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Artykuły w czasopismach na temat „Credit scoring”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

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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5

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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6

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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8

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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9

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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10

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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11

Onay, Ceylan, and Elif Öztürk. "A review of credit scoring research in the age of Big Data." Journal of Financial Regulation and Compliance 26, no. 3 (2018): 382–405. http://dx.doi.org/10.1108/jfrc-06-2017-0054.

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Purpose This paper aims to survey the credit scoring literature in the past 41 years (1976-2017) and presents a research agenda that addresses the challenges and opportunities Big Data bring to credit scoring. Design/methodology/approach Content analysis methodology is used to analyze 258 peer-reviewed academic papers from 147 journals from two comprehensive academic research databases to identify their research themes and detect trends and changes in the credit scoring literature according to content characteristics. Findings The authors find that credit scoring is going through a quantitativ

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12

Silva, Fabio, and Cesar Analide. "Information asset analysis: credit scoring and credit suggestion." International Journal of Electronic Business 9, no. 3 (2011): 203. http://dx.doi.org/10.1504/ijeb.2011.042542.

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13

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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14

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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15

Aggarwal, Nikita. "THE NORMS OF ALGORITHMIC CREDIT SCORING." Cambridge Law Journal 80, no. 1 (2021): 42–73. http://dx.doi.org/10.1017/s0008197321000015.

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AbstractThis article examines the growth of algorithmic credit scoring and its implications for the regulation of consumer credit markets in the UK. It constructs a frame of analysis for the regulation of algorithmic credit scoring, bound by the core norms underpinning UK consumer credit and data protection regulation: allocative efficiency, distributional fairness and consumer privacy (as autonomy). Examining the normative trade-offs that arise within this frame, the article argues that existing data protection and consumer credit frameworks do not achieve an appropriate normative balance in

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16

Journal, IJSREM. "A Machine Learning Approach to Detect Credit Fraud." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26214.

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Credit fraud poses a significant challenge in the financial sector, leading to substantial financial losses annually. Traditional credit scoring methods often fall short in accurately detecting fraudulent transactions. This research project aims to leverage machine learning algorithms to address this issue by developing intelligent credit scoring systems with a specific focus on credit fraud detection. The objectives include gathering and analyzing a diverse dataset, exploring and evaluating various machine learning algorithms, developing and optimizing models, and evaluating their performance

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Wilhelmina Afua Addy, Adeola Olusola Ajayi-Nifise, Binaebi Gloria Bello, Sunday Tubokirifuruar Tula, Olubusola Odeyemi, and Titilola Falaiye. "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. http://dx.doi.org/10.30574/gjeta.2024.18.2.0029.

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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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18

Alamsyah, Andry, Aufa Azhari Hafidh, and Annisa Dwiyanti Mulya. "Innovative Credit Risk Assessment: Leveraging Social Media Data for Inclusive Credit Scoring in Indonesia’s Fintech Sector." Journal of Risk and Financial Management 18, no. 2 (2025): 74. https://doi.org/10.3390/jrfm18020074.

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The financial technology domain has undertaken significant strides toward more inclusive credit scoring systems by integrating alternative data sources, prompting an exploration of how we can further simplify the process of efficiently assessing creditworthiness for the younger generation who lack traditional credit histories and collateral assets. This study introduces a novel approach leveraging social media analytics and advanced machine learning techniques to assess the creditworthiness of individuals without traditional credit histories and collateral assets. Conventional credit scoring m

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19

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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20

Chacko, Annie. "Optimized algorithm for Credit Scoring." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 1.3 (2020): 361–65. http://dx.doi.org/10.30534/ijatcse/2020/5691.32020.

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Bono, Teresa, Karen Croxson, and Adam Giles. "Algorithmic fairness in credit scoring." Oxford Review of Economic Policy 37, no. 3 (2021): 585–617. http://dx.doi.org/10.1093/oxrep/grab020.

