Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-07-12T12:11:15Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Tese_Rosalvo_Neto_CIN_2015.pdf: 7674683 bytes, checksum: 99037c704450a9a878bcbe93ab8b392d (MD5) / Made available in DSpace on 2016-07-12T12:11:15Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Tese_Rosalvo_Neto_CIN_2015.pdf: 7674683 bytes, checksum: 99037c704450a9a878bcbe93ab8b392d (MD5) Previous issue date: 2015-12-11 / CAPEs / A etapa de pré-processamento em um projeto de descoberta do conhecimento é custosa, em geral, consome cerca de 50 a 80% do tempo total de um projeto. É nesta etapa que um banco de dados relacional é transformado para aplicação de um algoritmo de mineração de dados. A transformação dos dados nesta etapa é uma tarefa complexa, uma vez que exige uma forte integração entre projetistas de banco de dados e especialistas do domínio da aplicação. Os frameworks que buscam sistematizar a etapa de transformação dos dados encontrados na literatura apresentam limitações significativas quando aplicados a soluções comportamentais, como Credit Behavior Scoring. Estas soluções visam a auxiliar as instituições financeiras a decidirem sobre a concessão de crédito aos consumidores com base no risco das solicitações. Este trabalho propõe um framework baseado no Desenvolvimento Dirigido por Modelos para sistematizar esta etapa em soluções de Credit Behavior Scoring. Ele é composto por um meta-modelo que mapeia os conceitos do domínio e um conjunto de regras de transformações. As três principais contribuições do framework proposto são: 1) aumentar o poder discriminatório da solução, através da construção de novas variáveis que maximizam o conteúdo estatístico da informação do domínio; 2) reduzir o tempo da transformação dos dados através da geração automática de código e 3) permitir que profissionais e pesquisadores de Inteligência Artificial e Estatística realizem a transformação dos dados sem o auxílio de especialistas de Banco de Dados. Para validar o framework proposto, dois estudos comparativos foram realizados. Primeiro, um estudo comparando o desempenho entre os principais frameworks existentes na literatura e o framework proposto foi realizado em duas bases de dados. Uma base de dados de um conhecido benchmark de uma competição internacional organizada pela PKDD, e outra obtida de uma das maiores empresas de varejo do Brasil, que possui seu próprio cartão de crédito. Os frameworks RelAggs e Validação de Múltiplas Visões Baseado em Correção foram escolhidos como representantes das abordagens proposicional e mineração de dados relacional, respectivamente. A comparação foi realizada através do processo de validação cruzada estratificada, para definir os intervalos de confiança para a avaliação de desempenho. Os resultados mostram que o framework proposto proporciona um desempenho equivalente ou superior aos principais framework existentes, medido pela área sob a curva ROC, utilizando uma rede neural MultiLayer Perceptron, K vizinho mais próximos e Random Forest como classificadores, com um nível de confiança de 95%. O segundo estudo verificou a redução de tempo proporcionada pelo framework durante a transformação dos dados. Para isso, sete times compostos por estudantes de uma universidade brasileira mensuraram o tempo desta atividade com e sem o framework proposto. O teste pareado Wilcoxon Signed-Rank mostrou que o framework proposto reduz o tempo de transformação com um nível de confiança de 95%. / The pre-processing stage in knowledge discovery projects is costly, generally taking between 50 and 80% of total project time. It is in this stage that data in a relational database are transformed for applying a data mining technique. This stage is a complex task that demands from database designers a strong interaction with experts who have a broad knowledge about the application domain. The frameworks that aim to systemize the data transformation stage have significant limitations when applied to behavior solutions such as the Credit Behavior Scoring solutions. Their goal is help financial institutions to decide whether to grant credit to consumers based on the credit risk of their requests. This work proposes a framework based on the Model Driven Development to systemize this stage in Credit Behavioral Scoring solutions. It is composed by a meta-model which maps the domain concepts and a set of transformation rules. This work has three main contributions: 1) improving the discriminant power of data mining techniques by means of the construction of new input variables, which embed new knowledge for the technique; 2) reducing the time of data transformation using automatic code generation and 3) allowing artificial intelligence and statistics modelers to perform the data transformation without the help of database experts. In order to validate the proposed framework, two comparative studies were conducted. First, a comparative study of performance between the main existing frameworks found in literature and the proposed framework applied to two databases was performed. One database from a known benchmark of an international competition organized by PKDD, and another one obtained from one of the biggest retail companies from Brazil, that has its own private label credit card. The RelAggs and Correlation-based Multiple View Validation frameworks were chosen as representatives of the propositional and relational data mining approaches, respectively. The comparison was carried out through by a 10-fold stratified cross-validation process with ten stratified parts in order to define the confidence intervals. The results show that the proposed framework delivers a performance equivalent or superior to those of existing frameworks, for the evaluation of performance measured by the area under the ROC curve, using a Multilayer Perceptron neural network, k-nearest neighbors and Random Forest as classifiers, with a confidence level of 95%. The second comparative study verified the reduction of time required for data transformation using the proposed framework. For this, seven teams composed by students from a Brazilian university measured the runtime of this stage with and without the proposed framework. The paired Wilcoxon Signed-Rank’s Test showed that the proposed framework reduces the time of data transformation with a confidence level of 95%.

