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1
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.
2
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.
4
Fernandes, António Francisco de Melo. "Credit scoring : uma análise econométrica." Master's thesis, Instituto Superior de Economia e Gestão, 2017. http://hdl.handle.net/10400.5/14342.
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Mestrado em Métodos Quantitativos para a Decisão Económica e Empresarial<br>Com intenção de melhorar os serviços de análise e gestão de crédito, as instituições financeiras desenvolveram o modelo credit scoring. Este modelo é utilizado por estas instituições para previsão do risco de crédito no processo da tomada de decisão de concessão de crédito. O objetivo deste trabalho, é desenvolver um modelo de credit scoring a partir de uma amostra de 1000 solicitantes de créditos extraídos da carteira de crédito de um banco alemão. Para tal, estimou-se um modelo probit, considerando-se 25 variáveis 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.<br>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.<br>info:eu-repo/semantics/publishedVersion
5
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.
6
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.
8
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.
10
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.
11
Frohlich, Robert M. Jr. "Credit Scoring in a Hospital Setting." UNF Digital Commons, 1997. http://digitalcommons.unf.edu/etd/97.
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This is a study of the relationship between consumer credit scoring and the resolution of a patient's account for hospital services. Accounts studied were classified as Good accounts or Bad accounts based upon their final resolution. Bad accounts were those written-off to bad debt with Good accounts being all others.
The probability of predicting a patient's account being either Good or Bad was based upon a consumer credit scoring process. The null hypothesis of this study was that the consumer credit scoring process would not provide any indication about the outcome or resolution of the account. Analysis of the credit score and the outcome of the hospital account suggested the consumer credit score would indicate the patient's reliability in taking responsibility for the account. Based on the confidence given to credit scoring in consumer markets and the results of this study, the consumer credit score would have value for the health care industry.
12
Bijak, Katarzyna. "Selected modelling problems in credit scoring." Thesis, University of Southampton, 2013. https://eprints.soton.ac.uk/359285/.
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This research addresses three selected modelling problems that occur in credit scoring. The focus is on segmentation, modelling Loss Given Default (LGD) for unsecured loans and affordability assessment. It is usually expected that segmentation, i.e. dividing the population into a number of groups and building separate scorecards for them, will improve the model performance. The most common statistical methods for segmentation are the two-step approaches, where logistic regression follows Classification and Regression Trees (CART) or Chi-square Automatic Interaction Detection (CHAID) trees. In this research, these approaches and a simultaneous method, in which both segmentation and scorecards are optimised at the same time: Logistic Trees with Unbiased Selection (LOTUS), are applied to the data provided by two UK banks and a European credit bureau. The model performance measures are compared to assess an improvement due to the segmentation. For unsecured retail loans, LGD is often found difficult to model. In the frequentist (classical) two-step approach, the first model (logistic regression) is used to separate positive values from zeroes and the second model (e.g. linear regression) is applied to estimate these values. Instead, one can build a Bayesian hierarchical model, which is a more coherent approach. In this research, Bayesian methods and the frequentist approach are applied to the data on personal loans provided by a UK bank. The Bayesian model generates an individual predictive distribution of LGD for each loan, whose potential applications include approximating the downturn LGD and stress testing LGD under Basel II. An applicant’s affordability (ability to repay) is often checked using a simple, static approach. In this research, a theoretical framework for dynamic affordability assessment is proposed. Both income and consumption are allowed to vary over time and their changes are described with random effects models for panel data. On their basis a simulation is run for a given applicant. The ability to repay is checked over the life of the loan and for all possible instalment amounts. As a result, a probability of default is assigned to each amount, which can help find the maximum affordable instalment. This is illustrated with an example based on artificial data.
13
Sundbom, Tobias. "Mathematical programming based approaches in credit scoring." Thesis, Uppsala University, Department of Mathematics, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-120980.
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Aldgate, Hannah Jane. "Credit application scoring with Gaussian spatial processes." Thesis, Imperial College London, 2006. http://hdl.handle.net/10044/1/1256.
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Credit scoring has been described as the most successful application of statistical and operational research methods to financial problems in recent decades. In this thesis methods analogous to those used in spatial modelling and prediction are applied to the problem of application scoring, a branch of credit scoring that involves deciding whether or not to offer credit and how much credit to offer. In particular, Gaussian spatial process (GSP) models, commonly employed in disease mapping, geostatistics and design, are explored in an approach that is novel in the credit scoring field. Credit scoring methods are well established and usually involve computations of scores. By contrast, the focus of this work is to use best linear unbiased predictors in order to predict the probabilities of repayment for credit applications. A spatial structure for the model is provided by reformulating the data. Both theoretical and industry standard methods are used in order to assess the predictive competence of GSP models. In addition, the GSP model approach is compared with standard methods for application scoring and conclusions are made regarding the relevance of such models in this area
15
Webster, Gregg. "Bayesian logistic regression models for credit scoring." Thesis, Rhodes University, 2011. http://hdl.handle.net/10962/d1005538.
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The Bayesian approach to logistic regression modelling for credit scoring is useful when there are data quantity issues. Data quantity issues might occur when a bank is opening in a new location or there is change in the scoring procedure. Making use of prior information (available from the coefficients estimated on other data sets, or expert knowledge about the coefficients) a Bayesian approach is proposed to improve the credit scoring models. To achieve this, a data set is split into two sets, “old” data and “new” data. Priors are obtained from a model fitted on the “old” data. This model is assumed to be a scoring model used by a financial institution in the current location. The financial institution is then assumed to expand into a new economic location where there is limited data. The priors from the model on the “old” data are then combined in a Bayesian model with the “new” data to obtain a model which represents all the available information. The predictive performance of this Bayesian model is compared to a model which does not make use of any prior information. It is found that the use of relevant prior information improves the predictive performance when the size of the “new” data is small. As the size of the “new” data increases, the importance of including prior information decreases
16
Dyrberg, Rommer Anne. "Accounting-based credit-scoring models : econometric investigations /." Copenhagen, 2005. http://www.gbv.de/dms/zbw/505621215.pdf.
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Wainwright, Thomas A. "The geographies of securitisation and credit scoring." Thesis, University of Nottingham, 2009. http://eprints.nottingham.ac.uk/10949/.
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This thesis draws upon contemporary research in economic geography and the social sciences to explore the spatial relationships that exist between residential mortgage lenders, investment banks and investors and the subsequent geographies that are produced through these intertwined networks. The research is informed through empirical material collected through semi-structured interviews with directors and associates working in the financial sector to see how consumer mortgages are produced and restructured into debt securities. There is a particular focus on how the UK financial sector has undergone restructuring, as a consequence of the politics of financialisation since the 1990s, which aligned the residential mortgage market with the circuits of international capital. The thesis examines three areas of banking and finance to comprehend how retail mortgages have become embedded within international finance. First, the thesis explores how deregulation in the UK initiated a spatial reorganisation of mortgage production networks and funding. Second, the research investigates the migration and adoption of automated decision-making technologies, highlighting how these devices have reshaped the geographies of banking, and are inherently geographical themselves. Third, the thesis focuses on how mortgages are (re)engineered into debt-securities, with a particular focus on how geography is used to mitigate credit and tax risks. It is argued that the restructuring of the UK retail sector and its increased integration with the international circuits of capital exacerbated the exposure of the UK’s economy to the effects of the international credit crunch. Furthermore, the thesis underlines the effect of geography which has shaped the adoption, of new financial technologies and strategies, through local regulations, epistemic cultures and histories.