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Abstract The use of machine learning as an input into decision-making is on the rise, owing to its ability to uncover hidden patterns in large data and improve prediction accuracy. Questions have been raised, however, about the potential distributional impacts of these technologies, with one concern being that they may perpetuate or even amplify human biases from the past. Exploiting detailed credit file data for 800,000 UK borrowers, we simulate a switch from a traditional (logit) credit scoring model to ensemble machine-learning methods. We confirm that machine-learning models are more accur

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22

Sengupta, Rajdeep, and Geetesh Bhardwaj. "Credit Scoring and Loan Default." International Review of Finance 15, no. 2 (2015): 139–67. http://dx.doi.org/10.1111/irfi.12048.

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Sewart, P. "Graphical models in credit scoring." IMA Journal of Management Mathematics 9, no. 3 (1998): 241–66. http://dx.doi.org/10.1093/imaman/9.3.241.

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West, David. "Neural network credit scoring models." Computers & Operations Research 27, no. 11-12 (2000): 1131–52. http://dx.doi.org/10.1016/s0305-0548(99)00149-5.

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Paleologo, Giuseppe, André Elisseeff, and Gianluca Antonini. "Subagging for credit scoring models." European Journal of Operational Research 201, no. 2 (2010): 490–99. http://dx.doi.org/10.1016/j.ejor.2009.03.008.

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Finlay, Steven. "Credit scoring for profitability objectives." European Journal of Operational Research 202, no. 2 (2010): 528–37. http://dx.doi.org/10.1016/j.ejor.2009.05.025.

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Simonov, P. M., and E. V. Zboev. "Scoring Credit Rating of Individuals." Herald of Dagestan State University 35, no. 2 (2020): 18–26. http://dx.doi.org/10.21779/2542-0321-2020-35-2-18-26.

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Hohnen, Pernille, Michael Alexander Ulfstjerne, and Mathias Sosnowski Krabbe. "Assessing Creditworthiness in the Age of Big Data." Journal of Extreme Anthropology 5, no. 1 (2021): 29–55. http://dx.doi.org/10.5617/jea.8315.

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The purpose of this article is twofold: first, we show how algorithms have become increasingly central to financial credit scoring; second, we draw on this to further develop the anthropological study of algorithmic governance. As such, we describe the literature on credit scoring and then discuss ethnographic examples from two regulatory and commercial contexts: the US and Denmark. From these empirical cases, we carve out main developments of algorithmic governance in credit scoring and elucidate social and cultural logics behind algorithmic governance tools. Our analytical framework builds o

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Sun, Jie, Hui Li, Pei-Chann Chang, and Qing-Hua Huang. "Dynamic credit scoring using B & B with incremental-SVM-ensemble." Kybernetes 44, no. 4 (2015): 518–35. http://dx.doi.org/10.1108/k-02-2014-0036.

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Purpose – Previous researches on credit scoring mainly focussed on static modeling on panel sample data set in a certain period of time, and did not pay enough attention on dynamic incremental modeling. The purpose of this paper is to address the integration of branch and bound algorithm with incremental support vector machine (SVM) ensemble to make dynamic modeling of credit scoring. Design/methodology/approach – This new model hybridizes support vectors of old data with incremental financial data of corporate in the process of dynamic ensemble modeling based on bagged SVM. In the incremental

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Ruddy, Thomas Fay. "Anglo-American Credit Scoring and Consumer Debt in the Subprime Mortgage Crisis of 2007 as Models for Other Countries?" tripleC: Communication, Capitalism & Critique. Open Access Journal for a Global Sustainable Information Society 8, no. 2 (2010): 275–84. http://dx.doi.org/10.31269/triplec.v8i2.176.

