No autorizada por el autor para ser publicada a texto completo / El presente trabajo de título tuvo como objetivo desarrollar una herramienta de scoring crediticio dirigida a empresas Latinoamericanas emisoras de títulos con clasificación menor o igual a BB. Actualmente en la plaza local, el desconocimiento que existe en este tipo de instrumentos de renta fija se supedita, en su mayoría, a la compra de fondos elaborados por empresas externas y no al estudio y desarrollo de tecnologías in house, externalizando, de esta forma, el análisis crediticio. Por lo tanto, el interés de desarrollar herramientas que apoyen la toma de decisiones es imperante para instituciones como Asesorías e Inversiones Cruz del Sur que busca, evidentemente, obtener retornos por sobre la competencia. La metodología para el scoring consistió en un estudio de los reportes y recomendaciones de los principales bancos de inversión y compañías de servicios financieros, tanto nacional como internacional, que brindan fondos e investigación de empresas Latinoamericanas y mercados emergentes, de tal manera de crear un universo de las principales métricas que son utilizadas en sus análisis actualmente. De tal universo se derivaron, a través de un estudio de incidencias y juicio de expertos, 5 ratios que otorgaban un diagnóstico de la estructura de deuda y capacidad de cumplir con obligaciones en el corto plazo. Posteriormente, se le asignó a cada métrica un puntaje ajustado al percentil diez de la distribución que presentaba y luego, a través de una descomposición del rendimiento del instrumento, se realizaron ejercicios regresivos (lineal y de panel) que estimaron la importancia de cada métrica en la calibración final. La herramienta fue realizada en lenguaje VBA y su interfaz en Excel, otorgando, además del score crediticio, funcionalidades complementarias que incluyeran información de mercado de los títulos, gráficos y fácil manejo de una base de datos interna con objeto de disminuir tiempos asignados al proceso de manejo de información. El resultado, considerando todas las funcionalidades que abarca, fue una herramienta capaz de otorgar una opinión sobre las circunstancias de un emisor para cubrir sus compromisos financieros sujeta a la limitada posibilidad de automatización de las variables y presentar un punto de partida para el departamento de estudios.

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. / 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.

Despite being governed by several laws aimed at preventing racial inequality in access to housing and credit resources, the mortgage lending market remains a contributor to racial and place-based disparities in homeownership rates, wealth, and access to high-quality community resources. Scholarship has identified persistent disparities in mortgage loan approval rates and subprime lending between white borrowers and those from other racial and ethnic groups, and between white neighborhoods and neighborhoods with high levels of non-white residents. Against this backdrop, the mortgage lending industry is undergoing rapid, technology-driven changes in risk assessment and application processing. Traditional borrower risk-assessment methods including face-to-face discussions between lenders and applicants and the prominent use of FICO credit scores have been replaced or supplemented by automated decision-making tools at a new generation of institutions known as fintech lenders. Little is known about the relationship between lenders using these new tools and the racial and spatial disparities that have long defined the wider mortgage market. Given the well-documented history of discrimination in lending along with findings of technology-driven racial inequality in other economic sectors, fintech lending’s potential for racial discrimination warrants increased scrutiny. This dissertation compares the lending outcomes of traditional and fintech mortgage lenders in the United States depending on applicant and neighborhood racial characteristics. Using data from the Home Mortgage Disclosure Act, an original dataset classifying lenders as fintech or traditional, and an array of complimentary administrative data sources, it provides an assessment of the salience of race and place in the rates at which mortgage loans from each lender type are approved and assigned subprime terms. Results from a series of regression-based quantitative analyses suggest fintech mortgage lenders, like traditional mortgage lenders, approve and deny loans and distribute subprime credit at disparate rates to white borrowers and neighborhoods relative to nonwhite borrowers and neighborhoods. Findings suggest that policymakers and regulators must increase their oversight of fintech lenders, ensuring that further advances in lending technology do not concretize longstanding racial and spatial disparities.

