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A dynamic scorecard for monitoring baseline performance with application to tracking a mortgage portfolio

Author

Listed:
  • J Whittaker

    (Lancaster University)

  • C Whitehead

    (Lancaster University)

  • M Somers

    (Scimetrics Ltd)

Abstract

A principled technique for monitoring the performance of a consumer credit scorecard through time is derived from Kalman filtering. Standard approaches sporadically compare certain characteristics of the new applicants with those predicted from the scorecard. The new approach systematically updates the scorecard combining new applicant information with the previous best estimate. The dynamically updated scorecard is tracked through time and compared to limits calculated by sequential simulation from the baseline scorecard. The observation equation of the Kalman filter is tailored to take the results of fitting local scorecards by logistic regression to batches of new clients that arrive in the current time interval. The states in the Kalman filter represent the true or underlying score for each attribute in the card: the parameters of the logistic regression. Their progress in time is modelled by a random walk and the filter provides the best estimate of the scores using past and present information. We illustrate the technique using a commercial mortgage portfolio and the results indicate significant emerging deficiencies in the baseline scorecard.

Suggested Citation

  • J Whittaker & C Whitehead & M Somers, 2007. "A dynamic scorecard for monitoring baseline performance with application to tracking a mortgage portfolio," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(7), pages 911-921, July.
    • Handle: RePEc:pal:jorsoc:v:58:y:2007:i:7:d:10.1057_palgrave.jors.2602226
    DOI: 10.1057/palgrave.jors.2602226
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    References listed on IDEAS

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    Cited by:

    1. T H Moon & S Y Sohn, 2010. "Technology credit scoring model considering both SME characteristics and economic conditions: The Korean case," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(4), pages 666-675, April.
    2. K Bijak, 2011. "Kalman filtering as a performance monitoring technique for a propensity scorecard," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(1), pages 29-37, January.

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