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The system of credit scoring has been built up in recent times on the basis of a compromise struck between individuality and surveillance in ways that boosted consumption through consumer debt. This paper considers the role of credit scoring in the recent financial crisis, concluding that even if credit scoring had worked as intended under its own terms, the practice would not have been enough to limit the defaulting of mortgage borrowers under conditions of falling house prices. The broader economic problem is the crippling amount of consumer debt involved; hence the paper places credit scori

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Ruddy, Thomas Fay. "Anglo-American Credit Scoring and Consumer Debt in the Subprime Mortgage Crisis of 2007 as Models for Other Countries?" tripleC: Communication, Capitalism & Critique. Open Access Journal for a Global Sustainable Information Society 8, no. 2 (2010): 275–84. http://dx.doi.org/10.31269/vol8iss2pp275-284.

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The system of credit scoring has been built up in recent times on the basis of a compromise struck between individuality and surveillance in ways that boosted consumption through consumer debt. This paper considers the role of credit scoring in the recent financial crisis, concluding that even if credit scoring had worked as intended under its own terms, the practice would not have been enough to limit the defaulting of mortgage borrowers under conditions of falling house prices. The broader economic problem is the crippling amount of consumer debt involved; hence the paper places credit scori

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de Paula, Daniel Abreu Vasconcellos, Rinaldo Artes, Fabio Ayres, and Andrea Maria Accioly Fonseca Minardi. "Estimating credit and profit scoring of a Brazilian credit union with logistic regression and machine-learning techniques." RAUSP Management Journal 54, no. 3 (2019): 321–36. http://dx.doi.org/10.1108/rausp-03-2018-0003.

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Purpose Although credit unions are nonprofit organizations, their objectives depend on the efficient management of their resources and credit risk aligned with the principles of the cooperative doctrine. This paper aims to propose the combined use of credit scoring and profit scoring to increase the effectiveness of the loan-granting process in credit unions. Design/methodology/approach This sample is composed by the data of personal loans transactions of a Brazilian credit union. Findings The analysis reveals that the use of statistical methods improves significantly the predictability of def

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Seleznyova, Zinaida. "Applying Complementary Credit Scores to Calculate Aggregate Ranking." Journal of Corporate Finance Research / Корпоративные Финансы | ISSN: 2073-0438 15, no. 3 (2021): 5–12. http://dx.doi.org/10.17323/j.jcfr.2073-0438.15.3.2021.5-13.

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Researchers have been improving credit scoring models for decades, as an increase in the predictive ability of scoring even by a small amount can allow financial institutions to avoid significant losses. Many researchers believe that ensembles of classifiers or aggregated scorings are the most effective. However, ensembles outperform base classifiers by thousandths of a percent on unbalanced samples. This article proposes an aggregated scoring model. In contrast to previous models, its base classifiers are focused on identifying different types of borrowers. We illustrate the effectivene

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Idczak, Adam Piotr. "Remarks on Statistical Measures for Assessing Quality of Scoring Models." Acta Universitatis Lodziensis. Folia Oeconomica 4, no. 343 (2019): 21–38. http://dx.doi.org/10.18778/0208-6018.343.02.

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Granting a credit product has always been at the heart of banking. Simultaneously, banks are obligated to assess the borrower’s credit risk. Apart from creditworthiness, to grant a credit product, banks are using credit scoring more and more often. Scoring models, which are an essential part of credit scoring, are being developed in order to select those clients who will repay their debt. For lenders, high effectiveness of selection based on the scoring model is the primary attribute, so it is crucial to gauge its statistical quality. Several textbooks regarding assessing statistical quality o

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Saraswati, Mei Sita, Moch Abdul Mukid, and Abdul Hoyyi. "METODE GENERALIZED MEAN DISTANCE-BASED K-NEAREST NEIGHBOR CLASSIFIER (GMDKNN) UNTUK ANALISIS CREDIT SCORING CALON DEBITUR KREDIT TANPA AGUNAN (KTA)." Jurnal Gaussian 8, no. 1 (2019): 149–60. http://dx.doi.org/10.14710/j.gauss.v8i1.26629.