18
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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Kelly, Mark Gerard. "Tackling change and uncertainty in credit scoring." Thesis, Open University, 1998. http://oro.open.ac.uk/54554/.
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Credit scoring methods summanse information on credit applicants. An assessment of creditworthiness is derived from this summary. This thesis is concerned with statistical methods of credit scoring. Much of the existing literature on credit scoring is concerned with comparing the predictive power of a wide variety of classification techniques. However, much of the published work concludes that classifier performance on credit data is relatively insensitive to the choice of statistical technique. Consequently, the techniques used in commercial credit scoring have remained broadly similar during recent years. This thesis investigates credit scoring from a more fundamental level, by considering the formulation of the credit problem. A review of the credit literature is given, focusing on areas that have been subjected to much recent research activity. Details of the data sets used throughout this thesis are provided and analysed using techniques common to the credit industry. Methods that capitalise on the uncertainty and flexibility in the definitions of the classes used to represent 'good' and 'bad' credit risks are proposed. Firstly, a class of models is described that permits the choice of class definition to be deferred until the time at which the classification is required. Secondly, a strategy for choosing a suitable definition which optimises some external criterion is introduced. In addition, an approach is presented that combats classifier deterioration resulting from the evolution of the underlying populations. This thesis is essentially concerned with the uncertainties and change inherent in credit scoring. We present novel ways in which these properties may be incorporated in the formulation of the credit problem.
20
Gryffenberg, Ludwig Emil. "Credit scoring in terms of the National Credit Act / Ludwig Emil Gryffenberg." Thesis, North-West University, 2006. http://hdl.handle.net/10394/1455.
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The new National Credit Act (NCA), of which the first two phases have already been
implemented and of which the third and final phase will be implemented in full by 1 June
2007, will have a major impact on all credit providers in South Africa.
The microfinance industry has been subject to similar rules under the Microfinance
Regulatory Council (MFRC) and therefore this segment of the finance industry can be
used as an example of how to deal with the changes imposed by the NCA.
Of particular interest are the portions of the NCA regarding reckless lending, the
imposition of interest rate ceilings and the establishment of a national credit register.
Collectively these aspects create an environment for the application of credit scoring as a
risk reduction tool.
A retrospective analysis was done using the loan data of a lender in the microfinance
industry and from this data certain characteristics were identified which could be used to
develop a credit scoring model.
Two score cards were developed from the research data and these were then deployed in
a dual scoring matrix to combine their strengths.
The development data was then analysed in terms of these score cards and their relative
effectiveness was measured with a receiver operating characteristic curve (ROC curve)
and the Kolmogorov Smirnov test (KS test).
It is recommended that the manner in which characteristics is recorded on the credit
application should be improved and that the improved information be re-evaluated at some
point in the future to re-calibrate the scorecard which will improve its effectiveness.
It is also recommended that a formal credit policy should be deployed which should serve
as a framework to improve the effectiveness of the credit scoring tool.<br>Thesis (M.B.A.)--North-West University, Potchefstroom Campus, 2007
21
Tombari, Davide. "Sperimentazione di Metodi Predittivi per il Credit Scoring." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18017/.
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A lungo sono state studiate tecniche di previsione di fallimento e di credit scoring nell'ambito di concessione di prestiti bancari. Recentemente nuove tecniche come il machine learning e le reti neurali hanno portano cambiamenti significativi in questa area.
Sulla base di precedenti studi scientifici si vogliono testare nuove tecniche finora poco utilizzate a causa della loro recente introduzione. Inizialmente verranno utilizzati metodi tradizionali già testati in precedenza, successivamente ci si concentrerà su modelli più recenti. A fronte dei risultati ottenuti saranno effettuate diverse considerazioni utilizzando un confronto con la letteratura, infine verranno tratte le opportune conclusioni.
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Hamilton, Robert. "[Credit] scoring : predicting, understanding and explaining consumer behaviour." Thesis, Loughborough University, 2005. https://dspace.lboro.ac.uk/2134/13053.
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This thesis stems from my research into the broad area of (credit) scoring and the predicting, understanding and explaining of consumer behaviour. This research started at the Univers1ty of Edinburgh on an ESRC funded project in 1988. This work, which is being submitted as the partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough Unvers1ty, consists of an introductory chapter and a selection of papers published 1991 - 2001 (inclusive). The papers address some of the key issues and areas of interest and concern arising from the rapidly evolving and expanding credit (card) market and the highly competitive nature of the credit industry. These features were particularly evident during the late 1980's and throughout the 90's Chapter One provides a general background to the research and outlines some of the key (practical) issues involved in building a (credit) scorecard Additionally, it provides a brief summary of each of the research papers appearing in full in Chapters 2- 9 (inclusive) and ends with some general limitations and conclusions. The research papers appearing in Chapters 2-9 inclusive) are all concerned with predicting, understanding and explaining different types of consumer behaviour in relation to the use of credit cards. For example discriminating between 'GOOD' and 'BAD' repayers of credit card debt on the basis of different definitions of good and bad, the identification of 'slow payers' using different statistical methods; examining the characteristics of credit card users and non-users, and identifying the characteristics of credit card holders most likely to return their credit card.
23
Pereira, Gustavo Henrique de Araujo. ""Modelos de risco de crédito de clientes: Uma aplicação a dados reais"." Universidade de São Paulo, 2004. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-28122004-224257/.
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Modelos de customer scoring são utilizados para mensurar o risco de crédito de clientes de instituições financeiras. Neste trabalho, são apresentadas três estratégias que podem ser utilizadas para o desenvolvimento desses modelos. São discutidas as vantagens de cada uma dessas estratégias, bem como os modelos e a teoria estatística associada a elas. Algumas medidas de performance usualmente utilizadas na comparação de modelos de risco de crédito são descritas. Modelos para cada uma das estratégias são ajustados utilizando-se dados reais obtidos de uma instituição financeira. A performance das estratégias para esse conjunto de dados é comparada a partir de medidas usualmente utilizadas na avaliação de modelos de risco de crédito. Uma simulação também é desenvolvida com o propósito de comparar o desempenho das estratégias em condições controladas.<br>Customer scoring models are used to measure the credit risk of financial institution´s customers. In this work, we present three strategies that can be used to develop these models. We discuss the advantages of each of the strategies, as well as the models and statistical theory related with them. We fit models for each of these strategies using real data of a financial institution. We compare the strategies´s performance through some measures that are usually used to validate credit risk models. We still develop a simulation to study the strategies under controlled conditions.
