The University of Southampton
University of Southampton Institutional Repository

Kalman filtering as a performance monitoring technique for a propensity scorecard

Kalman filtering as a performance monitoring technique for a propensity scorecard
Kalman filtering as a performance monitoring technique for a propensity scorecard
Propensity scorecards allow forecasting, which bank customers would like to be granted new credits in the near future, through assessing their willingness to apply for new loans. Kalman filtering can help to monitor scorecard performance. Data from successive months are used to update the baseline model. The updated scorecard is the output of the Kalman filter. There is no assumption concerning the scoring model specification and no specific estimation method is presupposed. Thus, the estimator covariance is derived from the bootstrap. The focus is on a relationship between the score and the natural logarithm of the odds for that score, which is used to determine a customer’s propensity level. The propensity levels corresponding to the baseline and updated scores are compared. That comparison allows for monitoring whether the scorecard is still up-to-date in terms of assigning the odds. The presented technique is illustrated with an example of a propensity scorecard developed on the basis of credit bureau data.
propensity scorecard, scorecard monitoring, kalman filtering, bootstrap
0160-5682
29-37
Bijak, Katarzyna
5130b6b9-fbf1-44e8-9106-1dd69c6692a6
Bijak, Katarzyna
5130b6b9-fbf1-44e8-9106-1dd69c6692a6

Bijak, Katarzyna (2011) Kalman filtering as a performance monitoring technique for a propensity scorecard. Journal of the Operational Research Society, 62 (1), 29-37. (doi:10.1057/jors.2009.183).

Record type: Article

Abstract

Propensity scorecards allow forecasting, which bank customers would like to be granted new credits in the near future, through assessing their willingness to apply for new loans. Kalman filtering can help to monitor scorecard performance. Data from successive months are used to update the baseline model. The updated scorecard is the output of the Kalman filter. There is no assumption concerning the scoring model specification and no specific estimation method is presupposed. Thus, the estimator covariance is derived from the bootstrap. The focus is on a relationship between the score and the natural logarithm of the odds for that score, which is used to determine a customer’s propensity level. The propensity levels corresponding to the baseline and updated scores are compared. That comparison allows for monitoring whether the scorecard is still up-to-date in terms of assigning the odds. The presented technique is illustrated with an example of a propensity scorecard developed on the basis of credit bureau data.

Text
__soton.ac.uk_ude_personalfiles_users_khb1a08_mydocuments_JORS_JORS paper_Kalman filtering as a performance monitoring technique for a propensity scorecard.pdf - Accepted Manuscript
Download (221kB)

More information

e-pub ahead of print date: 3 February 2010
Published date: January 2011
Keywords: propensity scorecard, scorecard monitoring, kalman filtering, bootstrap
Organisations: Centre of Excellence for International Banking, Finance & Accounting

Identifiers

Local EPrints ID: 355114
URI: https://eprints.soton.ac.uk/id/eprint/355114
ISSN: 0160-5682
PURE UUID: 15f42dda-a40a-4c20-8610-baba2636e531

Catalogue record

Date deposited: 07 Aug 2013 10:57
Last modified: 19 Jul 2019 21:30

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×