Made available in DSpace on 2016-03-15T19:32:48Z (GMT). No. of bitstreams: 1 Hugo Crespi Junior.pdf: 1552051 bytes, checksum: 34b1ca02581c6a8240a8bd4702d15a71 (MD5) Previous issue date: 2014-02-14 / One of the most important ways to finance consumers in the Brazilian market is the consumer credit offered in store. Provided by independent or captive finance companies, the consumer credit is normally granted or denied without taking into account its effect on the retailer s profitability. Denying credit to finance a high profit margin product is more damaging to the companies profits than if such refusal is about the sale of lower margin product. This suggests that there is an opportunity to improve the profitability in this sales channel. The objective of this research was to check the possibility to increase the Brazilian retailers profitability by introducing the retail products operating margin as an additional parameter of the consumer credit analysis. Simulations using tangents to ROC curves, as proposed by Stein (2005), made possible to confirm, through a new balance of type I and type II errors, that the maximization of corporate earnings occurs when using operating retail margins for determining cutoffs in consumer credit models. / Uma das mais importantes ferramentas de financiamento no varejo brasileiro é o crédito direto ao consumidor oferecido nos estabelecimentos por ocasião da compra. Operado através de financeiras cativas ou parceiras, o crédito é, normalmente, concedido ou negado sem que se leve em conta seu efeito na lucratividade do varejista. Quando se recusa o financiamento para um produto de grande margem de lucro, o efeito sobre os ganhos da empresa é evidentemente maior do que quando essa recusa inibe a venda de produto de menor margem, o que sugere haver ineficácia no processo. Esta pesquisa visou verificar se há espaço para aumentar a rentabilidade no varejo brasileiro, introduzindo a margem operacional nos critérios utilizados para concessão de crédito direto ao consumidor. Utilizando a curva ROC e a abordagem oferecida por Stein (2005), construíram-se simulações em torno de valores reais praticados no mercado, que permitiram confirmar, através de um novo balanceamento dos erros tipo I e tipo II, que ocorre a maximização de ganhos empresarias quando as margens operacionais do varejo são consideradas para a determinação de pontos de corte em modelos de crédito direto ao consumidor.

Factoring innebär försäljning av fakturor till tredjepart och därmed möjlighet att få in kapital snabbt och har blivit alltmer populärt bland företag idag. Ett fakturaköp innebär en viss kreditrisk för företaget i de fall som fakturan inte blir betald och som köpare av kapital önskar man att minimera den risken. Aros Kapital erbjuder sina kunder tjänsten factoring. Under detta projekt undersöks möjligheten att använda maskininlärningsmetoder för att bedöma om en faktura är en bra eller dålig investering. Om maskininlärningen visar sig vara bättre än manuell hantering kan även bättre resultat uppnås i form av minskade kreditförluster, köp av fler fakturor och därmed ökad vinst. Fyra maskininlärningsmetoder jämfördes: beslutsträd, slumpmässig skog, Adaboost och djupa neurala nätverk. Utöver jämförelse sinsemellan har metoderna jämförts med Aros befintliga beslut och nuvarande regelmotor. Av de jämförda maskininlärningsmetoderna presterade slumpmässig skog bäst och visade sig bättre än Aros befintliga beslut på de testade fakturorna, slumpmässig skog fick F1-poängen 0,35 och Aros 0,22 . / Today, companies can sell their invoices to a third party in order to to quickly capitalize them. This is called factoring. For the financial institute which serve as the third party, the purchase of an invoice infers a certain risk in case the invoice is not paid, a risk the financial institute would like to minimize. Aros Kapital is a financial institute that offers factoring as one of their services. This project at Aros Kapital evaluated the possibility of using machine learning to determine whether or not an invoice will be good investment for the financial institute. If the machine learning algorithm performs better than manual handling and by minimizing credit losses and buying more invoices this could lead to an increase in profit for Aros. Four machine learning algorithms have been compared: decision trees, random forest, Adaboost and deep neural network. Beyond the comparison between the four algorithms, the algorithms were also compared with Aros actual decision and Aros current rule engine solution. The  results show that random forest is the best performing algorithm and it also shows a slight improvement on performance compared to Aros actual decision, random forest got an F1- core of 0.35 and Aros 0.22.