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Araya, Osorio Pamela Jacquelinne. "El Credit Scoring en la Pequeña y Microempresa." Tesis, Universidad de Chile, 2005. http://repositorio.uchile.cl/handle/2250/107624.
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Memoria (licenciado en ciencias jurídicas y sociales)<br>El presente trabajo, describiendo y considerando la situación en que se encuentran las pequeñas y microempresas en nuestro país, analiza sus escenarios en relación al crédito bancario formal. Destacando que los problemas se dan en materia de acceso, monto y plazos.
Al mismo tiempo, diferencia los conceptos de crédito a la microempresa y microcrédito, atendiendo al origen de este último, para que el tema no se preste a confusión al momento de abordar una posible solución para hacer frente a los elevados costos de transacción y al riesgo crediticio del cual son presa, no sólo la microempresas, sino también las pequeñas, en el marco del crédito bancario tradicional.
Por último, se plantea al Credit Scoring, sistema de evaluación estadístico cuantitativo y luego de analizarlo pormenorizadamente, como una de las mediadas a adoptar por parte de los privados – la banca formal – con el objeto superar la brecha que genera la falta de historial crediticio en este sector empresarial.
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Baccega, Tanja <1993>. "P2P lending: credit scoring e analisi di performance." Master's Degree Thesis, Università Ca' Foscari Venezia, 2018. http://hdl.handle.net/10579/13110.
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L’innovazione tecnologica e la grave crisi finanziaria del 2008, hanno spinto enormemente l’evoluzione e l’incremento nel mondo dei Peer-to-Peer lending, soprattutto grazie alla loro capacità di agevolare il collegamento tra debitore e creditore a migliori condizioni di mercato. Focalizzando l’attenzione dello studio sui prestiti P2P personali, in questa analisi si vuole indagare se il rischio associato sia correttamente remunerato e quale sia l’entità della perdita in cui si potrebbe realmente incorrere.
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Frazzato, Viana Renato. "Técnicas de classificação aplicadas a credit scoring : revisão sistemática e comparação." Universidade Federal de São Carlos, 2015. https://repositorio.ufscar.br/handle/ufscar/7294.
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Previous issue date: 2015-12-18<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)<br>Nowadays the increasing amount of bank transactions and the increasing of data storage created a demand for risk evaluation associated with personal loans. It is very important for a company has a very good tools in credit risk evaluation because theses tools can avoid money losses. In this context, it is interesting estimate the default probability for a customers and, the credit scoring techniques are very useful for this task. This work presents a credit scoring literature review with and aim to give a overview covering many techniques employed in credit scoring and, a computational study is accomplished in order to compare some of the techniques seen in this text.<br>Com a crescente demanda por cr edito e muito importante avaliar o risco de cada opera ção desse tipo. Portanto, ao fornecer cr edito a um cliente e necess ario avaliar as chances do cliente n~ao pagar o empr estimo e, para esta tarefa, as t ecnicas de credit scoring s~ao aplicadas. O presente trabalho apresenta uma revis~ao da literatura de credit scoring com o objetivo de fornecer uma vis~ao geral das v arias t ecnicas empregadas. Al em disso, um estudo de simula c~ao computacional
e realizado com o intuito de comparar o comportamento de v arias t ecnicas apresentadas no estudo.
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Schwarz, Alexandra. "Lokale Scoring-Modelle." Lohmar Köln Eul, 2008. http://d-nb.info/990622835/04.
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Till, Robert John. "Predictive behavioural models in credit scoring and retail banking." Thesis, Imperial College London, 2002. http://hdl.handle.net/10044/1/7984.
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Lund, Anton. "Two-Stage Logistic Regression Models for Improved Credit Scoring." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-160551.
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This thesis has investigated two-stage regularized logistic regressions applied on the credit scoring problem. Credit scoring refers to the practice of estimating the probability that a customer will default if given credit. The data was supplied by Klarna AB, and contains a larger number of observations than many other research papers on credit scoring. In this thesis, a two-stage regression refers to two staged regressions were the some kind of information from the first regression is used in the second regression to improve the overall performance. In the best performing models, the first stage was trained on alternative labels, payment status at earlier dates than the conventional. The predictions were then used as input to, or to segment, the second stage. This gave a gini increase of approximately 0.01. Using conventional scorecutoffs or distance to a decision boundary to segment the population did not improve performance.<br>Denna uppsats har undersökt tvåstegs regulariserade logistiska regressioner för att estimera credit score hos konsumenter. Credit score är ett mått på kreditvärdighet och mäter sannolikheten att en person inte betalar tillbaka sin kredit. Data kommer från Klarna AB och innehåller fler observationer än mycket annan forskning om kreditvärdighet. Med tvåstegsregressioner menas i denna uppsats en regressionsmodell bestående av två steg där information från det första steget används i det andra steget för att förbättra den totala prestandan. De bäst presterande modellerna använder i det första steget en alternativ förklaringsvariabel, betalningsstatus vid en tidigare tidpunkt än den konventionella, för att segmentera eller som variabel i det andra steget. Detta gav en giniökning på approximativt 0,01. Användandet av enklare segmenteringsmetoder så som score-gränser eller avstånd till en beslutsgräns visade sig inte förbättra prestandan.
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Stepanova, Maria. "Using survival analysis methods to build credit scoring models." Thesis, University of Southampton, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364729.
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Oetama, Raymond Sunardi. "Dynamic credit scoring using payment prediction a dissertation submitted to Auckland University of Technology in fulfilment of the requirements for the degree of Master of Computer and Information Sciences, 2007." Abstract. Full dissertation, 2007.
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Thesis (MCIS - Computer and Information Sciences) -- AUT University, 2007.<br>Includes bibliographical references. Also held in print (x, 102 leaves : ill. ; 30 cm.) in City Campus Theses Collection (T 332.7 OET)
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Rychnovský, Michal. "Scoring Models in Finance." Master's thesis, Vysoká škola ekonomická v Praze, 2011. http://www.nusl.cz/ntk/nusl-72256.
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The aim of the present work is to describe the application of the logistic regression model to the field of probability of default modeling, and provide a brief introduction to the scoring development process used in financial practice. We start by introducing the theoretical background of the logistic regression model; followed by a consequent derivation of three most common scoring models. Then we present a formal definition of the Gini coefficient as a diversification power measure and derive the Somers-type formulas for its estimation. Finally, the key part of this work gives an overview of the whole scoring development process illustrated on the examples of real business data.