This dissertation is concerned with three problems within the field of optimization for agent--based systems. Firstly, the inverse optimal control problem is investigated for the single-agent system. Given a dynamic process, the goal is to recover the quadratic cost function from the observation of optimal control sequences. Such estimation could then help us develop a better understanding of the physical system and reproduce a similar optimal controller in other applications. Next, problems of optimization over networked systems are considered. A novel differential game approach is proposed for the optimal intrinsic formation control of multi-agent systems. As for the credit scoring problem, an optimal filtering framework is utilized to recursively improve the scoring accuracy based on dynamic network information. In paper A, the problem of finite horizon inverse optimal control problem is investigated, where the linear quadratic (LQ) cost function is required to be estimated from the optimal feedback controller. Although the infinite-horizon inverse LQ problem is well-studied with numerous results, the finite-horizon case is still an open problem. To the best of our knowledge, we propose the first complete result of the necessary and sufficient condition for the existence of corresponding LQ cost functions. Under feasible cases, the analytic expression of the whole solution space is derived and the equivalence of weighting matrices is discussed. For infeasible problems, an infinite dimensional convex problem is formulated to obtain a best-fit approximate solution with minimal control residual, where the optimality condition is solved under a static quadratic programming framework to facilitate the computation. In paper B, the optimal formation control problem of a multi-agent system is studied. The foraging behavior of N agents is modeled as a finite-horizon non-cooperative differential game under local information, and its Nash equilibrium is studied. The collaborative swarming behaviour derived from non-cooperative individual actions also sheds new light on understanding such phenomenon in the nature. The proposed framework has a tutorial meaning since a systematic approach for formation control is proposed, where the desired formation can be obtained by only intrinsically adjusting individual costs and network topology. In contrast to most of the existing methodologies based on regulating formation errors to the pre-defined pattern, the proposed method does not need to involve any information of the desired pattern beforehand. We refer to this type of formation control as intrinsic formation control. Patterns of regular polygons, antipodal formations and Platonic solids can be achieved as Nash equilibria of the game while inter-agent collisions are naturally avoided. Paper C considers the credit scoring problem by incorporating dynamic network information, where the advantages of such incorporation are investigated in two scenarios. Firstly, when the scoring publishment is merely individual--dependent, an optimal Bayesian filter is designed for risk prediction, where network observations are utilized to provide a reference for the bank on future financial decisions. Furthermore, a recursive Bayes estimator is proposed to improve the accuracy of score publishment by incorporating the dynamic network topology as well. It is shown that under the proposed evolution framework, the designed estimator has a higher precision than all the efficient estimators, and the mean square errors are strictly smaller than the Cramér-Rao lower bound for clients within a certain range of scores. / I denna avhandling behandlas tre problem inom optimering för agentbaserade system. Inledningsvis undersöks problemet rörande invers optimal styrning för ett system med en agent. Målet är att, givet en dynamisk process, återskapa den kvadratiska kostnadsfunktionen från observationer av sekvenser av optimal styrning. En sådan uppskattning kan ge ökad förståelse av det underliggande fysikaliska systemet, samt vara behjälplig vid konstruktion av en liknande optimal regulator för andra tillämpningar. Vidare betraktas problem rörande optimering över nätverkssystem. Ett nytt angreppssätt, baserat på differentialspel, föreslås för optimal intrinsisk formationsstyrning av system med fler agenter. För kreditutvärderingsproblemet utnyttjas ett filtreringsramverk för att rekursivt förbättra kreditvärderingens noggrannhet baserat på dynamisk nätverksinformation. I artikel A undersöks problemet med invers optimal styrning med ändlig tidshorisont, där den linjärkvadratiska (LQ) kostnadsfunktionen måste uppskattas från den optimala återkopplingsregulatorn. Trots att det inversa LQ-problemet med oändlig tidshorisont är välstuderat och med flertalet resultat, är fallet med ändlig tidshorisont fortfarande ett öppet problem. Så vitt vi vet presenterar vi det första kompletta resultatet med både tillräckliga och nödvändiga villkor för existens av en motsvarande LQ-kostnadsfunktion. I fallet med lösbara