Artificial intelligence and machine learning has become an important part of society and today businesses compete in a new digital environment. However, scholars and regulators are concerned with these technologies' societal impact as their use does not come without risks, such as those stemming from transparency and accountability issues. The potential wrongdoing of these technologies has led to guidelines and future regulations on how they can be used in a trustworthy way. However, these guidelines are argued to lack practicality and they have sparked concern that they will hamper organisations' digital pursuit for innovation and competitiveness. This master’s thesis aims to contribute to this field by studying how teams can work with risk mitigation of risks associated with machine learning. The scope was set on capturing insights on the perception of employees, on what they consider to be important and challenging with machine learning risk mitigation, and then put it in relation to research to develop practical recommendations. The master’s thesis specifically focused on the financial technology sector and the use of machine learning in credit scoring. To achieve the aim, a qualitative single case study was conducted. The master’s thesis found that a combination of processes and capabilities are perceived as important in this work. Moreover, current barriers are also found in the single case. The findings indicate that strong responsiveness is important, and this is achieved in the single case by having separation of responsibilities and strong team autonomy. Moreover, standardisation is argued to be needed for higher control, but that it should be implemented in a way that allows for flexibility. Furthermore, monitoring and validation are important processes for mitigating machine learning risks. Additionally, the capability of extracting as much information from data as possible is an essential component in daily work, both in order to create value but also to mitigate risks. One barrier in this work is that the needed knowledge takes time to develop and that knowledge transferring is sometimes restricted by resource allocation. However, knowledge transfer is argued to be important for long term sustainability. Organisational culture and societal awareness are also indicated to play a role in machine learning risk mitigations. / Artificiell intelligens och maskininlärning har blivit en betydelsefull del av samhället och idag konkurrerar organisationer i en ny digital miljö. Forskare och regulatorer är däremot bekymrade gällande den samhällspåverkan som sådan teknik har eftersom användningen av dem inte kommer utan risker, såsom exempelvis risker som uppkommer från brister i transparens och ansvarighet. Det potentiella olämpliga användandet av dessa tekniker har resulterat i riktlinjer samt framtida föreskrifter på hur de kan användas på ett förtroendefullt och etiskt sätt. Däremot så anses dessa riktlinjer sakna praktisk tillämpning och de har väckt oro då de möjligen kan hindra organisationers digitala strävan efter innovation och konkurrenskraft. Denna masteruppsats syftar till att bidra till detta område genom att studera hur team kan arbeta med riskreducering av risker kopplade till maskininlärning. Uppsatsens omfång lades på att fånga insikter på medarbetares uppfattning, för att sedan ställa dessa i relation till forskning och utveckla praktiska rekommendationer. Denna masteruppsats fokuserade specifikt på finansteknologisektorn och användandet av maskininlärning inom kreditvärdering. En kvalitativ singelfallstudie genomfördes för att uppnå detta mål. Masteruppsatsen fann att en kombination av processer och förmågor uppfattas som viktiga inom detta arbete. Dessutom fann fallstudien några barriärer. Resultaten indikerar att en stark förmåga att reagera är essentiellt och att detta uppnås i fallstudien genom att ha tydlig ansvarsfördelning och att teamen har stark autonomi. Vidare så anses standardisering behövas för en högre nivå av kontroll, samtidigt som det bör vara implementerat på ett sådant sätt som möjliggör flexibilitet. Fortsättningsvis anses monitorering och validering vara viktiga processer för att mitigera maskininlärningsrisker. Dessutom är förmågan att extrahera så mycket information från data som möjligt en väsentlig komponent i det dagliga arbetet, både för värdeskapande och för att minska risker. En barriär inom detta arbetet är att det tar tid för den behövda kunskapen att utvecklas och att kunskapsöverföring ibland hindras av resursallokering. Kunskapsöverföring anses däremot vara viktigt för långsiktig hållbarhet. Organisationskultur och samhällsmedvetenhet indikeras också påverka minskningen av risker kring maskininlärning.