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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, à 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 ferramenta de apoio aos gestores de crÃdito na tomada de decisÃo. VÃrios modelos de avaliaÃÃo de risco sÃo adotados pelas instituiÃÃes, modelos de credit scoring, behavioral scoring, sÃo exemplos destes modelos. O modelo de credit scoring tem sido um dos mais usados, em especial para concessÃo de crÃdito a pessoas fÃsicas. Os modelos de credit scoring utilizam tÃcnicas como a anÃlise de discriminantes, programaÃÃo matemÃtica, econometria, redes neurais, entre outras, para atravÃs da anÃlise de caracterÃsticas particulares dos indivÃduos, estabelecer uma mÃtrica de separaÃÃo de bons e maus pagadores, atribuindo probabilidades diferentes de inadimplÃncia aos mesmos. A presente dissertaÃÃo tem como objetivo central analisar os determinantes de inadimplÃncia (pessoa fÃsica), usando uma abordagem economÃtrica com base no modelo Logit. O modelo utilizado foi um modelo para aprovaÃÃo de crÃdito na abertura de conta corrente, partindo de um estudo com uma amostra de 308 observaÃÃes (cadastros pessoas fÃsicas), baseados na experiÃncia real de uma instituiÃÃo financeira, cujo objetivo à atingir uma taxa de aprovaÃÃo de crÃdito tal que a receita mÃdia depois das perdas de emprÃstimos seja maximizada.<br>In the financial intermediation, banks focus on its main activity, allocating resources from clients with surplus to deficit clients. The uncertainty related to the characteristics or payment capacity of the clients establishes the risk and the need to search for new alternatives to protect the institutions from potential losses, which may reflect on lower profits. Besides the subjective issue of credit analysts, the use of quantitative models, based on statistical, mathematical or econometric practices are becoming an important tool to support credit managers on the decision making process. There are several models of risk evaluation, which are adopted by financial institutions such as the credit scoring and the behavioral scoring models. The credit-scoring model has been widely used, especially on the concession of individual credit. The credit scoring model uses techniques such as discriminant analysis, mathematic programming, econometrics, neural networks, among others, to analyze particular characteristics of individuals where it establishes a metric separation of good and bad payers, therefore providing different nonpayment status to each. This present dissertation has the main objective of analyzing the determinants of nonpayment status (individuals), using an econometric approach based on the Logit model. The model utilized was a model for approval of credit in the opening from the bill shackle, starting from a study with 308 observations (physical registers Persons), based in the real experience of a financial institution, whose objective is he reach a credit approval rate such that the medium prescription after the losses of loans be maximized.
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Khudnitskaya, Alesia S. [Verfasser]. "Improved Credit Scoring with Multilevel Statistical Modelling / Alesia S. Khudnitskaya." Dortmund : Universitätsbibliothek Technische Universität Dortmund, 2011. http://d-nb.info/1011568411/34.
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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 should give recommendations to the bank in terms of whether or not a loan should be given, or otherwise can give a probability in relation to whether the loan will be returned. Nowadays, a number of models have been designed, but there is no ideal classifier amongst these models since each gives some percentage of incorrect outputs; this is a critical consideration when each percent of incorrect answer can mean millions of dollars of losses for large banks. However, the LR remains the industry standard tool for credit-scoring models development. For this purpose, an investigation is carried out on the combination of the most efficient classifiers in credit-scoring scope in an attempt to produce a classifier that exceeds each of its classifiers or components. In this work, a fusion model referred to as ‘the Classifiers Consensus Approach’ is developed, which gives a lot better performance than each of single classifiers that constitute it. The difference of the consensus approach and the majority of other combiners lie in the fact that the consensus approach adopts the model of real expert group behaviour during the process of finding the consensus (aggregate) answer. The consensus model is compared not only with single classifiers, but also with traditional combiners and a quite complex combiner model known as the ‘Dynamic Ensemble Selection’ approach. As a pre-processing technique, step data-filtering (select training entries which fits input data well and remove outliers and noisy data) and feature selection (remove useless and statistically insignificant features which values are low correlated with real quality of loan) are used. These techniques are valuable in significantly improving the consensus approach results. Results clearly show that the consensus approach is statistically better (with 95% confidence value, according to Friedman test) than any other single classifier or combiner analysed; this means that for similar datasets, there is a 95% guarantee that the consensus approach will outperform all other classifiers. The consensus approach gives not only the best accuracy, but also better AUC value, Brier score and H-measure for almost all datasets investigated in this thesis. Moreover, it outperformed Logistic Regression. Thus, it has been proven that the use of the consensus approach for credit-scoring is justified and recommended in commercial banks. Along with the consensus approach, the dynamic ensemble selection approach is analysed, the results of which show that, under some conditions, the dynamic ensemble selection approach can rival the consensus approach. The good sides of dynamic ensemble selection approach include its stability and high accuracy on various datasets. The consensus approach, which is improved in this work, may be considered in banks that hold the same characteristics of the datasets used in this work, where utilisation could decrease the level of mistakenly rejected loans of solvent customers, and the level of mistakenly accepted loans that are never to be returned. Furthermore, the consensus approach is a notable step in the direction of building a universal classifier that can fit data with any structure. Another advantage of the consensus approach is its flexibility; therefore, even if the input data is changed due to various reasons, the consensus approach can be easily re-trained and used with the same performance.
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Souza, Victor Hugo Delvalle. "Estimação de escores binomiais correlacionados: uma aplicação em Credit Scoring." Universidade Federal de São Carlos, 2008. https://repositorio.ufscar.br/handle/ufscar/4520.
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Previous issue date: 2008-06-09<br>Financiadora de Estudos e Projetos<br>For the most part of modelings in the credit risk area, the most widely used model is the credit scoring, and as the main statistical technique, the binary logistic regression, used to determine whether a customer is a good or bad payer. In this academic work an alternative methodology is proposed, where the estimative is formed based on the scores obtained by customers; this means the response follows a binomial distribution. In this modeling the combined estimate of scores of various products used by customers is included, considering the correlation between these scores.<br>Em grande parte das modelagens na área de risco de crédito, o modelo mais utilizado é o credit scoring, e como técnica estatística principal a regressão logistica binária, utilizada para decidir se um cliente é bom ou mau pagador. Neste trabalho propomos uma metodologia alternativa, onde a estimativa é feita diretamente nos escores dos clientes, com issa a resposta segue uma distribuição binomial. Nessa modelagem incluimos ainda a estimativa conjunta dos escores de vários produtos utilizados pelos clientes, levando em consideração a correlação existente entre estes escores.
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Prazeres, Filho Jurandir. "Capacidade preditiva de Modelos Credit Scoring em inferência dos rejeitados." Universidade Federal de São Carlos, 2014. https://repositorio.ufscar.br/handle/ufscar/4583.