problem härleds ett analytiskt uttryck för hela lösningsrummet och frågan om ekvivalens med viktmatriser behandlas. För de olösbara problemen formuleras ett oändligtdimensionellt konvext optimeringsproblem för att hitta den bästa approximativa lösningen med den minsta styrresidualen. För att underlätta beräkningarna löses optimalitetsvillkoren i ett ramverk för statisk kvadratisk programmering. I artikel B studeras problemet rörande optimal formationsstyrning av ett multiagentsystem. Agenternas svärmbeteende modelleras som ett icke-kooperativt differentialspel med ändlig tidshorisont och enbart lokal information. Vi studerar detta spels Nashjämvikt. Att, ur icke-kooperativa individuella handlingar, härleda ett kollaborativt svärmbeteende kastar nytt ljus på vår förståelse av sådana, i naturen förekommande, fenomen. Det föreslagna ramverket är vägledande i den meningen att det är ett systematiskt tillvägagångssätt för formationsstyrning, där den önskade formeringen kan erhållas genom att endast inbördes justera individuella kostnader samt nätverkstopologin. I motstat till de flesta befintliga metoder, vilka baseras på att reglera felet i formeringen relativt det fördefinierade mönstret, så behöver den föreslagna metoden inte på förhand ta hänsyn till det önskade mönstret. Vi kallar denna typ av formationsstyrning för intrinsisk formationsstyrning. Mönster så som regelbundna polygoner, antipodala formeringar och Platonska kroppar kan uppnås som Nashjämvikter i spelet, samtidigt som kollisioner mellan agenter undviks på ett naturligt sätt. Artikel C behandlar kreditutvärderingsproblemet genom att lägga till dynamisk nätverksinformation. Fördelarna med en sådan integrering undersöks i två scenarier. Då kreditvärdigheten enbart är individberoende utformas ett optimalt Bayesiskt filter för riskvärdering, där observationer från nätverket används för att tillhandahålla en referens för banken på framtida finansiella beslut. Vidare föreslås en rekursiv Bayesisk estimator (stickprovsvariabel) för att förbättra noggrannheten på den skattade kreditvärdigheten genom att integrera även den dynamiska nätverkstopologin. Inom den föreslagna ramverket för tidsutveckling kan vi visa att, för kunder inom ett visst intervall av värderingar, har den utformade estimatorn högre precision än alla effektiva estimatorer och medelkvadrafelet är strikt mindre än den nedre gränsen från Cramér-Raos olikhet. / <p>QC 20190603</p>

Made available in DSpace on 2016-06-02T20:06:06Z (GMT). No. of bitstreams: 1 4300.pdf: 2695135 bytes, checksum: c7742258a75f77aa35ccb54abc3439fe (MD5) Previous issue date: 2012-03-16 / Financiadora de Estudos e Projetos / In credit scoring problems, the interest is to associate to an element who request some kind of credit, a probability of default. However, traditional models uses samples biased because the data obtained from the tenderers has only clients who won a approval of a request for previous credit. In order to reduce the bias sample of these models, we use strategies to extract information about individuals rejected to be able to infer a response, good or bad payer. This is what we call the reject inference. With the use of these strategies, we also use the bagging technique (bootstrap aggregating), which consist in generate models based in some bootstrap samples of the training data in order to get a new predictor, when these models is combined. In this work we will discuss about some of the combination methods in the literature, especially the method of combination by logistic regression, although little used but with interesting results.We'll also discuss some strategies relating to reject inference. Analyses are given through a simulation study, in data sets generated and real data sets of public domain. / Em problemas de credit scoring, o interesse é associar a um elemento solicitante de algum tipo de crédito, uma probabilidade de inadimplência. No entanto, os modelos tradicionais utilizam amostras viesadas, pois constam apenas de dados obtidos dos proponentes que conseguiram a aprovação de uma solicitação de crédito anterior. Com o intuito de reduzir o vício amostral desses modelos, utilizamos estratégias para extrair informações acerca dos indivíduos rejeitados para que nele seja inferida uma resposta do tipo bom/- mau pagador. Isto é o que chamamos de inferência dos rejeitados. Juntamente com o uso dessas estratégias utilizamos a técnica bagging (bootstrap aggregating ), que é baseada na construção de diversos modelos a partir de réplicas bootstrap dos dados de treinamento, de modo que, quando combinados, gera um novo preditor. Nesse trabalho discutiremos sobre alguns dos métodos de combinação presentes na literatura, em especial o método de combinação via regressão logística, que é ainda pouco utilizado, mas com resultados interessantes. Discutiremos também as principais estratégias referentes à inferência dos rejeitados. As análises se dão por meio de um estudo simulação, em conjuntos de dados gerados e em conjuntos de dados reais de domínio público.