This paper deals with a measure theoretical model for evaluation of e-learning programs. Based on methods of general measure theory an evaluation model is developed which can be used for assessment of complex target structures in context of e-learning programs. With the presented rating function target structures can be evaluated by a scoring value which indicates how the targets in sense of a given logical target structure has been reached. A procedure is developed for the estimation of scoring values for target structures based on adapted assessment checklists.

The thesis presents a comparison of several selected modeling methods used by financial institutions for (not exclusively) decision-making processes. First theoretical part describes well known modeling methods such as logistic regression, decision trees, neural networks, alternating decision trees and relatively new method called "Random forest". The practical part of thesis outlines some processes within financial institutions, in which selected modeling methods are used. On real data of two financial institutions logistic regression, decision trees and decision forest are compared which each other. Method of neural network is not included due to its complex interpretability. In conclusion, based on resulting models, thesis is trying to answers, whether logistic regression (method most widely used by financial institutions) remains most suitable.

Submitted by Nadir Basilio (nadirsb@uninove.br) on 2017-08-04T21:33:38Z No. of bitstreams: 1 Priscila Cristina Silva.pdf: 2177666 bytes, checksum: a8d3c5290664fa16f138371def86fcdd (MD5) / Made available in DSpace on 2017-08-04T21:33:38Z (GMT). No. of bitstreams: 1 Priscila Cristina Silva.pdf: 2177666 bytes, checksum: a8d3c5290664fa16f138371def86fcdd (MD5) Previous issue date: 2017-02-23 / Customers with credit agreement in arrears for more than 90 days are characterized as non-performing loans and cause concerns in credit companies because the lack of guarantee of discharge debtor's amount. To treat this type of customer are applied collection scoring models that have as main objective to predict those debtors who have propensity to honor their debts, that is, this model focuses on credit recovery. Models based on statistical prediction techniques can be applied to the recovery of these credits, such as logistic regression and discriminant analysis. Therefore, the aim of this paper was to apply logistic regression and discriminant analysis models in predicting the recovery of non-performing loans credit portfolios. The database used was provided by the company Serasa Experian and contains a sample of ten thousand customers with twenty independent variables and a variable binary response (dependent) indicating whether or not the defaulting customer paid their debt. The sample was divided into training, validation and test and the models cited in the objective were applied individually. Then, two new logistic regression models and discriminant analysis were implemented from the outputs of the individually implemented models. The both models applied individually as the new models had generally good performance form, highlighting the new model of discriminant analysis that got correct classification of percentage higher than the new logistic regression model. It was concluded, then, based on the results that the models are a good option for predicting the credit portfolio recovery. / Os clientes que possuem contrato de crédito em atraso há mais de 90 dias são caracterizados como non-performing loans e preocupam as instituições financeiras fornecedoras de crédito pela falta de garantia da quitação desse montante devedor. Para tratar este tipo de cliente são aplicados modelos de collection scoring que têm como principal objetivo predizer aqueles devedores que possuem propensão em quitar suas dívidas, ou seja, esse modelo busca a recuperação de crédito. Modelos baseados em técnicas estatísticas de predição podem ser aplicados na recuperação como a regressão logística e a análise discriminante. Deste modo, o objetivo deste trabalho foi aplicar os modelos de regressão logística e análise discriminante na predição da recuperação de portfólios de crédito do tipo non-performing loans. A base de dados utilizada foi cedida pela empresa Serasa Experian e contém uma amostra de dez mil indivíduos com vinte variáveis independentes e uma variável resposta (dependente) binária indicando se o cliente inadimplente pagou ou não sua dívida. A amostra foi dividida em treinamento, validação e teste e foram aplicados os modelos citados de forma individual. Em seguida, dois novos modelos de regressão logística e análise discriminante foram implementados a partir das saídas (outputs) dos modelos aplicados individualmente. Com base nos resultados, tanto os modelos aplicados individualmente quanto os novos modelos apresentaram bom desempenho, com destaque para o novo modelo de análise discriminante que apresentou um percentual de classificações corretas superior ao novo modelo de regressão logística. Concluiu-se, então, que os modelos são uma boa opção para predição da recuperação de portfólios de crédito do tipo non-performing loans.

