Relevant bibliographies by topics / Credit scoring

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Credit scoring'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 July 2024

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Journal articles on the topic "Credit scoring"

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Crook, J. N., D. E. Edelman, and L. C. Thomas. "Credit Scoring." Journal of the Operational Research Society 56, no. 9 (September 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 (December 15, 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 benefit the regulators. Validated on two real-world credit datasets, empirical results reveal that credit scoring models with good predictive accuracy or profitability do not necessarily provide low capital charge requirement error, which further highlights the importance of regulatory scoring framework. The family of gradient boosting decision tree (GBDT) provides significantly better average performance than industry benchmarks and deep multilayer perceptron network, especially when financial stability is the primary focus. To further examine the robustness of the proposed regulatory scoring, sampling techniques, cut-off value modification, and probability calibration are employed within the framework and the main conclusions hold in most cases. Furthermore, the analysis on the interpretability via TreeSHAP algorithm alleviates the concerns on transparency of GBDT-based models, and confirms the important roles of loan characteristics, borrowers’ solvency and creditworthiness as powerful predictors in credit scoring. Finally, the managerial implications for both financial institutions and regulators are discussed.
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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 (February 13, 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 was conducted on five financial aspects consisting of 14 research variables, including (i) liquidity aspects, including current ratio and quick ratio variables; (ii) solvency aspects, including debt asset ratio and equity ratio variables; (iii) profitability aspects including return on net assets, operating profit ratio, price to earnings ratio variables, (iv) activity aspects including total asset turnover, accounts receivable turnover, inventory turnover, current assets turnover, and (v) growth aspects including operating income growth rate, total assets growth rate, and operating profit growth rate variables. The analysis tool uses Logistic Regression through an assessment conducted on the company's credit rating as a proxy for the dependent variable, worth one if the credit application is feasible and worth 0 if the credit application is not feasible with a cut-off point of 0.5. The results show that credit scoring modeling for corporate credit is significantly formed from liquidity (CR) and solvency (DER) aspects. Out of 61 companies classified as not eligible for credit facilities, 58 companies were classified correctly, and out of 39 companies classified as eligible, 29 companies were classified correctly. The overall percentage shows 68.0, meaning that the logistic regression model has an accuracy of 68%.
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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 (January 9, 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 on nominal ones. The key feature of this method, called learning vector quantization and particle swarm optimization (LVQ + PSO), is the finding of a reduced set of classifying rules. This is possible because of the combination of a competitive neural network with an optimization technique. Findings These rules constitute a predictive model for credit risk approval. The reduced quantity of rules makes this method useful for credit officers aiming to make decisions about granting a credit. It also could act as an orientation for borrower’s self evaluation about her/his creditworthiness. Research limitations/implications In spite of the fact that conducted tests showed no evidence of dependence between results and the initial size of the LVQ network, it is considered desirable to repeat the measurements using an LVQ network of minimum size and a version of variable population PSO to adequately explore the solution space in the future. Practical implications In the past decades, there has been an increase in consumer credit. Retail banking is a growing industry. Not only has there been a boom in credit card memberships, specially in emerging economies, but also an increase in small consumption credits. For example, it is very common in emerging economies that families buy home appliances on installments. In those countries, the association of a home appliance shop with a financial institution is usual, to provide customers with quick-decision credit line facilities. The existence of such a financial instrument aids to boost sales. This association generates conflict of interests. On one hand, the home appliance shop wants to sell products to all customers. Therefore, it is in its best interest to promote a generous credit policy. On the other hand, the financial institution wants to maximize the revenue from credits, leading to a strict surveillance of loan losses. Having a fair and transparent credit-granting policy favors a good business relationship between home appliances shops and financial institutions. One way of developing such a policy is to construct objective rules to decide to grant or deny a credit application. Social implications Better credit decision rules generate enhanced risk sharing. In addition, it improves transparency in credit acceptance decisions, giving less room to arbitrary decisions. Originality/value This study develops a new method that combines a competitive neural network and an optimization technique. It was applied to a real database of a financial institution in a developing country.
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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 (January 1, 2007): 5–22. http://dx.doi.org/10.1111/j.1540-627x.2007.00195.x.

