Submitted by Melissa Forti (melissaforti@gmail.com) on 2018-09-03T12:07:02Z No. of bitstreams: 1 Melissa_Forti_dissertacao.pdf: 2661806 bytes, checksum: a588904f04c4b3d523f82e716231ffd6 (MD5) / Approved for entry into archive by Joana Martorini (joana.martorini@fgv.br) on 2018-09-03T17:14:01Z (GMT) No. of bitstreams: 1 Melissa_Forti_dissertacao.pdf: 2661806 bytes, checksum: a588904f04c4b3d523f82e716231ffd6 (MD5) / Approved for entry into archive by Suzane Guimarães (suzane.guimaraes@fgv.br) on 2018-09-04T13:30:27Z (GMT) No. of bitstreams: 1 Melissa_Forti_dissertacao.pdf: 2661806 bytes, checksum: a588904f04c4b3d523f82e716231ffd6 (MD5) / Made available in DSpace on 2018-09-04T13:30:28Z (GMT). No. of bitstreams: 1 Melissa_Forti_dissertacao.pdf: 2661806 bytes, checksum: a588904f04c4b3d523f82e716231ffd6 (MD5) Previous issue date: 2018-08-08 / A necessidade de conhecer o cliente sempre foi um diferencial para o mercado e nestes últimos anos vivenciamos um crescimento exponencial de informações e técnicas que promovem a avaliação para todas as fases do ciclo de crédito, desde a prospecção até a recuperação de dívidas. Nesse contexto, as empresas estão investindo cada vez mais em métodos de Machine Learning para que possam extrair o máximo de informações e assim terem processos mais assertivos e rentáveis. No entanto, essas técnicas possuem ainda alguma desconfiança no ambiente financeiro. Diante desse contexto, o objetivo desse trabalho foi aplicar as técnicas de Machine Learning: Random Forest, Support Vector Machine e Gradient Boosting para um banco de dados real de cobrança, a fim de identificar os clientes mais propensos a quitar suas dívidas (Collection Score) e comparar a acurácia e interpretação desses modelos com a metodologia tradicional de Regressão Logística. A principal contribuição desse trabalho está relacionada com a comparação das técnicas em um cenário de recuperação de crédito considerando as principais características, vantagens e desvantagens. / The need to know the customer has always been a differential for the market, and in currently years we have experienced an exponential growth of information and techniques that promote this evaluation for all phases of the credit cycle, from prospecting to debt recovery. In this context, companies are increasingly investing in Machine Learning methods, so that they can extract the maximum information and thus have more assertive and profitable processes. However, these models still have a lot of distrust in the financial environment. Given this need and uncertainty, the objective of this work was to apply the Machine Learning techniques: Random Forest, Support Vector Machine and Gradient Boosting to a real collection database in order to identify the recover clients (Collection Score) and to compare the accuracy and interpretation of these models with the classical logistic regression methodology. The main contribution of this work is related to the comparison of the techniques and if they are suitable for this application, considering its main characteristics, pros and cons.