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Previous issue date: 2014-03-28<br>Universidade Federal de Sao Carlos<br>Granting credit to an applicant is a decision made in a context of uncertainty. At the moment the lender decides to grant a loan or credit sale there is always the possibility of loss, and, if it is associated with a probability, the decision to grant or not credit will be more reliable. In order to aid the decision to accept or not the request for applicants are used the credit scoring models, which estimate the probability of loss associated with granting credit. But one of the problems involving these models is that only information about the applicants accepted are used, which causes a sampling bias, because the rejected applicants are discarded. With the aim to solve this problem it can use rejected inference, which are considered individuals who have had credit application rejected. However, only considering rejected inference and one method of modeling data, usually, is not sufficient to get satisfactory predictive measures, and thus, were used combined results of three methods, logistic regression, analysis probit and decision tree. The purpose of this combination were to increase the predictive perfomance and the metrics used were sensitivity, specificity , positive predictive value, negative predictive value and accuracy. Through the application in data sets we concluded that the use of the combined results increased the predictive performance, specially regarding to sensitivity.<br>A concessão de crédito e uma decisão a ser tomada num contexto de incertezas. No momento em que o credor decide conceder um empréstimo, realizar um financiamento ou venda a prazo sempre existe a possibilidade de perda, e, se for atribuída uma probabilidade a esta perda, a decisão de conceder ou não credito será mais confiável. Com o objetivo de auxiliar a tomada de decisão em relação ao pedido de credito dos solicitantes são utilizados os modelos credit scoring, os quais estimam a probabilidade de perda associada a concessão de credito. Um dos problemas envolvendo estes modelos e que somente informações a respeito dos proponentes aceitos são utilizadas, o que causa um viés amostral, pois, os solicitantes recusados são descartados no processo de modelagem. Com intuito de solucionar este problema tem-se a inferência dos rejeitados, em que são considerados os indívíduos que tiveram pedido de credito rejeitado. No entanto, considerar a inferência dos rejeitados e o uso de somente um método de modelagem de dados, muitas vezes, não e suficiente para que se tenha medidas preditivas satisfatórias. Desta forma, foram utilizados resultados combinados de três metodologias, regressão logística, probit e árvore de decisão/classificação concomitantemente a utilização dos métodos de inferência dos rejeitados que incluem o uso de variável latente, reclassificação, parcelamento e ponderação. O objetivo dessa combinação foi aumentar a capacidade preditiva e as métricas utilizadas foram a sensibilidade, especificidade, valor preditivo positivo, valor preditivo negativo e acurácia. Através da aplicação em conjuntos de dados concluiu-se que a utilização dos resultados combinados aumentou a capacidade preditiva, principalmente, em relação a sensibilidade.
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Kraus, Anne. "Recent methods from statistics and machine learning for credit scoring." Diss., Ludwig-Maximilians-Universität München, 2014. http://nbn-resolving.de/urn:nbn:de:bvb:19-171439.
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Credit scoring models are the basis for financial institutions like retail and consumer credit banks. The purpose of the models is to evaluate the likelihood of credit applicants defaulting in order to decide whether to grant them credit. The area under the receiver operating characteristic (ROC) curve (AUC) is one of the most commonly used measures to evaluate predictive performance in credit scoring. The aim of this thesis is to benchmark different methods for building scoring models in order to maximize the AUC. While this measure is used to evaluate the predictive accuracy of the presented algorithms, the AUC is especially introduced as direct optimization criterion.
The logistic regression model is the most widely used method for creating credit scorecards and classifying applicants into risk classes. Since this development process, based on the logit model, is standard in the retail banking practice, the predictive accuracy of this proceeding is used for benchmark reasons throughout this thesis.
The AUC approach is a main task introduced within this work. Instead of using the
maximum likelihood estimation, the AUC is considered as objective function to optimize it directly. The coefficients are estimated by calculating the AUC measure with Wilcoxon-Mann-Whitney and by using the Nelder-Mead algorithm for the optimization. The AUC optimization denotes a distribution-free approach, which is analyzed within a simulation study for investigating the theoretical considerations. It can be shown that the approach still works even if the underlying distribution is not logistic.
In addition to the AUC approach and classical well-known methods like generalized additive models, new methods from statistics and machine learning are evaluated for the credit scoring case. Conditional inference trees, model-based recursive partitioning methods and random forests are presented as recursive partitioning algorithms. Boosting algorithms are also explored by additionally using the AUC as a loss function.
The empirical evaluation is based on data from a German bank. From the application scoring, 26 attributes are included in the analysis. Besides the AUC, different performance measures are used for evaluating the predictive performance of scoring models. While classification trees cannot improve predictive accuracy for the current credit scoring case, the AUC approach and special boosting methods provide outperforming results compared to the robust classical scoring models regarding the predictive performance with the AUC measure.<br>Scoringmodelle dienen Finanzinstituten als Grundlage dafür, die Ausfallwahrscheinlichkeit von Kreditantragstellern zu berechnen und zu entscheiden ob ein Kredit gewährt wird oder nicht. Das AUC (area under the receiver operating characteristic curve) ist eines der am häufigsten verwendeten Maße, um die Vorhersagekraft im Kreditscoring zu bewerten. Demzufolge besteht das Ziel dieser Arbeit darin, verschiedene Methoden zur Scoremodell-Bildung hinsichtlich eines optimierten AUC Maßes zu „benchmarken“. Während das genannte Maß dazu dient die vorgestellten Algorithmen hinsichtlich ihrer Trennschärfe zu bewerten, wird das AUC insbesondere als direktes Optimierungskriterium eingeführt.
Die logistische Regression ist das am häufigsten verwendete Verfahren zur Entwicklung von Scorekarten und die Einteilung der Antragsteller in Risikoklassen. Da der Entwicklungsprozess mittels logistischer Regression im Retail-Bankenbereich stark etabliert ist, wird die Trennschärfe dieses Verfahrens in der vorliegenden Arbeit als Benchmark verwendet.
Der AUC Ansatz wird als entscheidender Teil dieser Arbeit vorgestellt. Anstatt die Maximum Likelihood Schätzung zu verwenden, wird das AUC als direkte Zielfunktion zur Optimierung verwendet. Die Koeffizienten werden geschätzt, indem für die Berechnung des AUC die Wilcoxon Statistik und für die Optimierung der Nelder-Mead Algorithmus verwendet wird. Die AUC Optimierung stellt einen verteilungsfreien Ansatz dar, der im Rahmen einer Simulationsstudie untersucht wird, um die theoretischen Überlegungen zu analysieren. Es kann gezeigt werden, dass der Ansatz auch dann funktioniert, wenn in den Daten kein logistischer Zusammenhang vorliegt.
Zusätzlich zum AUC Ansatz und bekannten Methoden wie Generalisierten Additiven Modellen, werden neue Methoden aus der Statistik und dem Machine Learning für das Kreditscoring evaluiert. Klassifikationsbäume, Modell-basierte Recursive Partitioning Methoden und Random Forests werden als Recursive Paritioning Methoden vorgestellt. Darüberhinaus werden Boosting Algorithmen untersucht, die auch das AUC Maß als Verlustfunktion verwenden.
Die empirische Analyse basiert auf Daten einer deutschen Kreditbank. 26 Variablen werden im Rahmen der Analyse untersucht. Neben dem AUC Maß werden verschiedene Performancemaße verwendet, um die Trennschärfe von Scoringmodellen zu bewerten. Während Klassifikationsbäume im vorliegenden Kreditscoring Fall keine Verbesserungen erzielen, weisen der AUC Ansatz und einige Boosting Verfahren gute Ergebnisse im Vergleich zum robusten klassischen Scoringmodell hinsichtlich des AUC Maßes auf.