中小企業是台灣經濟貿易發展的命脈，過去以中小企業為主的出口貿易經濟體系，是創造台灣經濟奇蹟的主要動力。隨著2006年底新巴賽爾協定的正式實施，金融機構為符合新協定規範，亦需將中小企業信用評分程序，納入其徵、授信管理系統，以求信用風險評估皆可量化處理。故本研究將資料採礦技術應用於建置中小企業違約風險模型，針對內部評等法中的企業型暴險，根據新協定與金管會的準則，不僅以財務變數為主，也廣泛增加如企業基本特性及總體經濟因子等非財務變數，納入模型作為考慮變數，計算違約機率進而建置一信用評等系統，作為金融機構對於未來新授信戶之風險管理的參考依據。而本研究將以中小企業中製造傳統產業公司為主要的研究對象，建構企業違約風險模型及其信用評等系統，資料的觀察期間為2003至2005年。 本研究分別利用羅吉斯迴歸、類神經網路、和C&R Tree三種方法建立模型並加以評估比較其預測能力。研究結果發現，經評估確立以1:1精細抽樣比例下，使用羅吉斯迴歸技術建模的效果最佳，共選出六個變數作為企業違約機率模型之建模變數。經驗證後，此模型即使應用到不同期間或其他實際資料，仍具有一定的穩定性與預測效力，且符合新巴塞資本協定與金管會的各項規範，表示本研究之信用評等模型，確實能夠在銀行授信流程實務中加以應用。 / To track the development of Taiwan’s economy history, one very important factor that should never be ignored is the role of small enterprise businesses (the SMEs) which has always been played as a main driving force in the growth of Taiwan’s export trade economic system. With the formal implementation of Basel II in the end of 2006, there arises the need in the banking institutions to establish a credit scoring process for the SMEs into their credit evaluation systems in order to conform to the new accords and to quantify the credit risk assessment process. Consequently, in this research we apply data mining techniques to construct the default risk model for the SMEs in accordance to the new accords and the guidelines published by the FSC (the Financial Supervisory Commission). In addition we not only take the financial variables as the core variables but also increase the non- financial variables such as the enterprise basic characteristics and overall economic factors extensively into the default risk model in order to formulate the probability of credit default risk as well as to establish the credit rating system for the enterprise-based at risk for default in the IRB in the second pillars of the Basel II. The data which used in this research is taken from the traditional SMEs industry ranging from the year of 2003 to 2005. We use each of the following three methods, the Logistic Regression, the Neural Network and the C&R Tree, to build the model. Evaluation of the models is carried out using several statistics test results to compare the prediction accuracy of each model. Based on the result of this research under the 1:1 oversampling proportion, we are inclined to adopt the Logistic Regression techniques modeling as our chosen choice of model. There are six variables being selected from the dataset as the final significant variables in the default risk model. After multiple testing of the model, we believe that this model can withstand the testing for its capability of prediction even when applying in a different time frame or on other data set. More importantly this model is in conformity with the Basel II requirements published by the FSC which makes it even more practical in terms of evaluating credit risk default and credit rating system in the banking industry.

