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.
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Acknowledgements
I am grateful to an anonymous referee for suggestions and comments that helped improve this paper. I would also like to say thank you to Sophie N'Jai for proof-reading the draft.
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Bijak, K. Kalman filtering as a performance monitoring technique for a propensity scorecard. J Oper Res Soc 62, 29–37 (2011). https://doi.org/10.1057/jors.2009.183
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DOI: https://doi.org/10.1057/jors.2009.183