Henriques, Roberto André PereiraSilvestre, Martinho de Matos2019-06-032019-06-032019-04-03http://hdl.handle.net/10362/71588Thesis proposal presented as partial requirement for obtaining the Master’s degree in Statistics and Information Management, with specialization in Risk Analysis and ManagementOver the last decade, several banks have developed models to quantify credit risk. In addition to the monitoring of the credit portfolio, these models also help deciding the acceptance of new contracts, assess customers profitability and define pricing strategy. The objective of this paper is to improve the approach in credit risk modeling, namely in scoring models to predict default events. To this end, we propose the development of a three-stage ensemble model that combines the results interpretability of the Scorecard with the predictive power of machine learning algorithms. The results show that ROC index improves 0.5%-0.7% and Accuracy 0%-1% considering the Scorecard as baseline.engEnsemble ModelingProbability of DefaultCredit ScoringScorecardLogistic RegressionDecision TreeArtificial Neural NetworkMultilayer PerceptronRandom ForestMachine Learningmaster thesis202250768