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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 score is a predictor, there is danger in relying solely on its information. Thus other variables are needed in order to more accurately be able to find the probability of default. Essay 2, “Christian Mutual Fund Performance,” draws attention to a much ignored type of funds, Christian mutual funds. The following questions are asked: How does Christian mutual fund perform compared to the market? Is there a difference in performance during recessions as indicated by literature? Is Christian mutual fund performance different than SRI funds? How do Catholic and Protestant fund perform? Looking at qualitative evidence, Christian mutual funds place much more importance on moral issue than SRI funds. Thus there is a clear difference in objectives and the type of screening that these two mutual fund pursue. Overall data reflects that screened data perform worse than the market, however during recession screened funds perform as well and at times better than the market. Christian mutual funds tends to perform worse than SRI funds.
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Medina, Fabio Augusto Scalet. "Regressão Logística Geograficamente Ponderada aplicada a modelos de Credit Scoring." reponame:Repositório Institucional da UnB, 2016. http://repositorio.unb.br/handle/10482/20790.
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Dissertação (mestrado)—Universidade de Brasília, Faculdade de Economia, Administração e Contabilidade, Programa de Pós-Graduação em Administração, 2016.<br>Submitted by Fernanda Percia França (fernandafranca@bce.unb.br) on 2016-06-23T16:39:44Z
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2016_FabioAugustoScaletMedina.pdf: 980307 bytes, checksum: c57d891197aa6227064966be4883bc3e (MD5)<br>A presente dissertação de mestrado teve como objetivo principal verificar a aplicabilidade da metodologia Regressão Logística Geograficamente Ponderada (GWLR) para a construção de modelos de credit scoring. As fórmulas do melhor conjunto de modelos locais estimados via GWLR foram comparadas entre si, em termos de valor dos coeficientes e significância das variáveis, e frente ao modelo global estimado via Regressão Logística. Foram utilizados dados reais referentes às operações de Crédito Direto ao Consumidor (CDC) de uma instituição financeira pública nacional concedidas a clientes domiciliados no Distrito Federal (DF). Os resultados encontrados demonstraram a viabilidade da utilização da técnica GWLR para desenvolver modelos de credit scoring. Os modelos estimados para cada região do DF se mostraram distintos em suas variáveis e coeficientes (parâmetros) e três dos cinco indicadores do modelo via GWLR se mostraram superiores aos do modelo via Regressão Logística. ________________________________________________________________________________________________ ABSTRACT<br>This master thesis aimed to verify the applicability of the methodology Geographically Weighted Logistic Regression (GWLR) to develop credit scoring models. The formulas of the best set of local models estimated by GWLR were compared in terms of value of the coefficients and significance of the variables, and against the global model estimated by Logistic Regression. It was used a real granting data of Direct Credit Consumer from a national public financial institution to borrowers domiciled in the Federal District (FD) of Brazil. The results demonstrated the feasibility of using the technique GWLR to develop credit scoring models. The estimated models for each region of FD have showed to be different in their variables and coefficients (parameters) and three out of five indicators calculated for the developed model by GWLR were superiors than indicators of the developed model by Logistic Regression.
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Sanchez, Barrios Luis Javier. "Alternative profit scorecards for revolving credit." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8043.
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The aim of this PhD project is to design profit scorecards for a revolving credit using alternative measures of profit that have not been considered in previous research. The data set consists of customers from a lending institution that grants credit to those that are usually financially excluded due to the lack of previous credit records. The study presents for the first time a relative profit measure (i.e.: returns) for scoring purposes and compares results with those obtained from usual monetary profit scores both in cumulative and average terms. Such relative measure can be interpreted as the productivity per customer in generating cash flows per monetary unit invested in receivables. Alternatively, it is the coverage against default if the lender discontinues operations at time t. At an exploratory level, results show that granting credit to financially excluded customers is a profitable business. Moreover, defaulters are not necessarily unprofitable; in average the profits generated by profitable defaulters exceed the losses generated by certain non-defaulters. Therefore, it makes sense to design profit (return) scorecards. It is shown through different methods that it makes a difference to use alternative profit measures for scoring purposes. At a customer level, using either profits or returns alters the chances of being accepted for credit. At a portfolio level, in the long term, productivity (coverage against default) is traded off if profits are used instead of returns. Additionally, using cumulative or average measures implies a trade off between the scope of the credit programme and customer productivity (coverage against default). The study also contributes to the ongoing debate of using direct and indirect prediction methods to produce not only profit but also return scorecards. Direct scores were obtained from borrower attributes, whilst indirect scores were predicted using the estimated probabilities of default and repurchase; OLS was used in both cases. Direct models outperformed indirect models. Results show that it is possible to identify customers that are profitable both in monetary and relative terms. The best performing indirect model used the probabilities of default at t=12 months and of repurchase in t=12, 30 months as predictors. This agrees with banking practices and confirms the significance of the long term perspective for revolving credit. Return scores would be preferred under more conservative standpoints towards default because of unstable conditions and if the aim is to penetrate relatively unknown segments. Further ethical considerations justify their use in an inclusive lending context. Qualitative data was used to contextualise results from quantitative models, where appropriate. This is particularly important in the microlending industry, where analysts’ market knowledge is important to complement results from scorecards for credit granting purposes. Finally, this is the first study that formally defines time-to-profit and uses it for scoring purposes. Such event occurs when the cumulative return exceeds one. It is the point in time when customers are exceedingly productive or alternatively when they are completely covered against default, regardless of future payments. A generic time-to-profit application scorecard was obtained by applying the discrete version of Cox model to borrowers’ attributes. Compared with OLS results, portfolio coverage against default was improved. A set of segmented models predicted time-to-profit for different loan durations. Results show that loan duration has a major effect on time-to-profit. Furthermore, inclusive lending programmes can generate internal funds to foster their growth. This provides useful insight for investment planning objectives in inclusive lending programmes such as the one under analysis.
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Fabík, Peter. "Credit risk management v leasingové společnosti." Master's thesis, Vysoká škola ekonomická v Praze, 2007. http://www.nusl.cz/ntk/nusl-1580.
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Práce pojednává o řízení rizik v leasingové společnosti. Popisuje proces hodnocení bonity klienta a faktory ovlivňující schvalování obchodních případů. Charakterizuje ratingový a scoringový model v konkrétní leasingové společnosti, hodnotí jejich nedostatky a navrhuje změny na jejich vylepšení. Obsahuje i praktický příklad komplexního hodnocení obchodního případu včetně posouzení bonity klienta prostřednictvím ratingového modelu a nástrojů finanční analýzy.
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Schmidt, Wagner. "Passivo contingente em instituição financeira: proposta de análise de risco utilizando os modelos Credit Scoring e Behaviour Scoring." Pontifícia Universidade Católica de São Paulo, 2010. https://tede2.pucsp.br/handle/handle/1437.
