A tripartite scorecard for the pay/no pay decision-making in the retail banking industry

• Type: Article in International Scientific Journal
• Date: 2008

Authors

Sousa, MR (Author) Other; Da Costa, JP (Author) FCUP

Journal

Title: Frontiers in Artificial Intelligence and Applications

Vol. 50

Pages: 45-50

ISSN: 0922-6389

Publisher: IOS PRESS

Indexing

ISI Web of Knowledge

Scopus - 2 Citations

Other information

Authenticus ID: P-008-A99

DOI: 10.3233/978-1-58603-890-8-45

Abstract (EN): Traditionally retail banks have supported the credit decision-making on scorecards developed for predicting default in a six-month period or more. However, the underlying pay/no pay cycles justify a decision in a 30-day period. In this work several classification models are built on this assumption. We start by assessing binary scorecards, assigning credit applicants to good or bad risk classes according to their record of defaulting. The detection of a critical region between good and bad risk classes, together with the opportunity of manually classifying some of the credit applicants, led us to develop a tripartite scorecard, with a third output class, the review class, in-between the good and bad classes. With this model 87% decisions are automated, which compares favourably with the 79% automation rate of the actual scorecards.

Language: English

Type (Professor's evaluation): Scientific