Modelos de classificação e definição de montantes de crédito de clientes

: caso do Grupo Nors

Abstract

Financial analysis is an important feature concerning credit assignment, for financial institutions. Credit Scoring models arise as a way to facilitate the financial analysis needed before conceding credit, as they classify clients in different categories. Financial institutions have been applying these models since they allow them to save time and resources. Nors Group concedes credit to customers in order to increase sales. So, the department “Contas a Receber” (CaR), a receiving accounts area of the Shared Services of Nors is responsible for customer’s credit analysis and for deciding whether or not to concede credit, as well as to defines the amount of credit to concede to a customer that asks for credit. Thus, this work aims to develop and propose a credit scoring model useful to CaR area and a tool to indicate the amount of credit to concede. Therefore, different credit scoring models were developed, using and comparing three distinguished methods (Multiple Discriminant Analysis, Artificial Neural Networks, Logistic Regression). It was also developed a model using genetic algorithms that indicates the amount of credit to concede to a customer applying for credit. This work shows that the Artificial Neural Networks are the classification method giving best results for the credit classification at Nors. Also the model that defines the amount of credit to concede to a customer can become a useful tool to CaR, because the usual analytical pointers are not oriented specifically to that goal.

Date of Award	17 Jul 2017
Original language	Portuguese
Awarding Institution	Universidade Católica Portuguesa
Supervisor	Rita Ribeiro (Supervisor)