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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 (December 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 classification rules for credit granting. To achieve this, linguistic variables were used to help the analyst interpret the information available from the credit officer. The method proposed here combines the use of fuzzy logic with a neural network and a variable population optimization technique to obtain fuzzy classification rules. It was tested with three databases from financial entities in Ecuador — one credit and savings cooperative and two banks that grant various types of credits. To measure its performance, three benchmarks were used: accuracy, number of classification rules generated, and antecedent length. The results obtained indicate that the hybrid model that is proposed performs better than its previous versions due to the addition of fuzzy logic. At the end of the article, our conclusions are discussed and future research lines are suggested.
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Medina, Rosa Puertas, and Maria Luisa Martí Selva. "Análisis del credit scoring." Revista de Administração de Empresas 53, no. 3 (June 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 (April 10, 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 experts. The radial-basis function neural networks were used as specific expert models. As a result of a comparative analysis of the efficiency of the used ensemble technologies it was confirmed that the algorithm for constructing an ensemble based on the specialization of experts proposed by the authors is the most adapted for the task of assessing credit risk.
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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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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 (May 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 the banks with more fundamental underwriting problems such as inexperienced loan officers, inadequate credit procedures, underdeveloped internal and external credit info – system , persistent arrears problems, etc. However credit scoring is only useful , and can make reliable predictions , if the credit scoring models are properly made, data base updated and credit scoring limitations are properly understood, which is very difficult in the turbulent economic environment as it is the world economy in the recent years. Therefore, the paper objective is to analyze if the banking industry can prevent credit default payments through the implementation of the credit scoring models and what are the parameters and the weights that need to be incorporated, so that the models to be more effective in the period of financial crises.
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Dissertations / Theses on the topic "Credit scoring"