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Unsecured Credit is one of the credit facilities provided by banks, where the prospective debtor can borrow some amount of fund from the bank without having to provide collateral. Credit scoring is a process that aims to assess the worthiness of credit applications and classify the credit applicants into prospective debtors whose the credit application is worthy to be accepted and prospective debtors whose the credit application should be rejected. One of the statistical methods that can be applied in examining the analysis of credit scoring is the Generalized Mean Distance-Based k-Nearest Nei

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Hoai, Linh Do, Thu Hang Luong Thi, Thang Nguyen Xuan, and Linh Mai Ngoc. "Credit Scoring Application at Banks: Mapping to Basel II." Journal of Social and Political Sciences 2, no. 1 (2019): 83–89. https://doi.org/10.31014/aior.1991.02.01.51.

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Credit scoring is a process to find out the numerical assessment that mainly relied on an analysis borrower' profile. A credit score is primarily based on a credit report information commonly sourced from customer files and independent sources. Lenders, such as banks use credit scores to evaluate the potential risk posed by lending money to consumers and to mitigate losses due to bad debt. Hence, credit scoring is a powerful tool. By examining knowledge in Credit scoring as well as Basel II fundamentals, the author proposed a mapping process to match Credit score application at banks in ac

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Bernardelli, Michał, Zbigniew Korzeb, and Paweł Niedziółka. "Multidimensional disagreement of the credit ratings and ESG scorings granted to European banks. Are credit ratings related to ESG scorings?" Economics and Environment 92, no. 1 (2025): 830. https://doi.org/10.34659/eis.2025.92.1.830.

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The study covers 53 largest European banks and the largest banks from CEE countries. Credit ratings from 3 leading rating agencies and 8 ESG scoring providers are considered. The similarity is measured by cosine distance and Spearman rank correlation. The analysis shows a high similarity between the credit ratings with S&P’s ratings being relatively more conservative. A much more significant divergence characterises the pairwise comparison of ESG scores. As the C/I ratio increases, the similarity of credit ratings increases, and sovereign banks have the relatively highest correlation of cr

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Shannia Angelia Rahardjo, Tarsisius Murwadji, and Helza Nova Lita. "Efektivitas Hukum Jaminan Performa Kredit UMKM Melalui Penerapan Innovative Credit Scoring." Jembatan Hukum : Kajian ilmu Hukum, Sosial dan Administrasi Negara 2, no. 2 (2025): 73–81. https://doi.org/10.62383/jembatan.v2i2.1607.

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Micro, Small and Medium Enterprises (MSMEs) play a significant role in the economy, but often face obstacles in accessing financing. Credit performance guarantee is one of the mechanisms to mitigate risks for financial institutions. Along with the development of financial technology, innovative credit scoring emerged as an alternative method of assessing the creditworthiness of MSMEs. This research aims to analyze the legal effectiveness of the application of MSME credit performance guarantees based on innovative credit scoring. This research uses a normative juridical method with qualitative

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Rigopoulos, Georgios. "A Feature Elimination Machine Learning Model for Credit Assessment and Repayment Behavior Prediction in Marketplace Lending." WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS 21 (November 29, 2024): 2335–44. http://dx.doi.org/10.37394/23207.2024.21.192.

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With the rapid development of the credit industry and the advent of marketplace lending, credit scoring models play a vital role in reducing the risk exposure for lenders. However, traditional credit scoring models like the FICO Score make it hard for people with weak credit history to acquire credit services. Credit scoring models based on machine learning can provide accurate assessments for such thin-credit people, but a lot of private data, like social media activities, are used during the evaluation procedure. In this work, a credit scoring approach with a focus on marketplace lending is

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Shen, Feng, Run Wang, and Yu Shen. "A COST-SENSITIVE LOGISTIC REGRESSION CREDIT SCORING MODEL BASED ON MULTI-OBJECTIVE OPTIMIZATION APPROACH." Technological and Economic Development of Economy 26, no. 2 (2019): 405–29. http://dx.doi.org/10.3846/tede.2019.11337.