Made available in DSpace on 2016-06-02T20:06:06Z (GMT). No. of bitstreams: 1 4338.pdf: 1335557 bytes, checksum: 8e0bef5711ff8c398be194e335deecec (MD5) Previous issue date: 2012-02-28 / Universidade Federal de Sao Carlos / Classification consists in the discovery of rules of prediction to assist with planning and decision-making, being a continuously indispensable tool and a highly discussed subject in literature. As a special case in classification, we have the process of credit risk rating, within which there is interest in identifying good and bad paying customers through binary classification methods. Therefore, in many application backgrounds, as in financial, several techniques can be utilized, such as discriminating analysis, probit analysis, logistic regression and neural nets. However, the Probabilistic Nets technique, also known as Bayesian Networks, have showed itself as a practical convenient classification method with successful applications in several areas. In this paper, we aim to display the appliance of Probabilistic Nets in the classification scenario, specifically, the technique named K-dependence Bayesian Networks also known as KDB nets, as well as compared its performance with conventional techniques applied within context of the Credit Scoring and Medical diagnosis. Applications of the technique based in real and artificial datasets and its performance assisted by the bagging procedure will be displayed as results. / A classificação consiste na descoberta de regras de previsão para auxílio no planejamento e tomada de decisões, sendo uma ferramenta indispensável e um tema bastante discutido na literatura. Como caso especial de classificação, temos o processo de avaliação de risco de crédito, no qual temos o interesse de identificar clientes bons e maus pagadores através de métodos de classificação binária. Assim, em diversos enredos de aplicação, como nas financeiras, diversas técnicas podem ser utilizadas, tais como análise discriminante, análise probito, regressão logística e redes neurais. Porém, a técnica de Redes Probabilísticas, também conhecida como Redes Bayesianas, tem se mostrado um método prático de classificação e com aplicações bem sucedidas em diversos campos. Neste trabalho, visamos exibir a aplicação das Redes Probabilísticas no contexto de classificação, em específico, a técnica denominada Redes Probabilísticas com K-dependência, também conhecidas como redes KDB, bem como comparar seu desempenho com as técnicas convencionais aplicadas no contexto de Credit Scoring e Diagnose Médica. Exibiremos como resultado aplicações da técnica baseadas em conjuntos de dados reais e artificiais e seu desempenho auxiliado pelo procedimento de bagging.

