Classification Algorithms in Financial Application: Credit Risk Analysis on Legal Entities

This research aims at analyzing bank credit of legal entity (in non-default, default and temporarily default), for the purpose of assisting the decision made by the analyst of this area. For that, we used Artificial Neural Networks (ANNs), more specifically, the Multilayer Perceptron (MLP) and the Radial Basis Functions (RBF) and, also, the statistical model of Logistic Regression (LR). For the implementation of the ANNs and LR, the softwares MATLAB and SPSS were used, respectively. For the simulations developed 5.432 data with 15 attributes were collected by the experts of the institution bank (called “XYZ”). The results show that the default clients are easily identifiable, but for the nondelinquent clients and for the temporarily defaulters, the techniques had greater difficulty in the discrimination, suggesting that they are no so discriminants. The main contributions of this work are: the analysis of three classes of clients (non-default, default and temporarily default), rather than just two (non-default and default) as is usually done; the coding of variables (attributes) of the company XYZ aiming to maximize the accuracy of the techniques and the use of the one-against all method, little used by the researchers of this research area. This work presents new insights towards research over Credit Risk Assessment showing other possibilities of client classification and codification, allowing different types of studies to take place

Rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART): Study protocol for a randomized controlled trial

Background: Acute respiratory distress syndrome (ARDS) is associated with high in-hospital mortality. Alveolar recruitment followed by ventilation at optimal titrated PEEP may reduce ventilator-induced lung injury and improve oxygenation in patients with ARDS, but the effects on mortality and other clinical outcomes remain unknown. This article reports the rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART). Methods/Design: ART is a pragmatic, multicenter, randomized (concealed), controlled trial, which aims to determine if maximum stepwise alveolar recruitment associated with PEEP titration is able to increase 28-day survival in patients with ARDS compared to conventional treatment (ARDSNet strategy). We will enroll adult patients with ARDS of less than 72 h duration. The intervention group will receive an alveolar recruitment maneuver, with stepwise increases of PEEP achieving 45 cmH(2)O and peak pressure of 60 cmH2O, followed by ventilation with optimal PEEP titrated according to the static compliance of the respiratory system. In the control group, mechanical ventilation will follow a conventional protocol (ARDSNet). In both groups, we will use controlled volume mode with low tidal volumes (4 to 6 mL/kg of predicted body weight) and targeting plateau pressure <= 30 cmH2O. The primary outcome is 28-day survival, and the secondary outcomes are: length of ICU stay; length of hospital stay; pneumothorax requiring chest tube during first 7 days; barotrauma during first 7 days; mechanical ventilation-free days from days 1 to 28; ICU, in-hospital, and 6-month survival. ART is an event-guided trial planned to last until 520 events (deaths within 28 days) are observed. These events allow detection of a hazard ratio of 0.75, with 90% power and two-tailed type I error of 5%. All analysis will follow the intention-to-treat principle. Discussion: If the ART strategy with maximum recruitment and PEEP titration improves 28-day survival, this will represent a notable advance to the care of ARDS patients. Conversely, if the ART strategy is similar or inferior to the current evidence-based strategy (ARDSNet), this should also change current practice as many institutions routinely employ recruitment maneuvers and set PEEP levels according to some titration method.Hospital do Coracao (HCor) as part of the Program 'Hospitais de Excelencia a Servico do SUS (PROADI-SUS)'Brazilian Ministry of Healt

Algoritmos de classificação em aplicação financeira : avaliação de risco de crédito para pessoa jurídica

