise and other external factors. In order to overcome the weakness of single model, we believe one of best ways is to construct an ensemble model. In this paper, we proposed a new style of ensemble model and employed two publi

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Amat, Oriol, Raffaele Manini, and Marcos Antón Renart. "Credit concession through credit scoring: Analysis and application proposal." Intangible Capital 13, no. 1 (2017): 51. http://dx.doi.org/10.3926/ic.903.

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Purpose: The study herein develops and tests a credit scoring model which can help financial institutions in assessing credit requests. Design/methodology/approach: The empirical study has the objective of answering two questions: (1) Which ratios better discriminate the companies based on their being solvent or insolvent? and (2) What is the relative importance of these ratios? To do this, several statistical techniques with a multifactorial focus have been used (Multivariate Analysis of Variance, Linear Discriminant Analysis, Logit and Probit Models). Several samples of companies have been u

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Dissertations / Theses on the topic "Credit scoring model"

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Ayres, Gabriela, and Wei Wei. "Credit Scoring Model Applications: Testing Multinomial Targets." Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-35665.

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Lima, Evanessa Maria Barbosa de Castro. "AnÃlise de determinantes da inadimplÃncia (pessoa fÃsica) tomadores de crÃdito: uma abordagem economÃtrica." Universidade Federal do CearÃ, 2004. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=1480.

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nÃo hÃ<br>Sendo a intermediaÃÃo financeira a principal atividade dos bancos, alocando recursos de clientes superavitÃrios a clientes deficitÃrios, Ã na incerteza quanto ao carÃter e a capacidade de pagamento dos clientes que se estabelece o risco e com ele a necessidade de se buscar novas alternativas para se proteger de perdas potenciais, que podem refletir em menores lucros para as instituiÃÃes. AlÃm da subjetividade dos analistas de crÃdito, o uso de modelos quantitativos, baseados em prÃticas estatÃsticas, economÃtricas e matemÃticas, vÃm cada vez mais se firmando nos mercados como ferrame

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Ala'raj, Maher A. "A credit scoring model based on classifiers consensus system approach." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13669.

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Managing customer credit is an important issue for each commercial bank; therefore, banks take great care when dealing with customer loans to avoid any improper decisions that can lead to loss of opportunity or financial losses. The manual estimation of customer creditworthiness has become both time- and resource-consuming. Moreover, a manual approach is subjective (dependable on the bank employee who gives this estimation), which is why devising and implementing programming models that provide loan estimations is the only way of eradicating the ‘human factor’ in this problem. This model shoul

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Castro, Esther E. "An Applied Credit Scoring Model and Christian Mutual Funds Performance." ScholarWorks@UNO, 2015. http://scholarworks.uno.edu/td/2061.

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This dissertation comprises two different financial essays. Essay 1, “An Applied Credit Score Model,” uses data from local credit union to predict the probability of default. Due to recent financial crisis regulation has been enacted that makes it essential to develop a probability of default model that will mitigate charge-off losses. Using discriminant analysis and logistic regression this paper will attempt to see how well credit score can predict probability of default. While credit score does an adequate job at classifying loans, misclassification of loans can be costly. Thus while credit

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Islam, Md Samsul, Lin Zhou, and Fei Li. "Application of Artificial Intelligence (Artificial Neural Network) to Assess Credit Risk : A Predictive Model For Credit Card Scoring." Thesis, Blekinge Tekniska Högskola, Sektionen för management, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2099.

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Credit Decisions are extremely vital for any type of financial institution because it can stimulate huge financial losses generated from defaulters. A number of banks use judgmental decisions, means credit analysts go through every application separately and other banks use credit scoring system or combination of both. Credit scoring system uses many types of statistical models. But recently, professionals started looking for alternative algorithms that can provide better accuracy regarding classification. Neural network can be a suitable alternative. It is apparent from the classification out

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Serralheiro, Silvana Oliveira. "Construção de um processo de monitorização de modelo de scoring de crédito hipotecário para concessão." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/21649.

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Mestrado em Mathematical Finance<br>O crédito hipotecário abrange créditos destinados à aquisição, construção, arrendamento e/ou manutenção de habitação. O modelo de scoring permite distinguir os bons e maus créditos, através do perfil de risco dos clientes, seguindo-se a decisão de conceder ou não crédito. O objetivo deste trabalho foi desenvolver um processo de monitorização aplicado ao modelo de scoring de crédito hipotecário para concessão da Caixa Geral de Depósitos. O propósito desta monitorização é verificar se o modelo, implementado em junho de 2018, mantém uma boa capacidade pred

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Brown, Iain Leonard Johnston. "Basel II compliant credit risk modelling : model development for imbalanced credit scoring data sets, loss given default (LGD) and exposure at default (EAD)." Thesis, University of Southampton, 2012. https://eprints.soton.ac.uk/341517/.

