|
Title
|
|
|
|
Retail credit scoring using fine-grained payment data
|
|
|
Author
|
|
|
|
|
|
|
Abstract
|
|
|
|
| Banks are continuously looking for novel ways to leverage their existing data assets. A major source of data that has not yet been used to the full extent is massive fine‐grained payment data on the bank's customers. In the paper, a design is proposed that builds predictive credit scoring models by using the fine‐grained payment data. Using a real life data set of 183 million transactions made by 2.6 million customers, we show that the scalable implementation that is put forward leads to a significant improvement in the receiver operating characteristic area under the curve, with only seconds of computation needed. When investigating the 1% riskiest customers, twice as many defaulters are detected when using the payment data. Such an improvement has a big effect on the overall working of the bank, from applicant scoring to minimum capital requirements. |
|
|
|
Language
|
|
|
|
English
|
|
|
Source (journal)
|
|
|
|
Journal of the Royal Statistical Society : series A: statistics in society. - London, 1988, currens
|
|
|
Publication
|
|
|
|
London
:
2019
|
|
|
ISSN
|
|
|
|
0964-1998
[print]
1467-985X
[online]
|
|
|
DOI
|
|
|
|
10.1111/RSSA.12469
|
|
|
Volume/pages
|
|
|
|
182
:4
(2019)
, p. 1227-1246
|
|
|
ISI
|
|
|
|
000492420800007
|
|
|
Full text (Publisher's DOI)
|
|
|
|
|
|
|
Full text (open access)
|
|
|
|
|
|
|
Full text (publisher's version - intranet only)
|
|
|
|
|
|