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Credit scoring is an important process for peer-to-peer (P2P) lending companies as it determines whether loan applicants are likely to default. The aim of most credit scoring models is to minimize the classification error rate, which implies that all classification errors bear the same cost; however, in reality, there is a significant cost-sensitive problem in credit scoring methods. Therefore, in this paper, a new cost-sensitive logistic regression credit scoring model based on a multi-objective optimization approach is proposed that has two objectives in the cost-sensitive logistic regressio

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Mohammad, Khanbabaei, and Alborzi Mahmood. "The Use of Genetic Algorithm, Clustering and Feature Selection Techniques in Construction of Decision Tree Models for Credit Scoring." International Journal of Managing Information Technology (IJMIT) 5, no. 4 (2013): 13 to 32. https://doi.org/10.5281/zenodo.3828274.

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Decision tree modelling, as one of data mining techniques, is used for credit scoring of bank customers. The main problem is the construction of decision trees that could classify customers optimally. This study presents a new hybrid mining approach in the design of an effective and appropriate credit scoring model. It is based on genetic algorithm for credit scoring of bank customers in order to offer credit facilities to each class of customers. Genetic algorithm can help banks in credit scoring of customers by selecting appropriate features and building optimum decision trees. The new propo

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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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Taiwo Inumidun Adegoke, Onyeka Chrisanctus Ofodile, Nneka Adaobi Ochuba, and Olatunji Akinrinola. "Evaluating the fairness of credit scoring models: A literature review on mortgage accessibility for under-reserved populations." GSC Advanced Research and Reviews 18, no. 3 (2024): 189–99. http://dx.doi.org/10.30574/gscarr.2024.18.3.0104.

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This literature review critically evaluates the fairness of credit scoring models concerning mortgage accessibility for under-reserved populations. The review scrutinizes a diverse range of scholarly articles, reports, and empirical studies spanning various disciplines, including finance, economics, sociology, and public policy. It examines the methodologies, findings, and limitations of existing research to illuminate the multifaceted dimensions of credit scoring fairness and its implications for mortgage accessibility. Firstly, the review outlines the conceptual framework of credit scoring f

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Taiwo, Inumidun Adegoke, Chrisanctus Ofodile Onyeka, Adaobi Ochuba Nneka, and Akinrinola Olatunji. "Evaluating the fairness of credit scoring models: A literature review on mortgage accessibility for under-reserved populations." GSC Advanced Research and Reviews 18, no. 3 (2024): 189–99. https://doi.org/10.5281/zenodo.11217326.

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This literature review critically evaluates the fairness of credit scoring models concerning mortgage accessibility for under-reserved populations. The review scrutinizes a diverse range of scholarly articles, reports, and empirical studies spanning various disciplines, including finance, economics, sociology, and public policy. It examines the methodologies, findings, and limitations of existing research to illuminate the multifaceted dimensions of credit scoring fairness and its implications for mortgage accessibility. Firstly, the review outlines the conceptual framework of credit scoring f

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Li, Xinyi. "Optimization Strategy of Credit Scoring System based on Support Vector Machine." Advances in Engineering Technology Research 9, no. 1 (2024): 558. http://dx.doi.org/10.56028/aetr.9.1.558.2024.

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This article proposes a novel optimization strategy for credit scoring systems that exploits the capabilities of SVM. Focusing on the importance of personal credit scoring in today's credit dynamics, the article explores SVM's versatility in various domains through a literature review. The theoretical background underscores the unique approach and computational efficiency of SVM. The optimization strategy encompasses four critical aspects: debt solvency, earning potential, operational prowess, and growth capability using metrics such as asset-liability ratios. Experimental validation with cred

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Fata, Haris Diyaul, Gita Indah Marthasari, and Yufis Azhar. "Sistem Pendukung Keputusan Kelayakan Kredit pada PT. BPR Mitra Catur Mandiri Menggunakan Metode Credit Scoring." Jurnal Repositor 2, no. 5 (2020): 649. http://dx.doi.org/10.22219/repositor.v2i5.608.