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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 thesis reports the findings of a range of empirical research. Chapter Four reports the findings of the initial industry survey, which examines mortgage credit scoring. Chapter Five reports the findings of the interview programme conducted to augment the survey. Chapter Six describes a case study undertaken, which facilitated the development of a bespoke mortgage scorecard. The development and subsequent performance of the scorecard are examined. Chapter Seven provides the findings of a pilot study in which mortgage lender performance is benchmarked. This principle conclusions of this thesis are; (i) A range of organisational factors require to be controlled if scoring is to afford the risk, process and cost benefits sought by those who adopt it either as an alternative to judgmental evaluation, or to augment such a system. (ii) Subject to those controls being in place, credit scoring can outperform judgmental evaluation in predicting mortgage default.
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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. To aid in the understanding of financial data quantile regression and a variable transformation is applied to a 'missed payments' database resulting in a greater understanding and more accurate description of the data. A less standard sampling and modelling approach is also employed which may give increased predictive power on independent data not used for model construction. The third part of this thesis is concerned with regression modelling in situations where the dimensionality is large. Latent variable modelling of explanatory and binary response variables is suggested which can be maximised using an EM algorithm. Less progress than anticipated has been accomplished in this area. The first two parts of this thesis have suggested novel statistical methodology that can provide benefits over current industry practices, both of which are adapted to real credit scoring applications.
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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
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 independentes quantitativas e qualitativas que influenciam a probabilidade do crédito ser aprovado ou não. Os resultados deste estudo mostram que o modelo de credit scoring se apresenta adequado no ajustamento aos dados, obtendo uma classificação correta para cerca de 77% dos clientes. Contudo, os resultados encontrados fornecem informações importantes para auxílio no processo de tomada de decisões de concessão de crédito e gerenciamento do crédito bancário, podendo assim contribuir para a redução do número de clientes inadimplentes e dos respetivos custos.
In order to improve credit analysis and management services, financial institutions have developed the credit scoring model. This model is used by these institutions to predict credit risk in the process of making a credit granting decision. The objective of this work is to develop a credit scoring model from a sample of 1000 credit claimants extracted from the credit portfolio of a German bank. For this, a probit model was estimated, considering 25 independent quantitative and qualitative variables that influence the probability of credit being approved or not. The results of this study show that the credit scoring model is adequate in the adjustment to the data, obtaining a correct classification for about 77% of the clients. However, the results found provide important information to aid in the decision-making process of credit granting and bank credit management, thus contributing to the reduction of overdue customers and their costs.
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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. The practical part is dedicated to the comparison of two methods for approval of applicants for the loan at the non-bank and bank institutions. The final chapter presents a summary of both methods used for approval and the authoress' subjective evaluation and recommendations for improving both used methods.
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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) from two classifiers. In Chapter 6 we consider different approaches to reject inference, the procedure of allocating class membership probabilities to the rejects. One reason for needing reject inference is to reduce the sample selection bias that results from using a sample consisting only of accepted applicants to build new scorecards. We show that the characteristic vectors for the rejects do not contain information about the parameters of the observed data likelihood, unless extra information or assumptions are included. Methods of reject inference which incorporate additional information are proposed. In Chapter 7 we make comparisons of a range of different parametric and nonparametric classification techniques for credit scoring: linear regression, logistic regression, projection pursuit regression, Poisson regression, decision trees and decision graphs. We conclude that classifier performance is fairly insensitive to the particular technique adopted. In Chapter 8 we describe the application of the k-NN method to credit scoring. We propose using an adjusted version of the Eucidean distance metric, which is designed to incorporate knowledge of class separation contained in the data. We evaluate properties of the k-NN classifier through empirical studies and make comparisons with existing techniques.
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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 dimension of the sets, length, the included percentage defaults in data and effect of variables on estimation. Moreover, application of some important statistical and non-statistical methods on Turkish credit default data is provided and the method accuracies are compared for Turkish market. Finally, ratings on the results of best method is done by using receiver operating characteristic curve.
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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 medical and engineering fields, is an area of statistics that typically deals with censored lifetime data. The theoretical developments in this thesis revolve around linear regression for censored data, in particular the Buckley-James method. The Buckley-James method is analogous to linear regression and gives estimates of the mean expected lifetime given a set of explanato ry variables. The first development is a measure of fit for censored regression, similar to the classical r-squared of linear regression. Next, the variable-reduction technique of stepwise selection is extended to the Buckley-James method. For the last development, the Buckley-James algorithm is altered to incorporate non-linear regression methods such as neural networks and Multivariate Adaptive Regression Splines (MARS). MARS shows promise in terms of predictive power and interpretability in both simulation and empirical studies. The practical section of the thesis involves using the new techniques to predict the time to default and time to repayment of unsecured personal loans from a database obtained from a major Australian bank. The analyses are unique, being the first published work on applying Buckley-James and related methods to a large-scale financial database.
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Books on the topic "Credit scoring"

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

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

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

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

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

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

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Lewis, Edward M. An introduction to credit scoring. San Rafael, Calif: Athena Press, 1990.

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

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

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

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Book chapters on the topic "Credit scoring"

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Doumpos, Michael, and Constantin Zopounidis. "Credit Scoring." In Multicriteria Analysis in Finance, 43–59. Cham: 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, 319–58. Hoboken, NJ, USA: 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, 119–30. Hoboken, NJ, USA: 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, 149–72. Hoboken, NJ, USA: 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, 390. Berlin, Heidelberg: 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, 17–43. Wiesbaden: 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, 72–86. London: 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, 923–1031. Boca Raton : CRC Press, 2020. | Series: Chapman and Hall/CRC financial mathematics series: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781315144597-15.

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Stanghellini, Elena. "Il credit scoring." In Unitext, 1–27. Milano: 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, 19–116. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49800-3_3.

