Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - A dynamic scorecard for monitoring baseline performance with application to tracking a mortgage portfolio.
AU - Whittaker, Joseph
AU - Somers, M.
AU - Whitehead, C.
N1 - RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research
PY - 2007/7/1
Y1 - 2007/7/1
N2 - 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.
AB - 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.
KW - credit scoring
KW - Gini coefficient
KW - Kalman filter
KW - logistic regression
KW - mortgage scoring
KW - scorecard diagnostics
U2 - 10.1057/palgrave.jors.2602226
DO - 10.1057/palgrave.jors.2602226
M3 - Journal article
VL - 58
SP - 911
EP - 921
JO - Journal of the Operational Research Society
JF - Journal of the Operational Research Society
SN - 1476-9360
IS - 7
ER -