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Previous issue date: 2010-10-28<br>This study is the result of the present observation of the movement of civil lawsuits that are growing every day on the market of financial institutions. Nowadays, especially in financial institutions, significant civil lawsuits has been a concern of executives. The main objective of this study is to propose a model of risk management for contingent liabilities in financial institutions, since the difficulty of managing such numbers in the deal result. This is an adaptation of the instruments used in the management of credit risk for the legal area. The models used are the Behaviour Scoring and Credit Scoring. The first model is based on the curve behavioral processes, in this work are denominated like variables. These variables are known industry products offered by financial institutions. On a second level is taken into account the reasons, known as triggering events that led to the civil suits. The second model, Credit Scoring, based on a statistical study of values, which serve as the basis in determining the historical losses. The proposed study is to assist the risk management of these liabilities, eliminating the subjectivity of analysis and allowing greater speed in information. The present results prove that it is possible to use the instruments in question to the risk management of contingent liabilities, reducing the subjectivity of analysis, as greater adherence to criteria and faster responses for managers. The top ten products analyzed shows the results of Credit Scores, for the respective taxable events, termed here as Behaviour Scores. This work, in addition to demonstrating the applicability of the models Credit Scoring and Behavior Scoring also allows us to expand this study to other fields of activities, such as telecommunications, energy, companies that handle large volumes of civil lawsuits, as well as expanded discussion of risk allocation of contingent liability for the product<br>Este estudo é o resultado da observação atual do movimento de ações cíveis que vem crescendo a cada dia no mercado de instituições financeiras. Nos dias atuais, principalmente nas instituições financeiras, volumes significativos de ações judiciais cíveis tem sido motivo de preocupação dos executivos. O principal objetivo deste estudo é propor um modelo de gestão de risco para passivos contingentes nas instituições financeiras, visto a dificuldade de gestão desses números dentro do resultado do negócio. Trata-se de uma adaptação dos instrumentos utilizados na área de gestão de risco de crédito para a área jurídica. Os modelos utilizados em questão são o Behaviour Scoring e o Credit Scoring. O primeiro modelo baseia-se na curva comportamental dos processos, que neste trabalho denominam-se como variáveis. Estas variáveis são os conhecidos produtos ofertados pela indústria das instituições financeiras. Em um segundo nível é levado em consideração os motivos, ou seja, fatos geradores que geraram as ações cíveis. O segundo modelo, o Credit Scoring, baseia-se em um estudo estatístico de valores, os quais servirão de base na apuração das perdas históricas. A proposta do estudo é auxiliar a gestão do risco desses passivos, eliminando a subjetividade de análise e permitindo maior velocidade nas informações. Os resultados obtidos neste trabalho provam que é possível utilizar os instrumentos em questão para a gestão do risco do passivo contingente, diminuindo a subjetividade de análise, visto maior aderência nos critérios e respostas mais rápidas para os gestores. O top ten de produtos analisados mostra os resultados dos Credit Scores, para os respectivos fatos geradores, denominado neste trabalho como Behaviour Scores. Este trabalho, além de evidenciar a aplicabilidade dos modelos Credit Scoring e Behaviour Scoring, também permite expandir este estudo para outros ramos de atividades, como telefonia, energia, empresas que operam com grandes volumes de ações cíveis, além de expandir discussões como alocação de risco de passivo contingente por produto
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Viana, Renato Frazzato. "Técnicas de classificação aplicadas a credit scoring: revisão sistemática e comparação." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/104/104131/tde-18012017-112044/.
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Com a crescente demanda por crédito é muito importante avaliar o risco de cada operação desse tipo. Portanto, ao fornecer crédito a um cliente é necessário avaliar as chances do cliente não pagar o empréstimo e, para esta tarefa, as técnicas de credit scoring são aplicadas. O presente trabalho apresenta uma revisão da literatura de credit scoring com o objetivo de fornecer uma vis~ao geral das várias técnicas empregadas. Além disso, um estudo de simulação computacional é realizado com o intuito de comparar o comportamento de várias técnicas apresentadas no estudo.<br>Nowadays the increasing amount of bank transactions and the increasing of data storage created a demand for risk evaluation associated with personal loans. It is very important for a company has a very good tools in credit risk evaluation because theses tools can avoid money losses. In this context, it is interesting estimate the default probability for a customers and, the credit scoring techniques are very useful for this task. This work presents a credit scoring literature review with and aim to give a overview covering many techniques employed in credit scoring and, a computational study is accomplished in order to compare some of the techniques seen in this text.
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De, la Rey Tanja. "Two statistical problems related to credit scoring / Tanja de la Rey." Thesis, North-West University, 2007. http://hdl.handle.net/10394/3689.
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This thesis focuses on two statistical problems related to credit scoring. In credit scoring of individuals, two classes are distinguished, namely low and high risk individuals (the so-called "good" and "bad" risk classes). Firstly, we suggest a measure which may be used to study the nature of a classifier for distinguishing between the two risk classes. Secondly, we derive a new method DOUW (detecting outliers using weights) which may be used to fit logistic regression models robustly and for the detection of outliers.
In the first problem, the focus is on a measure which may be used to study the nature of a classifier. This measure transforms a random variable so that it has the same distribution as another random variable. Assuming a linear form of this measure, three methods for estimating the parameters (slope and intercept) and for constructing confidence bands are developed and compared by means of a Monte Carlo study. The application of these estimators is illustrated on a number of datasets. We also construct statistical hypothesis to test this linearity assumption. In the second problem, the focus is on providing a robust logistic regression fit and
the identification of outliers. It is well-known that maximum likelihood estimators of
logistic regression parameters are adversely affected by outliers. We propose a robust approach that also serves as an outlier detection procedure and is called DOUW. The approach is based on associating high and low weights with the observations as a result of the likelihood maximization. It turns out that the outliers are those observations to which low weights are assigned. This procedure depends on two tuning constants. A simulation study is presented to show the effects of these constants on the performance of the proposed methodology. The results are presented in terms of four benchmark datasets as well as a large new dataset from the application area of retail marketing campaign analysis.
In the last chapter we apply the techniques developed in this thesis on a practical credit scoring dataset. We show that the DOUW method improves the classifier performance and that the measure developed to study the nature of a classifier is useful in a credit scoring context and may be used for assessing whether the distribution of the good and the bad risk individuals is from the same translation-scale family.<br>Thesis (Ph.D. (Risk Analysis))--North-West University, Potchefstroom Campus, 2008.
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Seow, Hsin-Vonn. "Using adaptive learning in credit scoring to estimate acceptance probability distribution." Thesis, University of Southampton, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430721.
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Delamaire, Linda. "Implementing a credit risk management system based on innovative scoring techniques." Thesis, University of Birmingham, 2012. http://etheses.bham.ac.uk//id/eprint/3344/.
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In recent years, most developed countries have suffered a severe recession due to a financial crisis starting in the US with mortgages loans. The lack of credit risk management has been pointed out as one of the causes of this bank panics. To avoid a similar situation, the credit card companies need to have proper risk management tools. This thesis presents a credit scoring system which aims at setting credit lines and thus, controlling credit risk. It includes three types of models: application scorecards, early detection scorecards and behavioral scorecards. They have been built on real and recent data coming from a German credit card company. The models have been built with a training sample and validated accordingly, using logistic regression. Information value and validation charts have been used for comparing the models. In the scoring process described, the scorecards are used in a sequential order. The author shows that minimizing losses might not be optimal in order to maximize profit. Finally, the author presents possible extensions to the research. The author hopes that the microeconomic analysis of the mechanics of a particular lender’s credit allocation process described in this thesis can play some part in preventing future financial crisis.