The theoretical part of thesis covers consumer credit, particular parts of credit process and credit registers. Analysis of credit risk management in a bank of concern, quality of credit portfolio and suggestion of changes in rating of retail receivables are dealt with in the practical part.

Idag finns det cirka 1,2 miljoner företag i Sverige varav 96% av dessa är små företag. Svenska banker lånade ut cirka 3 000 miljarder kronor till företag 2021. Små företag har svårt för att finansiera sin verksamhet och vänder sig därför till finansiärer som banker för lån. Kreditgivaren gör en kreditbedömning av företaget som ansöker om ett lån. Små företag saknar ofta tillräcklig information, vilket kan vara utmanande vid kreditbedömning. Syftet med denna studie är att få en förståelse för de bedömningsfaktorer som är av vikt för kreditgivare vid kreditbedömning. Vidare vill vi undersöka hur informationsunderlag säkerställs av kreditgivare för att göra denna bedömning av små aktiebolag. Detta görs genom att ta hänsyn till den funktion som kreditupplysningsföretag och revisorer har i kreditbedömning. För att undersöka forskningsfrågorna genomförs en kvalitativ studie genom semistrukturerade intervjuer med banker, kreditupplysningsföretag och revisorer. Resultatet visar att återbetalningsförmåga, säkerhet och kundkännedom är de viktigaste bedömningsfaktorerna för en kreditgivare att analysera. Dessa bedömningsfaktorer kan kopplas till 5C-modellen. Bankerna använder sig av både finansiell- och icke-finansiell information i kreditbedömningar som till största del samlas in från kreditupplysningsföretag, men även från småföretagen för att komplettera den bristande informationen. Enligt undersökningsresultatet i denna studie ser respondenterna på avskaffandet av revisionsplikten som en nackdel för kreditbedömning. Det går därför att dra slutsatsen att kreditupplysningsföretag har en relativt stor funktion i de tre viktigaste bedömningsfaktorerna, medan revisorer har en mindre viktig funktion i dem. Kreditupplysningsföretag är kreditgivarens huvudsakliga källa för att samla in information. Trots det kan kreditgivarna fortfarande uppleva brist på information. Bankerna har anpassat sina modeller för kreditbedömning efter avskaffandet av revisionsplikten, vilket visar att revisorernas funktion är betydligt mindre i bedömningen. Trots detta skulle kreditgivaren uppskatta företag med reviderad finansiell information. / There are approximately 1.2 million companies in Sweden today, from which 96% of these are small companies. Swedish banks lent out SEK 3,000 billion to companies in 2021. Small companies face difficulties in financing their operations and therefore turn to financiers such as banks for lending. The lender performs a credit assessment of the company applying for a loan. Small companies often lack sufficient information which can be challenging in credit assessment. The aim of this study is to get an understanding of the assessment factors that are of importance to lenders in credit assessment. Furthermore, we want to investigate how the information base is secured by creditors in order to make this assessment of small joint stock companies. This is accomplished by taking the function that credit reporting companies and auditors have in credit assessment into account. To investigate the research questions, a qualitative study is conducted through semi-structured interviews with banks, credit reporting companies and auditors. The results show that repayment capacity, collateral and customer knowledge are the key assessment factors for the lenders to analyze. These assessment factors can be linked to the 5C model. The banks use both financial and non-financial information in credit assessments which are mostly collected from credit reporting companies, but also from the small companies to supplement the lacking information. The results also show that auditing in a company leads to the lender gaining increased trust for the company. According to the survey results, the respondents in this study see the abolition of the audit obligation as a disadvantage for credit assessment. The conclusion can thus be drawn that credit reporting companies have a relatively large function in the three most important assessment factors, whereas auditors have a less important function in it. Credit reporting companies are lenders’ main source for collecting information, yet the lenders may still experience a lack of information. Banks have adapted their models in credit assessment after the abolition of the audit obligation, which shows that the auditors’ function is significantly smaller in the assessment. Despite this, the lender would appreciate companies with audited financial information.

