Submitted by Luciana Sebin (lusebin@ufscar.br) on 2016-09-19T18:31:03Z No. of bitstreams: 1 DissRFV.pdf: 2859272 bytes, checksum: 4d67f29c51b595eea8e7a1fe15261706 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-09-20T18:16:18Z (GMT) No. of bitstreams: 1 DissRFV.pdf: 2859272 bytes, checksum: 4d67f29c51b595eea8e7a1fe15261706 (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-09-20T18:16:25Z (GMT) No. of bitstreams: 1 DissRFV.pdf: 2859272 bytes, checksum: 4d67f29c51b595eea8e7a1fe15261706 (MD5) / Made available in DSpace on 2016-09-20T18:16:33Z (GMT). No. of bitstreams: 1 DissRFV.pdf: 2859272 bytes, checksum: 4d67f29c51b595eea8e7a1fe15261706 (MD5) Previous issue date: 2015-12-18 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / 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. / 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.

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 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

This thesis provides a comprehensive ranking of 11 Czech statistical and 4 foreign credit scoring models. The ranking is based on the predictive performance of individual models, as measured by the area under curve, evaluated on a randomly sampled set of 250 training and validation samples. After establishing a baseline comparison, 3 avenues of estimation setup optimization are explored, namely missing value treatment, estimation method and the use of additional non-financial variables. After being optimized, the models are once again ranked based on their predictive performance. Statistical inference is drawn using ANOVA and the Friedman test, along with the corresponding Tukey and Nemeyi pos-hoc tests. In their baseline form, the Czech credit scoring models are found to be outperformed by the foreign benchmark model. Treating the missing values by OLS imputation and estimating the models by probit, significantly is found to significantly improve their predictive performance. In their optimized form, the difference in predictive performance between Czech and foreign credit scoring model is found to be only marginal. JEL Classification G28, G32, G33, G38 Keywords credit scoring, multiple discriminant analysis, logit analysis, probit analysis Author's e-mail 71247263@fsv.cuni.cz Supervisor's e-mail...

Machine learning is becoming a part of everyday life and has an indisputable impact across large array of industries. In the financial industry, this impact lies particularly in predictive modelling. The goal of this thesis is to describe the basic principles of artificial intelligence and its subset, machine learning. The most widely used machine learning techniques are outlined both in a theoretical and a practical way. As a result, four models were assembled within the thesis. Results and limitations of each model were discussed and these models were also mutually compared based on their individual per- formance. The evaluation was executed on a real world dataset, provided by Home Credit company. Final performance of machine learning methods, measured by the KS and GINI metrics, was either very comparable or even worse than the performance of a traditional logistic regression. Still, the problem may lie in an insu cient dataset, in the improper data prepara- tion, or in inappropriately used algorithms, not necessarily in the models themselves.

Credit scoring is critical for banks to evaluate new loan applications and monitor existing customers. Machine learning has been extensively researched for this case; however, the adoption of machine learning methods is minimal in financial risk management. The primary reason is that algorithms are viewed as ‘black box models’ and cannot satisfy regulatory requirements. While deep learning methods such as LSTM have been evaluated for behavioural credit scoring based on performance, research has not holistically evaluated these models on performance and explainability. To answer the research question, How can traditional machine learning and deep learning methods conform with regulatory guidelines for explainable artificial intelligence (XAI), and are they preferable to benchmark methods? this thesis used a public customer credit card dataset to compare the performance and explainability of machine learning and deep learning models against the benchmark statistical model linear regression. Model performance was evaluated using ROC-AUC, accuracy, Brier scores, F1 scores and the G-mean. The McNemar test evaluated whether, through pairwise comparison, the model performances were statistically different. The models were then evaluated on whether local and global explanations could be ascertained using feature/permutation importance and SHAP. The results found that neither the machine learning model, XGBoost, nor the deep learning model, LSTM, produced a statistically superior performance from the benchmark model. While there were performance improvements, only the machine learning model using post-hoc methods could produce local and global explanations. Given the strict regulatory environment, it is understandable that banks are hesitant to implement machine learning or deep learning models that lack the adequate levels of explainability regulators require.

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