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Falangis, Konstantinos. "Mathematical programming models for classification problems with applications to credit scoring." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8927.
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Mathematical programming (MP) can be used for developing classification models for the two–group classification problem. An MP model can be used to generate a discriminant function that separates the observations in a training sample of known group membership into the specified groups optimally in terms of a group separation criterion. The simplest models for MP discriminant analysis are linear programming models in which the group separation measure is generally based on the deviations of misclassified observations from the discriminant function. MP discriminant analysis models have been tested extensively over the last 30 years in developing classifiers for the two–group classification problem. However, in the comparative studies that have included MP models for classifier development, the MP discriminant analysis models either lack appropriate normalisation constraints or they do not use the proper data transformation. In addition, these studies have generally been based on relatively small datasets. This thesis investigates the development of MP discriminant analysis models that incorporate appropriate normalisation constraints and data transformations. These MP models are tested on binary classification problems, with an emphasis on credit scoring problems, particularly application scoring, i.e. a two–group classification problem concerned with distinguishing between good and bad applicants for credit based on information from application forms and other relevant data. The performance of these MP models is compared with the performance of statistical techniques and machine learning methods and it is shown that MP discriminant analysis models can be useful tools for developing classifiers. Another topic covered in this thesis is feature selection. In order to make classification models easier to understand, it is desirable to develop parsimonious classification models with a limited number of features. Features should ideally be selected based on their impact on classification accuracy. Although MP discriminant analysis models can be extended for feature selection based on classification accuracy, there are computational difficulties in applying these models to large datasets. A new MP heuristic for selecting features is suggested based on a feature selection MP discriminant analysis model in which maximisation of classification accuracy is the objective. The results of the heuristic are promising in comparison with other feature selection methods. Classifiers should ideally be developed from datasets with approximately the same number of observations in each class, but in practice classifiers must often be developed from imbalanced datasets. New MP formulations are proposed to overcome the difficulties associated with generating discriminant functions from imbalanced datasets. These formulations are tested using datasets from financial institutions and the performance of the MP-generated classifiers is compared with classifiers generated by other methods. Finally, the ordinal classification problem is considered. MP methods for the ordinal classification problem are outlined and a new MP formulation is tested on a small dataset.
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Silva, Liliane Travassos da. "Modelos baseados em pseudo-valores e sua aplicabilidade em credit scoring." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-28082010-221333/.
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Os modelos de credit scoring têm sido bastante difundidos nos últimos anos como uma importante ferramenta para agilizar e tornar mais confiável o processo de concessão de crédito por parte das instituições financeiras. Esses modelos são utilizados para classificar os clientes em relação a seus riscos de inadimplência. Neste trabalho, é avaliada a aplicabilidade de uma nova metodologia, baseada em pseudo-valores, como alternativa para a construção de modelos de credit scoring. O objetivo é compará-la com abordagens tradicionais como a regressão logística e o modelo de riscos proporcionais de Cox. A aplicação prática é feita para dados de operações de crédito pessoal sem consignação, coletados do Sistema de Informações de Crédito do Banco Central do Brasil. As performances dos modelos são comparadas utilizando a estatística de Kolmogorov-Smirnov e a área sob a curva ROC.<br>Credit Scoring models have become popular in recent years as an important tool in the credit granting process, making it more expedite and reliable. The models are mainly considered to classify customers according to their default risk. In this work we evaluate the apllicability of a new methodology, based on pseudo-values, as an alternative to constructing credit scoring models. The objective is to compare this novel methodology with traditional approaches such as logistic regression and Cox proportional hazards model. The models are applied to a dataset on personal credit data, collected from the Credit Information System of Central Bank of Brazil. The performances of the models are compared via Kolmogorov-Smirnov statistic and the area under ROC curve.
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Santos, Maria Ana e. Castello-Branco dos. "Backtesting of a credit scoring system under the current regulatory framework." Master's thesis, Instituto Superior de Economia e Gestão, 2017. http://hdl.handle.net/10400.5/14152.
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Mestrado em Ciências Actuariais<br>Desde a implementação do atual acordo de supervisão financeira internacional, os bancos podem usar as suas estimativas internas de avaliação de risco de crédito como base para o cálculo dos ponderadores de risco e requisitos de capital. Consequentemente, com vista a assegurar a estabilidade e solvabilidade das instituições de crédito, torna-se crescente a necessidade de um sistema de validação robusto, para garantir a consistência e precisão dos sistemas
de notação interna.
Existem vários estudos sobre o processo de validação de estimativas internas. No entanto, aprofundamento e acordo nesta matéria são ainda insuficientes, nomeadamente no que diz respeito à avaliação da precisão das estimativas internas para os parâmetros de risco de crédito, com o objectivo de atingir a estabilidade dos requisitos de capital. A calibração das probabilidades de incumprimento representa um dos procedimentos de validação quantitativa inerentes ao exercício
de backtesting.
Neste trabalho, será explorado o processo de calibração das probabilidades de incumprimento recorrendo a um modelo de scoring para exemplificar como é feita a avaliação da capacidade preditiva destas estimativas internas numa carteira de Crédito à Habitação. Para superar o desafio de desenvolver um sistema de validação adequado, o presente projeto tem em consideração o atual e amplo quadro regulatório proveniente do Comité de Basileia para a Supervisão Bancária (BCBS) e da Autoridade Bancária Europeia (EBA), alguns artigos relevantes nesta matéria e aquelas que são consideradas as melhores práticas de gestão do risco de crédito.<br>Since the implementation of the current regulatory framework within the global financial system, banks are allowed to rely in a system using their own estimates for credit risk parameters as inputs for the calculation of risk weights and capital requirements. Consequently, in order to assure the stability and soundness of credit institutions, the need for a robust validation system to ensure accuracy and consistency of internal rating systems is greater than ever before.
Although several studies on validation processes already exist, a deeper understanding and agreement on this subject is required, namely in what concerns the accuracy assessment of internal estimates for credit risk parameters, in order to achieve capital requirements stability. Calibration of default probabilities represents one of the quantitative validatio procedures underlying the exercise of backtesting that must be performed on a regular basis.
The present text discusses the probability of default (PD) calibration process using a scoring model to illustrate the assessment of the predictive power of these internal estimates in a residential mortgage portfolio. To overcome the challenge of developing an adequate validation scheme in compliance with the current regulatory framework, this project project keeps in mind the legislation from Basel Committee on Banking Supervision (BCBS) and European Banking Authority (EBA), some relevant studies developed on this subject and those that are consider to be the best practices of credit risk management.<br>info:eu-repo/semantics/publishedVersion