[pt] Tão expressiva quanto a curva de crescimento do número de matrículas nas instituições de ensino superior (IES) privadas nos últimos anos é a respectiva curva da inadimplência, cujo aumento pode ser explicado, principalmente, pelo aprofundamento da crise econômica no país e pela redução do número de vagas ofertadas pelo FIES. A inadimplência apresenta-se como um desafio à gestão financeira das instituições de ensino, uma vez que impacta os seus custos operacionais e acaba sendo repassada aos alunos sob forma de aumento de mensalidade. Além disso, a evasão estudantil é também uma das principais consequências da inadimplência, à medida que alunos com dificuldades financeiras acabam por abandonar seus cursos, representando para as instituições de ensino não só uma perda econômica, como também acadêmica e social. As IES, em sua maioria, não utilizam qualquer tipo de técnica de credit scoring para prever o risco de seus alunos se tornarem inadimplentes. Nesse sentido, este trabalho apresenta uma metodologia quantitativa para previsão de risco de inadimplência de alunos ativos. Baseado em dados históricos de alunos que estavam inadimplentes ou adimplentes, modelos gerados por algoritmos de machine learning foram estimados e comparados. Por fim, os resultados obtidos evidenciaram a relação entre a inadimplência e a variação do valor pago ao longo dos semestres analisados, quantidade média de disciplinas cursadas, natureza empregatícia ao aluno e existência de débitos em semestres anteriores. Com a aplicação dos modelos propostos, as IES seriam capazes de identificar alunos com maior risco de inadimplência e planejar ações preventivas específicas para este grupo. / [en] As impressive as the growth rate in the number of enrollments in private higher education institutions in recent years is the increase in the related default rate, driven by the deepening economic crisis in Brazil and by the reduction of the number of vacancies offered by the FIES. Default presents itself as a challenge to the financial management of educational institutions, since it impacts their operational costs and ends up being passed on to students in the form of an increase in tuition. In addition, student dropout is also one of the main consequences of default, since students with economic difficulties end up abandoning their courses. Most higher education institutions do not use any type of credit scoring analysis to predict the risk of their students becoming defaulters, failing to understand which factors cause it, and, therefore, refraining from planning preventive actions. Therefore, this study presents a quantitative methodology to predict the default risk of active students. Models generated by machine learning algorithms were analyzed based on a historical database of students who were in or not in default. The results showed a relationship between default and economic, academic and social characteristics of students. Thus, by employing models such as the ones proposed, higher education institutions should be able to identify those students who are at higher risk of defaulting and take specific preventive actions to prevent such an outcome.

A large number of organizations these days are offering some kind of digital services, relyon digital technologies for processing, storing, and sharing of information, are harvesting moderntechnologies to offer remote working arrangements and may face direct cybersecurity risks. Theseare some of the properties of a modern organization. The cybersecurity vulnerability managementprograms of most organizations have been relying on one-dimensional information to prioritizeefforts of remedying security flaws for many years. When combined with the ever-growing attacksurface of modern organizations, the number of vulnerabilities disclosed yearly and the limitedresources available to cybersecurity teams, this renders the goal of securing an organization almostimpossible. This thesis aims at reviewing existing methodologies as observed in academicliterature and in the industry, highlighting their disadvantages, as well as the importance of adynamic, data-driven and informed approach and finally providing a tool that can assist thevulnerability prioritization efforts and increase resource utilization and efficiency. The thesis isinspired by Design Science Research, to design and develop a web-based cybersecurity tool thatcan be utilized towards a data-rich and rigorous approach of Vulnerability Management, by relyingon various cyber threat intelligence metrics.