Les institutions financières sont, dans l’exercice de leurs fonctions, confrontées à divers risques, entre autres le risque de crédit, le risque de marché et le risque opérationnel. L’instabilité de ces facteurs fragilise ces institutions et les rend vulnérables aux risques financiers qu’elles doivent, pour leur survie, être à même d’identifier, analyser, quantifier et gérer convenablement. Parmi ces risques, celui lié au crédit est le plus redouté par les banques compte tenu de sa capacité à générer une crise systémique. La probabilité de passage d’un individu d’un état non risqué à un état risqué est ainsi au cœur de nombreuses questions économiques. Dans les institutions de crédit, cette problématique se traduit par la probabilité qu’un emprunteur passe d’un état de "bon risque" à un état de "mauvais risque". Pour cette quantification, les institutions de crédit recourent de plus en plus à des modèles de credit-scoring. Cette thèse porte sur les techniques actuelles de credit-scoring adaptées aux besoins d’une institution de crédit, le CFCAL-banque, spécialisé dans les prêts garantis par hypothèques. Nous présentons en particulier deux modèles non paramétriques (SVM et GAM) dont nous comparons les performances en termes de classification avec celles du modèle logit traditionnellement utilisé dans les banques. Nos résultats montrent que les SVM sont plus performants si l’on s’intéresse uniquement à la capacité de prévision globale. Ils exhibent toutefois des sensibilités inférieures à celles des modèles logit et GAM. En d’autres termes, ils prévoient moins bien les emprunteurs défaillants. Dans l’état actuel de nos recherches, nous préconisons les modèles GAM qui ont certes une capacité de prévision globale moindre que les SVM, mais qui donnent des sensibilités, des spécificités et des performances de prévision plus équilibrées. En mettant en lumière des modèles ciblés de scoring de crédit, en les appliquant sur des données réelles de crédits hypothécaires, et en les confrontant au travers de leurs performances de classification, cette thèse apporte une contribution empirique à la recherche relative aux modèles de credit-scoring. / Financial institutions face in their functions a variety of risks such as credit, market and operational risk. These risks are not only related to the nature of the activities they perform, but also depend on predictable external factors. The instability of these factors makes them vulnerable to financial risks that they must appropriately identify, analyze, quantify and manage. Among these risks, credit risk is the most prominent due to its ability to generate a systemic crisis. The probability for an individual to switch from a risked to a riskless state is thus a central point to many economic issues. In credit institution, this problem is reflected in the probability for a borrower to switch from a state of “good risk” to a state of “bad risk”. For this quantification, banks increasingly rely on credit-scoring models. This thesis focuses on the current credit-scoring techniques tailored to the needs of a credit institution: the CFCAL-banque specialized in mortgage credits. We particularly present two nonparametric models (SVM and GAM) and compare their performance in terms of classification to those of logit model traditionally used in banks. Our results show that SVM are more effective if we only focus on the global prediction performance of the models. However, SVM models give lower sensitivities than logit and GAM models. In other words the predictions of SVM models on defaulted borrowers are not satisfactory as those of logit or GAM models. In the present state of our research, even GAM models have lower global prediction capabilities, we recommend these models that give more balanced sensitivities, specificities and performance prediction. This thesis is not completely exhaustive about the scoring techniques for credit risk management. By trying to highlight targeted credit scoring models, adapt and apply them on real mortgage data, and compare their performance through classification, this thesis provides an empirical and methodological contribution to research on scoring models for credit risk management.

A presente tese objetivou investigar a contribuição de variáveis e escalas psicológicas sugeridas pela literatura de Psicologia Econômica, a fim de predizer o risco de crédito de pessoas físicas. Nesse sentido, através das técnicas de regressão logística, e seguindo todas as etapas para desenvolvimento de modelos de credit scoring, foram construídos modelos de application scoring para pessoas físicas com variáveis sociodemográficas e situacionais, comumente utilizadas nos modelos tradicionais, mais a inclusão de variáveis comportamentais e escalas psicológicas, tais como: variáveis de comparação social, variáveis relacionadas com educação financeira, variáveis de comportamento de consumo, proxies de autocontrole e horizonte temporal, escala do significado do dinheiro (ESD), escala de autoeficácia, escala de lócus de controle, escala de otimismo, escala de autoestima e escala de comprador compulsivo. Os resultados foram contundentes e direcionaram para uma significativa contribuição de algumas dessas variáveis em predizer o risco de crédito dos indivíduos. As variáveis oriundas da ESD mostraram que as dimensões negativas relacionadas com o dinheiro estão mais associadas a indivíduos com problemas com dívidas. Também foi possível constatar que indivíduos com altos escores na escala de autoeficácia, provavelmente indicando um maior grau de otimismo e excesso de confiança, estão mais associados ao grupo de mau pagador. Notou-se ainda que compradores classificados como compulsivos possui maior