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Abstrak Kredit adalah suatu cara yang dapat dilakukan untuk mendapatkan modal usaha. Tetapi terkadang pihak bank mengalami kesulitan dalam melakukan penentuan kredit, hal ini dikarenakan terdapat beberapa kriteria yang tidak terpenuhi oleh calon nasabah. Maka dibutuhkan suatu sistem yang dapat mempermudah petugas bank dalam melakukan penentuan kelayakan kredit, yaitu dengan membangun sistem pendukung keputusan kelayakan kredit menggunakan metode credit scoring. Dalam penentuan kredit, metode credit scoring melakukan perhitungan berdasarkan kriteria-kriteria yang ada, sehingga dapat dihasilkan

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Barhdadi, M., B. Benyacoub, and M. Ouzineb. "A hybrid variable neighborhood search with bootstrap resampling technique for credit scoring problem." Mathematical Modeling and Computing 11, no. 1 (2024): 109–19. http://dx.doi.org/10.23939/mmc2024.01.109.

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Credit scoring models have played a vitally important role in the granting credit by lenders and financial institutions. Recently, these have gained more attention related to the risk management practice. Many modeling techniques have been developed to evaluate the worthiness of borrowers. This paper presents a credit scoring model via one of local search methods – variable neighborhood search (VNS) algorithm. The optimizing VNS neighborhood structure is a useful method applied to solve credit scoring problems. By simultaneously tuning the neighborhood structure, the proposed algorithm generat

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Bazzi, Mehdi, Hasna Chamlal, Kharroubi Ahmed El, and Tayeb Ouaderhman. "Intelligent credit scoring system using knowledge management." International Journal of Artificial Intelligence (IJ-AI) 8, no. 4 (2019): 391–98. https://doi.org/10.11591/ijai.v8i4.pp391-398.

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Promoting entrepreneurship among Moroccan young people has been challenged by a plethora of economic and social problems in the aftermath of the Arab Spring. Several government programs have been set up for young entrepreneurs. Thus, faced with the large number of credit applications solicited by these young entrepreneurs, banks resorted to artificial intelligence techniques. In this respect, this article aims at proposing a decision-making system enabling the bank to automate its credit granting process. It is a tool that allows the bank, in the first instance, to select promising projects th

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Stąpor, Katarzyna, Tomasz Smolarczyk, and Piotr Fabian. "Heteroscedastic discriminant analysis combined with feature selection for credit scoring." Statistics in Transition new series 17, no. 2 (2016): 265–80. http://dx.doi.org/10.59170/stattrans-2016-014.

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Credit granting is a fundamental question and one of the most complex tasks that every credit institution is faced with. Typically, credit scoring databases are often large and characterized by redundant and irrelevant features. An effective classification model will objectively help managers instead of intuitive experience. This study proposes an approach for building a credit scoring model based on the combination of heteroscedastic extension (Loog, Duin, 2002) of classical Fisher Linear Discriminant Analysis (Fisher, 1936, Krzyśko, 1990) and a feature selection algorithm that retains suffic

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Nur Salima, Tsania, Mukaram, and Azizah Sri Aulia Aripin. "ANALYSIS OF THE EFFECT OF MANAGEMENT OF RECEIVABLES RISK WITH CREDIT SCORING ON CREDIT COLLECTIBILITY." Applied Business and Administration Journal 3, no. 1 (2024): 26–38. http://dx.doi.org/10.62201/abaj.v3i1.80.

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A good management of account receivable is needed to encourage company revenues to be able to fulfill financing needs. One way to manage receivables is to do credit scoring. This research was conducted to examine the effect, direction, and significance of credit scoring on credit collectability at PT Krakatau Steel in 2022 to 2023. In this study, the data used is secondary data, obtained from company archives. The population of this study is all consumers registered in the company, namely as many as 709 companies. The number of samples taken was 228 samples with the criteria of consumers who a

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