Submitted by Elba Lopes (elba.lopes@fecap.br) on 2016-12-12T17:27:40Z No. of bitstreams: 2 Marcos Santos Oliveira.pdf: 970582 bytes, checksum: 6d45d32bff529faa3e4f900f0ff06309 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2016-12-12T17:27:40Z (GMT). No. of bitstreams: 2 Marcos Santos Oliveira.pdf: 970582 bytes, checksum: 6d45d32bff529faa3e4f900f0ff06309 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2016-09-28 / This work has the objective to analyze the efficiency of the credit scoring model in cross-selling action to provide greater profitability aligned with the risk of new product. This study differs from others by using a database of clients who Payroll-linked loan from the conventional modeling of a Credit Scoring offer another product, the credit card that requires a better profile for meeting payments. The study resulted in 3 of profitability and performance scenarios. In Scenario 1 without use of shoring showed profitability of R$ 0.5 million and delinquencies of 16.1%. In the others scenarios with the use of the yields scores exceeded R$ 2.3 million and delinquencies below 9%. Scenarios 2 and 3 with just score Bureau companies. Scenario 4 includes Credit scoring model developed in this work, we showed the best discrimination between good and bad customers and the highest rate of approval, 75% against 64% of the best Bureau. For this, we used data provided by a financial institution. Using SPSS and statistical techniques, the risk analysis Relative, construction of dummies and Spearman correlation analysis, generated the model Logistic Regression Binary, validated with the Kolmogorov-Smirnov test, the ROC curve and others. The model developed credit scoring showed good results as to their power of customer classification. The effectiveness of Logistic Regression as credit performance prediction tool enables the application of the use of credit scoring model by the financial institution provider of data to improve profitability and default of the customer portfolio by credit card coming from the customer base of payroll loan. / Este trabalho tem o objetivo de analisar a efici??ncia do modelo de credit scoring na a????o de cross-selling para proporcionar uma maior rentabilidade alinhada ao risco do novo produto. A realiza????o deste estudo se diferencia dos demais por utilizar uma base de dados com clientes que realizaram empr??stimo Consignado, a partir da modelagem convencional de um Credit Scoring ofertar outro produto, o Cart??o de Cr??dito que exige um melhor perfil para cumprimento dos pagamentos. O estudo resultou em 3 cen??rios de rentabilidade e desempenho. No Cen??rio 1 sem uso do escoramento apresentou rentabilidade de R$ 0,5 milh??es e inadimpl??ncia de 16,1%. Nos demais cen??rios com uso de escores as rentabilidades ultrapassaram R$ 2,3 milh??es e inadimpl??ncias abaixo de 9%. Os Cen??rios 2 e 3 apenas com escore de empresas Bureau. O Cen??rio 4 inclui o modelo Cr??dit Scoring desenvolvido neste trabalho, apresentou a melhor discrimina????o entre clientes bons e maus e a maior taxa de aprova????o, sendo 75% contra 64% do melhor Bureau. Para isso, utilizou-se de dados fornecido por uma institui????o financeira. Utilizando o SPSS e t??cnicas estat??sticas, a an??lise de Risco Relativo, constru????o de dummies e a an??lise de correla????o de Spearman, foi gerado o modelo de Regress??o Log??stica Bin??ria, validado com o teste Kolmogorov-Smirnov, a Curva ROC e outros. O modelo de Credit Scoring desenvolvido apresentou resultados satisfat??rios quanto a seu poder de classifica????o dos clientes. A efic??cia da Regress??o Log??stica, como ferramenta de predi????o de performance de cr??dito, habilita a aplica????o da utiliza????o do modelo Credit Scoring pela institui????o financeira provedora dos dados para melhorar a rentabilidade e a inadimpl??ncia da carteira de clientes com Cart??o de Cr??dito oriundo da carteira de clientes do empr??stimo Consignado.