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The purpose of this thesis is to determine and to better inform industry practitioners to the most appropriate classification and regression techniques for modelling the three key credit risk components of the Basel II minimum capital requirement; probability of default (PD), loss given default (LGD), and exposure at default (EAD). The Basel II accord regulates risk and capital management requirements to ensure that a bank holds enough capital proportional to the exposed risk of its lending practices. Under the advanced internal ratings based (IRB) approach Basel II allows banks to develop the

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Hrubá, Elina. "Rozhodnutí o zavedení externího scoringového modelu na základě porovnání se současným interním řešením." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-264053.

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In my thesis I have analyzed internal and external scoring model of financial organization. I have prepared comprehensive comparison and evaluation of both internal and external scoring systems. The aim of the thesis was creating a complete assessment of external scoring system with the simplified financial analysis and also with taking into the consideration appropriateness of this offer before approving purchase of external model.

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Roszbach, Kasper. "Essays on banking, credit and interest rates." Doctoral thesis, Stockholm : Economic Research Institute, Stockholm School of Economics [Ekonomiska forskningsinstitutet vid Handelshögsk.] (EFI), 1998. http://www.hhs.se/efi/summary/488.htm.

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OlLIVEIRA, NETO Rosalvo Ferreira de. "COMOVI: um framework para transformação de dados em aplicações de credit behavior scoring baseado no desenvolvimento dirigido por modelos." Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/17330.

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Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-07-12T12:11:15Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Tese_Rosalvo_Neto_CIN_2015.pdf: 7674683 bytes, checksum: 99037c704450a9a878bcbe93ab8b392d (MD5)<br>Made available in DSpace on 2016-07-12T12:11:15Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Tese_Rosalvo_Neto_CIN_2015.pdf: 7674683 bytes, checksum: 99037c704450a9a878bcbe93ab8b392d (MD5) Previous issue date: 2015-12-11<br>CAPEs<br>A etapa de pré-proce

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Books on the topic "Credit scoring model"

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Thomas, L. C. Consumer credit models: Pricing, profit, and portfolios. Oxford University Press, 2009.

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Leadership, Management and Data Science: Risk Calculations, Financial Services, Credit Scoring, Agriculture, Risk, Credit Risk Scoring Model,TR6 Scorecard, Alternative Credit, Loan, Deep Learning Techniques. Independently Published, 2022.

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Anderson, Raymond A. Credit Intelligence & Modelling. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192844194.001.0001.

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This book, “Forest Paths” for short, started as a detailed guide for the construction of predictive models for credit and other risk assessment, for use in big-bank retail lending. It became a textbook covering credit processes (from marketing through to fraud), bureau and rating agencies, and various tools. Included are detailed histories (economics, statistics, social science}, which much referencing. It is unique in the field, with chatpers’-end questions. The primary target market is corporate and academic, but much would be of interest to a broader audience. There are eight modules: 1) an

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SCIENCE, Free. Credit Scoring : Modeling for Missing Data Relative Informations Collected on Rejected Customers : Binary Classification Machine Learning Models: Formalization and Study of Statistical Problems in Credit Scoring. Independently Published, 2022.

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Committee on Financial Services, United States House of Representatives, and United States United States Congress. What Borrowers Need to Know about Credit Scoring Models and Credit Scores: Hearing Before the Subcommittee on Oversight and Investigations of the Committee on Financial Services, U. S. House of Representatives, One Hundred Tenth Congress, Second Session,. Independently Published, 2019.

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What borrowers need to know about credit scoring models and credit scores: Hearing before the Subcommittee on Oversight and Investigations of the Committee on Financial Services, U.S. House of Representatives, One Hundred Tenth Congress, second session, July 29, 2008. U.S. G.P.O., 2008.

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Book chapters on the topic "Credit scoring model"

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Castro, Esther, M. Kabir Hassan, and Mark Rosa. "An applied credit scoring model." In Expert Systems in Finance. Routledge, 2019. http://dx.doi.org/10.4324/9780429024061-9.

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Finlay, Steven. "Multi-model (Fusion) Systems." In Credit Scoring, Response Modeling, and Insurance Rating. Palgrave Macmillan UK, 2012. http://dx.doi.org/10.1057/9781137031693_11.

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Finlay, Steven. "Model Construction (Parameter Estimation)." In Credit Scoring, Response Modeling, and Insurance Rating. Palgrave Macmillan UK, 2012. http://dx.doi.org/10.1057/9781137031693_7.

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Finlay, Steven. "Model Construction (Parameter Estimation)." In Credit Scoring, Response Modelling and Insurance Rating. Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230298989_7.