probabilidade de se encontrar no grupo de mau crédito. Indivíduos que consideram presentear crianças e amigos em datas comemorativas como uma necessidade, mesmo que muitas pessoas considerem um luxo, possuem maior chance de se encontrarem no grupo de mau crédito. Problemas de autocontrole identificados por indivíduos que bebem em média mais de quatro copos de bebida alcoólica no dia ou são fumantes, mostraram-se importantes para identificar tendências ao endividamento. A partir desses achados acredita-se que a presente tese avançou no entendimento do risco de crédito das pessoas físicas, de forma a suscitar variáveis que podem aumentar a precisão da previsão dos modelos de credit scoring, tendo como uma das implicações imediatas a consideração de algumas das variáveis significativas como uma pergunta no formulário cadastral para novos clientes, tais como: Você acha que presentear amigos em datas comemorativas é uma necessidade ou luxo? Você acha que presentear crianças em datas comemorativas é uma necessidade ou luxo? Na média, você bebe mais de 4 copos de bebida alcoólica no dia? Você fuma cigarros? As implicações dos resultados também podem ser discutidas no âmbito dos modelos de behavioral scoring e modelos de credit scoring para pessoas jurídicas. / This works aimed to investigate the contribution of variables and psychological scales, suggested by the literature of Economic Psychology, in order to predict the credit risk of individuals. Accordingly, through the techniques of logistic regression, and following all the steps for developing credit scoring models, application scoring models were built for individuals with socio demographic and situational variables, commonly used in traditional models, further the inclusion of behavioral variables and psychological scales, such as: variables of social comparison, variables related to financial education, variables in consumption behavior, proxies of self-control and temporal horizon, meaning of money scale (MMS), scale of self efficacy, locus of control scale, scale of optimism, scale of self-esteem and scale of compulsive buyer. The results were blunt, and directed a significant contribution to some of these variables in predicting the credit risk of individuals. The variables derived from the MMS showed that the negative dimensions related to money are more associated to individuals with debt problems. It was also noted that individuals with high scores on selfefficacy scale, probably indicating a higher degree of optimism and overconfidence, are the group most associated with bad credit. It was noted also that buyers classified as compulsive ones are more likely to find in the group of bad credit. Individuals who consider gifting children and friends on commemorative dates as a necessity, even though many people consider a luxury, have more chance in being found in the group of bad credit. Self-control problems, identified by individuals who drink more than four glasses of alcohol a day, or are smokers, were important to identify indebtedness trends. From these findings it is believed that this works has advanced the understanding of the credit risk of individuals, giving rise to variables that may increase the forecast accuracy of credit scoring models, having as one of the immediate implications, considering of some of the significant variables as one of the questions about the individual when he fills the new application form, such as: Do you think gifting friends in commemorative dates is a necessity or luxury? Do you think gifting children in commemorative dates is a necessity or luxury? On average, you drink more than four glasses of alcohol a day? Do you smoke cigarettes? The implications of these results can also be discussed in the context of behavioral scoring models and credit scoring models for corporations.

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.

This study follows a three–pronged approach to investigate the effects of the global financial crisis on the South African retail credit market (using Woolworths as subject). These three prongs, or areas, include a literature study, step–by–step credit scoring guide and an application of this guide in an empirical study. To achieve this goal, credit scoring was selected as the quantitative tool to illustrate these effects. Two different periods were chosen to supply a snapshot of the retail credit industry, namely the retail credit situation before and during the global financial crisis. To correctly define and understand the mechanics affecting South Africa's retail credit industry, a literature review was conducted to investigate the global financial crisis, the South African retail credit market and credit scoring itself. The literature investigation explains the global financial crisis and identifies some of the primary drivers behind it. These drivers included the US housing bubble, the introduction of subprime loans and the securitisation of these loans (mortgage backed securities). The study found that these drivers, especially the securitisation of subprime loans, were the vehicle used to enable the crisis to spread globally. The ultimate goal of the study was to provide the individual, and companies, with an understanding of the global financial crisis' effects on the consumer specifically through their credit worthiness and retail credit behaviour. Through the use of credit scoring, the study found that at least one retailer (Woolworths) in the retail industry was affected. Woolworths placed a stronger emphasis on reducing their credit exposure whilst consumers were steadily increasing their facility utilisation. / Thesis (M.Com. (Risk management))--North-West University, Potchefstroom Campus, 2012.