Made available in DSpace on 2016-04-25T16:44:29Z (GMT). No. of bitstreams: 1 Iaia Augusto Cuma.pdf: 3926944 bytes, checksum: a90853cb3f6ab828dc780f1e94c1c58e (MD5) Previous issue date: 2012-06-05 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The growth of installment credits in Guinea-Bissau during the study period from 2005 to 2010 can be explained by the relative economic stability, despite some political instability. The significant control of inflation, creating new job opportunities are factors that directly interfere with the purposes and the need to take credit. The study sought to explore and identify the determining factors in the increase of numbers of defaulters with the growth of credit to individuals in Guinea-Bissau. The research aims to contribute to a consistent model for the analysis of risk assessment of credit to individuals that fits the social and economic situation in Guinea-Bissau. To facilitate the review process for evaluating credit risk models were apresented Serasa, Magalhães and Mario and JOS developed bay Santos and Famá, considering a number of variables and parameters. To accomplish the purpose of this study, we addressed the fundamental processes of credit analysis (subjective and objective), regulatory and overview of the credit industry in Guinea-Bissau, its evolution, interest rates, inflation and GDP (Gross Domestic Product) in Guinea-Bissau. The presentation of the proposed model (2 JOS Credit Scoring) and its applicability in a sample of 200 clients drawn from the loan portfolio of the four commercial banks studied in Guinea-Bissau, logistic regression (Logit) yielded a rate adjustment of 54,70% by Nagelkerke index, or is, the model variables together contribute to the explanation of up to 54,70% of the increase in delinquencies in Guinea-Bissau. In Brazil, the same model was tested on a sample of a mid-sized financial institution, the result generated a rate adjustment of 81,90%, or is, the variables of the model, together, contribute to explaining up to 81,90% increase in default. But even with the moderate rate of success of the model is essential that banks in Guinea-Bissau to make continuous reassessment of the model, considering not only the selection and weighting of internal variables (non-systemic risks), as well as the inclusion of events external (systemic risk), which are directly related to income and payment capacity of borrowers / O crescimento de crediários em Guiné-Bissau no período estudado de 2005 a 2010 pode ser explicado pela relativa estabilidade econômica, apesar de algumas instabilidades políticas. O controle significativo da inflação, a criação de novas oportunidades de empregos são fatores que interferem diretamente nos propósitos e na necessidade de se tomar crédito. O estudo buscou explorar e identificar os fatores determinantes no aumento de números de inadimplentes com o crescimento de crédito às pessoas físicas em Guiné-Bissau. A pesquisa pretende-se contribuir com um modelo consistente de análise de avaliação de risco de crédito às pessoas físicas que se adéqua à realidade social e econômica da Guiné-Bissau. Para facilitar o processo de análise de avaliação de risco de crédito foram apresentados os modelos Serasa, Magalhães e Mario e JOS desenvolvido por Santos e Famá, considerando uma série de variáveis e parâmetros. Para efetivar o propósito deste trabalho, foram abordados os processos fundamentais de análise de crédito (subjetiva e objetiva), regulamentação e panorama do setor de crédito em Guiné-Bissau, sua evolução, taxas de juros, inflação e PIB (Produto Interno Bruto) em Guiné-Bissau. A apresentação do modelo proposto (JOS 2 de Credit Scoring) e sua aplicabilidade, em uma amostra de 200 clientes extraída da carteira de crédito dos quatro bancos comerciais estudados em Guiné-Bissau, a regressão logística (Logit) gerou um índice de ajustamento de 54,70% pelo índice de Nagelkerke, ou seja, as variáveis do modelo em conjunto, contribuem para a explicação de até 54,70% do aumento de inadimplência em Guiné-Bissau. No Brasil, o mesmo modelo foi testado em uma amostra de uma instituição financeira de médio porte, o resultado gerou um índice de ajustamento de 81,90%, ou seja, as variáveis do modelo, em conjunto, contribuem para a explicação de até 81,90% do aumento da inadimplência. Porém, mesmo com o índice moderado de acerto do modelo é indispensável que os bancos em Guiné-Bissau façam contínuas reavaliações do modelo, considerando não só a seleção e ponderação de variáveis internas (riscos não-sistêmicos), como também, a inclusão de eventos externos (riscos sistêmicos), que apresentam relação direta com a renda e a capacidade de pagamento dos tomadores

Made available in DSpace on 2016-04-25T18:39:35Z (GMT). No. of bitstreams: 1 Wagner Schmidt.pdf: 8727051 bytes, checksum: 9669cd75306633dfdd1a2d712ce4d2a3 (MD5) Previous issue date: 2010-10-28 / 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 / 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

This dissertation consists of four papers, each with an application of a discrete dependent variable model, censored regression or duration model to a credit market phenomenon or monetary policy question. The first three essays deal with bank lending policy, while the last one studies interest rate policy by Central Banks. In the first essay, a bivariate probit model is estimated to contrast the factors that influence banks’ loan granting decision and individuals’ risk of default. This model is used as a tool to construct a Value at Risk measure of the credit risk involved in a portfolio of consumer loans and to investigate the efficiency of bank lending policy. The second essay takes the conclusions from the first paper as a starting point. It investigates if the fact that banks do not minimize default risk can be explained by the existence of return maximization policy. For this purpose, a Tobit model with sample selection effects and variable censoring limits is developed and estimated on the survival times of consumer loans. The third paper focuses on dormancy, instead of default risk or survival time, as the most important factor affecting risk and return in bank lending. By means of a duration model the factors determining the transition from an active status to dormancy are studied. The estimated model is used to predict the expected durations to dormancy and to analyze the expected profitability for a sample loan applicants. In the fourth paper, the discrete nature of Central Bank interest rate policy is studied. A grouped data model, that can take the long periods of time without changes in the repo rate by the Central Bank into account, is estimated on weekly Swedish data. The model is found to be reasonably good at predicting interest rate changes. / Diss. (sammanfattning) Stockholm : Handelshögsk.