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Farquad, M. A. H., V. Ravi, Sriramjee, and G. Praveen. "Credit Scoring Using PCA-SVM Hybrid Model." In Computer Networks and Information Technologies. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19542-6_40.

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Marciano, Chiara, Mario Rosario Guarracino, and Brian Daniel Bernhardt. "Improved Credit Scoring Model with Hyperparameter Optimization." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-73365-9_23.

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Akil, Siham, Sara Sekkate, and Abdellah Adib. "Interpretable Credit Scoring Model via Rule Ensemble." In International Conference on Advanced Intelligent Systems for Sustainable Development. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26384-2_81.

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Finlay, Steven. "Validation, Model Performance and Cut-off Strategy." In Credit Scoring, Response Modeling, and Insurance Rating. Palgrave Macmillan UK, 2012. http://dx.doi.org/10.1057/9781137031693_8.

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Finlay, Steven. "Validation, Model Performance and Cut-off Strategy." In Credit Scoring, Response Modelling and Insurance Rating. Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230298989_8.

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Rogers, John C., Conway T. Rucks, and Shawne Swindler. "A Credit Scoring Model to Evaluate the Credit Worthiness of Credit Card Applicants." In Proceedings of the 1982 Academy of Marketing Science (AMS) Annual Conference. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16946-0_177.

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Conference papers on the topic "Credit scoring model"

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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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Chen, Keyi. "Research on credit scoring model optimization based on machine learning." In 2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2024. http://dx.doi.org/10.1109/icbase63199.2024.10762719.

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Reza, Md Shihab, Monirul Islam Mahmud, Ifti Azad Abeer, and Nova Ahmed. "Linear Discriminant Analysis in Credit Scoring: A Transparent Hybrid Model Approach." In 2024 27th International Conference on Computer and Information Technology (ICCIT). IEEE, 2024. https://doi.org/10.1109/iccit64611.2024.11022149.

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Zhou, Xiaohan, and Ruikun Zhang. "Model Analysis and Application of Quantum Computers in Credit Scoring Card Combination Optimization." In 2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2024. http://dx.doi.org/10.1109/ddcls61622.2024.10606854.

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Hafidh, Aufa Azhari, and Andry Alamsyah. "A Credit Scoring Model Proposal Based on Social Media Data to Enhance Financial Assessment." In 2024 12th International Conference on Information and Communication Technology (ICoICT). IEEE, 2024. http://dx.doi.org/10.1109/icoict61617.2024.10723094.

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Gicić, Adaleta, and Dženana Ɖonko. "Leveraging Time Sequence Deep Learning Models: Impact of Hidden Layers on AI Model Performance in Credit Scoring." In 2024 IEEE International Conference on Future Machine Learning and Data Science (FMLDS). IEEE, 2024. https://doi.org/10.1109/fmlds63805.2024.00068.

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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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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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Laesanklang, Wasakorn, Krung Sinapiromsaran, and Boonyarit Intiyot. "Entropy multi-hyperplane credit scoring model." In 2010 International Conference on Financial Theory and Engineering (ICFTE). IEEE, 2010. http://dx.doi.org/10.1109/icfte.2010.5499418.

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Yeh, Hui-Chung, Min-Li Yang, and Li-Chuen Lee. "An Empirical Study of Credit Scoring Model for Credit Card." In Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icicic.2007.138.

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Reports on the topic "Credit scoring model"

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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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Klinger, Bailey, Asim Khwaja, and Joseph LaMonte. Improving Credit Risk Analysis with Psychometrics in Peru. Inter-American Development Bank, 2013. http://dx.doi.org/10.18235/0009139.

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Access to finance remains a challenge for some micro, small, and medium-sized enterprises (MSMEs) in Peru, particularly informal enterprises with no borrowing history in the formal financial system. Lenders lack the tools to reach these borrowers with sufficient scale and control over risk due in part to the shortcomings of current screening technologies. For this paper, the authors carried out a pilot test of an innovative psychometric tool aimed at evaluating credit risk for business owners seeking a loan from Financiera Confianza, the fourth largest Empresa Financiera in Peru. Applicant res

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Klinger, Bailey, Lucio Castro, Paula Szenkman, and Asim Khwaja. Unlocking SME Finance in Argentina with Psychometrics. Inter-American Development Bank, 2013. http://dx.doi.org/10.18235/0009112.

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Access to finance remains a challenge for Small and Medium Enterprises (SMEs) in Argentina. It is a particularly acute problem among newly created SMEs that have a scant credit record and frequently lack collateral. The authors of this paper carried out a pilot test of an innovative psychometric tool aimed at evaluating credit risk among SME clients of Banco Ciudad de Buenos Aires (BCBA), one of the top three public banks of Argentina. SME responses were compared to their historical repayment records to determine if psychometric-based credit scoring models could help ameliorate the constraints

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