Template-Type: ReDIF-Paper 1.0
Author-Name:  Guilherme Barreto
Author-Name-First: Guilherme
Author-Name-Last: Barreto
Author-Name:  Fernandes Rinaldo Artes
Author-Name-First: Fernandes Rinaldo
Author-Name-Last: Artes
Title: Spatial correlation in credit risk and its improvement in credit scoring
Abstract:  Credit scoring models are important tools in the credit granting process. These models measure the credit risk of a prospective client based on idiosyncratic variables and macroeconomic factors. However, small and medium sized enterprises (SMEs) are subject to the effects of the local economy. From a data set with the localization and default information of 9 million Brazilian SMEs, provided by Serasa Experian (the largest Brazilian credit bureau), we propose a measure of the local risk of default based on the application of ordinary kriging. This variable has been included in logistic credit scoring models as an explanatory variable. These models have shown better performance when compared to models without this variable.A gain around 7 percentage points of KS and Gini was observed.
Length:  19 pages
Creation-Date:  2013
Order-URL: https://repositorio.insper.edu.br/handle/11224/5959
File-URL: https://repositorio.insper.edu.br/handle/11224/5959
File-Format: text/html
File-Function: Full text
Number: 180
Handle: RePEc:aap:wpaper:180