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. / Thesis (Ph.D. (Risk Analysis))--North-West University, Potchefstroom Campus, 2008.

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. / Thesis (Ph.D. (Risk Analysis))--North-West University, Potchefstroom Campus, 2008.

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.

PEC-PG. CNPq / As cooperativas de crédito são regidas pelos mesmos princípios cooperativistas, mas agregam funções financeiras como os bancos tradicionais. A solidariedade na área financeira permite que as cooperativas de crédito levem aos seus cooperados os fundos poupados ou repassando os de desenvolvimento governamental das políticas públicas em forma de crédito. É evidente, no entanto, que nem sempre os “apoios” são devolvidos da mesma maneira como foram recebidos, neste mundo de muita perfeição e de alta competitividade. Assim, integra o elemento estranho no meio onde se imagina seu difícil acesso, a falta de confiança no cumprimento do contrato de crédito contraído pelos credores (cooperados). É conhecido como Risco de Crédito ou de um eventual não pagamento das dívidas contraídas pelos cooperados, o principal fator para processo de Gestão de Risco de Crédito nas instituições cooperativas do mundo. Desse modo, se insere o problema da pesquisa, sendo nova a gestão de risco nas cooperativas de crédito e num mercado assombrado pelas crises financeiras, e com isso se questiona: em que medida ou em que ponto o cumprimento das normas da gestão de risco de crédito nessas instituições vem sendo atendido? Com base nessa preocupação, traçaram-se os objetivos, que apontam em avaliar as práticas de gestão de risco em cooperativas de crédito do Sudoeste do Paraná (PR) de acordo com as resoluções do Conselho Monetário Nacional (CMN). Para isso, tem-se como objetivos específicos: listar as práticas de gestão de risco necessárias para a concessão de crédito; identificar de que forma estão sendo abordadas as práticas de gestão de risco de crédito, considerando-se normas de CMN; verificar qual é o impacto da gestão de risco percebido pelos gestores nas cooperativas de crédito; levantar em termos quantitativos quanto essas cooperativas perdem anualmente em inadimplência (default); comparar as práticas das cooperativas pesquisadas entre si e com as resoluções do CMN. O trabalho se justifica pela importância do processo de gestão de risco para as instituições cooperativas, para a academia e pela contribuição no reforço àquilo que o CMN vem recomendado nas suas resoluções. Para se atingir os resultados pretendidos, foi aplicado um estudo de caso múltiplo nas cooperativas CRESOL, SICOOB e SICREDI/PR, com abordagem qualitativa e quantitativa, em parte com dados secundários e primários. Os dados secundários foram levantados por meio da revisão da literatura referente às áreas de cooperativismo em geral e de crédito, a gestão de risco de crédito e normas e as resoluções do CMN sobre o tema, além de informações nos Relatórios Financeiros das três cooperativas. Os dados primários foram levantados por meio de aplicação de questionário nessas instituições, sendo entrevistados os Gerentes de Crédito, o Assessor Sênior da Controladoria e o Assessor e Supervisor de Crédito. Os dados levantados foram comparados entre as informações dos entrevistados da mesma instituição e depois com as outras, sendo que, na análise dos dados, os nomes das mesmas (instituições) deixaram de ser citadas e foi atribuído o nome de CASO (CASO1, CASO2, e 3 ). Foi identificada uma preocupação com a gestão de risco nessas instituições. Também se percebeu que o crédito só é liberado quando passa por análise de, no mínimo, três pessoas. Existem procedimentos de análise de crédito que seguem todas as alçadas necessárias para que a proposta seja deferida e também são avaliados de acordo com a renda do proponente e as cooperativas singulares são classificadas de acordo com seus patrimônios de Referências (PR) e Patrimônios de Referências exigidas (PRE), para receberem crédito das suas centrais e até para liberarem crédito aos seus cooperados Pessoa Jurídica, caso existam. Mesmo