ZIP Code Versus Georeference

Abstract

When dealing with predictive modeling of credit-granting, different types of attributes are used: Cadastral, Behavioral, Business / Proposal, Credit Bureaux, in addition to Public, Private or Subsidiaries Sources. The Postal Address Code (Código de Endereçamento Postal CEP in Portuguese) in Brazil, in particular, has a unique contribution capacity (uncorrelated with most other attributes in general) and reasonably good predictive power. CEP is frequently used by truncating its numeric representation, considering the first d digits, for example. In this report, a preliminary methodology is proposed, aiming to elaborate clustering sets of CEPs by considering the information of clients' defaults over a period of time. Additionally, we tested the number of clusters obtained using the Information Value criterion. Promising solutions are obtained using statistical and optimizing approaches. Other methodologies are suggested and could be complementary with the principal methodology proposed.