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Conference papers on the topic "Credit scoring"

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Wei, Qiancheng, Ying Liu, and Kaichao Wu. "Transfer Learning Based Credit Scoring." In 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2021. http://dx.doi.org/10.1109/cscwd49262.2021.9437749.

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Kochański, Błażej. "Economics of credit scoring management." In International Days of Statistics and Economics 2019. Libuše Macáková, MELANDRIUM, 2019. http://dx.doi.org/10.18267/pr.2019.los.186.73.

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Demajo, Lara Marie, Vince Vella, and Alexiei Dingli. "Explainable AI for Interpretable Credit Scoring." In 10th International Conference on Advances in Computing and Information Technology (ACITY 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101516.

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With the ever-growing achievements in Artificial Intelligence (AI) and the recent boosted enthusiasm in Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. Credit scoring helps financial experts make better decisions regarding whether or not to accept a loan application, such that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. An interesting concept that has been recently introduced is eXplainable AI (XAI), which focuses on making black-box models more interpretable. In this work, we present a credit scoring model that is both accurate and interpretable. For classification, state-of-the-art performance on the Home Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is achieved using the Extreme Gradient Boosting (XGBoost) model. The model is then further enhanced with a 360-degree explanation framework, which provides different explanations (i.e. global, local feature-based and local instance-based) that are required by different people in different situations. Evaluation through the use of functionallygrounded, application-grounded and human-grounded analysis show that the explanations provided are simple, consistent as well as satisfy the six predetermined hypotheses testing for correctness, effectiveness, easy understanding, detail sufficiency and trustworthiness.
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Wang, Di, and Zuoquan Zhang. "Credit Scoring Using Information Fusion Technique." In 2018 7th International Conference on Digital Home (ICDH). IEEE, 2018. http://dx.doi.org/10.1109/icdh.2018.00036.

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Chen, Haojie, Minghui Jiang, and Xue Wang. "Bayesian ensemble assessment for credit scoring." In 2017 4th International Conference on Industrial Economics System and Industrial Security Engineering (IEIS). IEEE, 2017. http://dx.doi.org/10.1109/ieis.2017.8078596.

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Saad, Shakila, Noryati Ahmad, and Maheran Mohd Jaffar. "Forecasting the value of credit scoring." In PROCEEDINGS OF THE 24TH NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES: Mathematical Sciences Exploration for the Universal Preservation. Author(s), 2017. http://dx.doi.org/10.1063/1.4995844.

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Jiang, Minghui, Haojie Chen, Pei Xu, and Xue Wang. "Bayesian ensemble assessment for credit scoring." In International Conference on Modern Engineering Soultions for the Industry. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/mesi140401.

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Shi, Yong. "China's national personal credit scoring system." In the 18th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2339530.2339596.

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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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Yao, Ping. "Credit Scoring Using Ensemble Machine Learning." In 2009 Ninth International Conference on Hybrid Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/his.2009.264.

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Reports on the topic "Credit scoring"

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Sterbik-Lamina, Jaro, ed. Credit Scoring in �sterreich. Vienna: 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.], February 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 research methods such as analysis of the literature on banking scoring; artificial intelligence methods for scoring; modeling of scoring estimation algorithm using neural networks, empirical method for determining the optimal parameters of the training model; method of object-oriented design and programming. The result of the work is a neural network scoring model with high accuracy of calculations, an implemented system of automatic customer lending.
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Agarwal, Sumit, Paige Skiba, and Jeremy Tobacman. Payday Loans and Credit Cards: New Liquidity and Credit Scoring Puzzles? Cambridge, MA: National Bureau of Economic Research, January 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. Jakarta, 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. Jakarta, 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. Cambridge, MA: National Bureau of Economic Research, April 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. Jakarta, Indonesia: 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. Jakarta, Indonesia: 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. Cambridge, MA: National Bureau of Economic Research, March 2019. http://dx.doi.org/10.3386/w25694.

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