Publicação
Three-stage ensemble model : reinforce predictive capacity without compromising interpretability
| dc.contributor.advisor | Henriques, Roberto André Pereira | |
| dc.contributor.author | Silvestre, Martinho de Matos | |
| dc.date.accessioned | 2019-06-03T16:10:23Z | |
| dc.date.available | 2019-06-03T16:10:23Z | |
| dc.date.issued | 2019-04-03 | |
| dc.description | Thesis proposal presented as partial requirement for obtaining the Master’s degree in Statistics and Information Management, with specialization in Risk Analysis and Management | pt_PT |
| dc.description.abstract | Over 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. | pt_PT |
| dc.identifier.tid | 202250768 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/71588 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Ensemble Modeling | pt_PT |
| dc.subject | Probability of Default | pt_PT |
| dc.subject | Credit Scoring | pt_PT |
| dc.subject | Scorecard | pt_PT |
| dc.subject | Logistic Regression | pt_PT |
| dc.subject | Decision Tree | pt_PT |
| dc.subject | Artificial Neural Network | pt_PT |
| dc.subject | Multilayer Perceptron | pt_PT |
| dc.subject | Random Forest | pt_PT |
| dc.subject | Machine Learning | pt_PT |
| dc.title | Three-stage ensemble model : reinforce predictive capacity without compromising interpretability | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Estatística e Gestão de Informação, especialização em Análise e Gestão de Risco | pt_PT |