assim, foram localizadas as perdas (prejuízos), algumas mais acentuadas do que outras. Percebe-se que ainda existem reduzidos estudos sobre gestão de risco nas cooperativas de crédito, dada à situação em que se encontravam as mesmas, mas atualmente vale reforçar as pesquisas para conhecer como essas instituições lidam com a gestão de risco de crédito e outros riscos. / Credit unions are governed by the same cooperative principles but add financial functions as traditional banks. Solidarity in the financial area allows credit unions to take to their members or transferring funds spared government development of public policy in the form of credit. But it is not always clear that the "support" are returned the same way they were received, in this world of too much perfection and high competitiveness. Integrates the foreign element in the middle where you think its difficult access, lack of confidence in the fulfillment of the credit agreement contracted by creditors (cooperative). Known as credit risk or a possible non-payment of debts contracted by the cooperatives the main factor for Credit Risk Management process in cooperative institutions in the world. Thus falls the research problem, and new risk management in credit unions and a market haunted by financial crises, and if it asks: To what extent or at what point the compliance of the management of credit risk these institutions has been met? Based on this concern were traced objectives, which aim to assess the practices of risk management in credit unions Southwest of Paraná (PR) according to the resolutions of the National Monetary Council (CMN). For this, one has the following specific objectives: List the risk management practices needed to grant credit; Identify how they are being addressed management practices of credit risk considering CMN standard; Ascertain the impact of the management of risk perceived by managers in credit unions; Rise in quantitative terms as these cooperatives lost annually in default; Compare practices of cooperatives surveyed each other and with the resolutions of the CMN. The work is justified by the importance of the risk management process for cooperative institutions, academia and the contribution to the reinforcement to what the CMN has been recommended in its resolutions. And to achieve the desired results a study of multiple case was applied in cooperative CRESOL, SICOOB and SICREDI / PR, with qualitative and quantitative approach in part with secondary and primary data. The secondary data were collected by reviewing the literature pertaining to the areas of cooperative movement in general and credit risk management and credit standards and CMN resolutions on the topic, also collected in the Financial Reports of the three Credit unions. Primary data were collected through a questionnaire in these institutions, being interviewed Managers of Credit, Senior Advisor of the Comptroller, Assessor and Supervisor of Credit. And the data obtained were compared to the information of the respondents from the same institution and then with the other, in the data analysis of the same names (institutions) are no longer quoted and was assigned the name of CASE (case1 case2 and 3). A concern with risk management in these institutions was identified. Also noticed that credit is only been released when passing by analysis of at least three people. There are procedures for credit analysis that follows all the necessary limits for the proposal to be granted and are evaluated according to the income of the applicant and the individual cooperatives are classified according to their Wealth of References (PR in Portuguese) and Heritage References Required (PRE in Portuguese), to receive credit of their plants and up to release credit to their members Corporations if any. Yet losses were located some steeper others do not. Realize that there are still smaller studies on risk management in credit unions, given the situation they were in the same, but is currently worth strengthen research to know how these institutions deal with the management of credit risk and other risks. / 5000