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
29-37
Bijak, Katarzyna
5130b6b9-fbf1-44e8-9106-1dd69c6692a6
January 2011
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), .
(doi:10.1057/jors.2009.183).
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
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: http://eprints.soton.ac.uk/id/eprint/355114
ISSN: 0160-5682
PURE UUID: 15f42dda-a40a-4c20-8610-baba2636e531
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Date deposited: 07 Aug 2013 10:57
Last modified: 15 Mar 2024 03:36
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