Orientadora : Profª Drª Maria Teresinha Arns SteinerCoorientador : Prof. Dr. Cassius Tadeu ScarpinDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia de Produção. Defesa : Curitiba, 20/02/2018Inclui referênciasResumo: A avaliação de risco de credito propõe um problema clássico de tomada de decisão, onde o mérito de determinado individuo receber (ou não) o credito e baseado em estimativas do potencial de devolução deste credito acrescido pelas taxas bancarias. Desde que foi introduzida na década de 1950, a avaliação de risco tem sido amplamente utilizada. A avaliação do risco pode estar associada ao empréstimo por uma instituição financeira a uma determinada empresa (pessoa jurídica) ou a um individuo (pessoa física). A presente pesquisa visa analisar o primeiro caso: credito bancário concedido a entidades jurídicas (empresas), cujas respostas, já conhecidas por meio de registros históricos, estão divididas em classes, sendo estas: clientes adimplentes (pagam seus empréstimos em dia), inadimplentes (aqueles que não pagam) e temporariamente inadimplentes (clientes que pagam, mas com atraso), com o objetivo de auxiliar a decisão a ser tomada pela analista de determinada instituição financeira quanto a conceder (ou não) o credito a novas empresas solicitantes. Para melhor ilustrar a metodologia aqui utilizada fez-se uso de 5.432 dados (instancias; 2.600 Adimplentes, 1.281 Inadimplentes e 1.551 Temporariamente Inadimplentes), cada um dos quais com 15 atributos, de uma grande instituição bancaria brasileira. A metodologia fez uso dos seguintes métodos: Redes Neurais Artificiais (RNAs), mais especificamente, o modelo o Perceptron de Camada Múltipla (MLP) e as Funções de Base Radial (RBF) e, também, o modelo estatístico de Regressão Logística (RL). Para a implementação das RNAs, o software MATLAB foi utilizado e, para o modelo estatístico, foi utilizado o SPSS. O melhor desempenho foi apresentado o apresentado pelas MLP, cujas melhores acurácias foram de 74,70%; 91,4% e 74,6% para as classes "Adimplentes ou Outra", "Inadimplentes ou Outra" e Temporariamente Inadimplentes ou Outra", respectivamente. Desta forma, para a classificação de um novo cliente, teríamos que aplicar o modelo MLP para as três classes, verificando qual delas fornece o maior valor para a acurácia.Abstract: The credit risk assessment proposes a classic problem of decision making, where the merit of a given individual receives (or not) the credit is based on approximations of the potential for repayment of this credit plus bank fees. Since its introduction in the 1950s, risk assessment has been widely used. The risk assessment may be linked to the lending by a financial institution to a particular company (legal entity) or to an individual (individual). The present study aims at analyzing the first case: bank credit granted to legal entities (enterprises), whose answers, already known through historical records, are divided into classes, which are: clients default (pay their loans on time), defaulters (those who do not pay) and temporarily defaulters (customers who pay but are late), in order to assist the decision to be taken by the analyst of a particular financial institution to grant (or not) the credit to new applicant companies. In order to better illustrate the methodology used, 5,432 data were used (instances: 2,600 Default, 1,281 Defaulters and 1,551 Temporarily Defaulters), each one with 15 attributes, of a large Brazilian banking institution. The methodology used the following methods: Artificial Neural Networks (ANNs), more specifically, the Multiple Layer Perceptron (MLP) and the Radial Base Functions (RBF) model, as well as the Logistic Regression (RL). For the implementation of ANNs, MATLAB software was used and, for the statistical model, SPSS was used. The best performance was presented by the MLP, whose best accuracy was 74.70%; 91.4% and 74.6% for the "Default and Others", "Defaulters and Other" and "Temporarily Defaulters and Other" classes, respectively. Thus, for the classification of a new customer, we would have to apply the MLP model to the three classes, verifying which one provides the highest value for the accuracy

Use of machine learning techniques in bank credit risk analysis

The purpose of this article is to compare the performance of a credit scoring model by applying different Machine Learning techniques for the classification of payers in bank financing of companies (5 432 historical records). Clients were considered “non-default” or “default” depending on their default index, thus, 4 238 were considered “non-default” and 1 194 “default”, including the information related to 10 variables (features) that composed the database. First, a random undersampling technique was applied to solve the unbalanced data problem. The variables were then coded in two ways: Code I (categorical variables) and Code II (binary or dummy variables). This was followed by the feature selection methods to detect the most important variables. Finally, we used three classifier algorithms of Machine Learning (ML), Bayesian Networks (BN), Decision Tree (DT) and Support Vector Machine (SVM) comparatively. All these techniques were implemented in WEKA (Waikato Environment for Knowledge Analysis) software. The best performance was 95.2% using balanced classes, with the attributes coded in a binary way and the SVM machine learning technique. So, in this way, it is possible to automatically classify (“non-default” or “default”) new instances making use of the proposed methodology with high performance

Elevated Gastric Antrum Erosions in Portal Hypertension Patients: Peptic Disease or Mucosal Congestion?

Background/Aims: Portal hypertension (PH) is a syndrome characterized by chronic increase in the pressure gradient between the portal vein and inferior vena cava. Previous studies have suggested an increased frequency of antral elevated erosive gastritis in patients with PH, as well as an etiologic association; however, there has not been any histological evidence of this hypothesis to date. Our aim was to evaluate the histological features found in elevated antral erosions in patients with portal hypertension. Methods: Sixty-nine patients were included; 28 with and 41 without PH. All patients underwent endoscopy, and areas with elevated antral erosion were biopsied. Results: In the PH group, 24 patients had inflammatory infiltration with or without edema and vascular congestion, and 4 patients had no inflammation. In the group without PH, all patients showed inflammatory infiltration of variable intensity. There was no statistical significance between the two groups in the presence of Helicobacter pylori. There as a histological similarity between the two groups, if PH patients without inflammation were excluded; however, more edema and vascular congestion were observed in the PH group (p=0.002). Conclusions: The findings show that elevated antral erosions in patients with PH have more evident edema and vascular congestion in addition to lymphocytic infiltration

Rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART): Study protocol for a randomized controlled trial

Background: Acute respiratory distress syndrome (ARDS) is associated with high in-hospital mortality. Alveolar recruitment followed by ventilation at optimal titrated PEEP may reduce ventilator-induced lung injury and improve oxygenation in patients with ARDS, but the effects on mortality and other clinical outcomes remain unknown. This article reports the rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART). Methods/Design: ART is a pragmatic, multicenter, randomized (concealed), controlled trial, which aims to determine if maximum stepwise alveolar recruitment associated with PEEP titration is able to increase 28-day survival in patients with ARDS compared to conventional treatment (ARDSNet strategy). We will enroll adult patients with ARDS of less than 72 h duration. The intervention group will receive an alveolar recruitment maneuver, with stepwise increases of PEEP achieving 45 cmH(2)O and peak pressure of 60 cmH2O, followed by ventilation with optimal PEEP titrated according to the static compliance of the respiratory system. In the control group, mechanical ventilation will follow a conventional protocol (ARDSNet). In both groups, we will use controlled volume mode with low tidal volumes (4 to 6 mL/kg of predicted body weight) and targeting plateau pressure <= 30 cmH2O. The primary outcome is 28-day survival, and the secondary outcomes are: length of ICU stay; length of hospital stay; pneumothorax requiring chest tube during first 7 days; barotrauma during first 7 days; mechanical ventilation-free days from days 1 to 28; ICU, in-hospital, and 6-month survival. ART is an event-guided trial planned to last until 520 events (deaths within 28 days) are observed. These events allow detection of a hazard ratio of 0.75, with 90% power and two-tailed type I error of 5%. All analysis will follow the intention-to-treat principle. Discussion: If the ART strategy with maximum recruitment and PEEP titration improves 28-day survival, this will represent a notable advance to the care of ARDS patients. Conversely, if the ART strategy is similar or inferior to the current evidence-based strategy (ARDSNet), this should also change current practice as many institutions routinely employ recruitment maneuvers and set PEEP levels according to some titration method.13Hospital do Coracao (HCor) as part of the Program 'Hospitais de Excelencia a Servico do SUS (PROADI-SUS)'Brazilian Ministry of Healt