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The literature suggests that the commonly used augmentation method of reject inference achieves no appreciable benefit in the context of logistic and probit regression models. Ranking is not improved and the ability to discern a correct cut-off is undermined. This paper considers the application of augmentation to profit scoring applicants by means of survival analysis and by the Cox proportional hazard model, in particular. This new context involves more elaborate models answering more specific questions such as when will default occur and what will be its precise financial implication. Also considered in this paper is the extent to which the rejection rate is critical in the potential usefulness of reject inference and how augmentation meets that potential. The conclusion is essentially that augmentation achieves negative benefits only and that the scope for reject inference in this context pertains mainly to circumstances where a high proportion of applicants have been rejected. Journal of the Operational Research Society (2010) 61, 473-485. doi: 10.1057/jors.2008.180 Published online 4 March 2009 Keywords: credit scoring; reject inference; augmentation; survival analysis; proportional hazard

We use response data collected by a lender to estimate the probabilities of loan offers being accepted by the applicants and the survival probabilities of default and of paying back early. Combining all those together we estimated the expected profit surface for the lender at the time of application before making an offer to an applicant. The results show how a lender could find the optimal interest rate to increase the expected profit or its market share. We also consider how different optimal decision policies could be applied to different market segments. Journal of the Operational Research Society (2010) 61, 430-442. doi: 10.1057/jors.2009.33 Published online 1 April 2009 Keywords: profitability; acceptance; credit; take-up

Survival analysis can be applied to build models for time to default on debt. In this paper, we report an application of survival analysis to model default on a large data set of credit card accounts. We explore the hypothesis that probability of default (PD) is affected by general conditions in the economy over time. These macroeconomic variables (MVs) cannot readily be included in logistic regression models. However, survival analysis provides a framework for their inclusion as time-varying covariates. Various MVs, such as interest rate and unemployment rate, are included in the analysis. We show that inclusion of these indicators improves model fit and affects PD yielding a modest improvement in predictions of default on an independent test set. doi:10.1057/jors.2008.130 Keywords: credit scoring; survival analysis; time-varying covariates; risk; banking: macroeconomic variables

If a credit scoring model is built using only applicants who have been previously accepted for credit such a non-random sample selection may produce bias in the estimated model parameters and accordingly the model's predictions of repayment performance may not be optimal. Previous empirical research suggests that omission of rejected applicants has a detrimental impact on model estimation and prediction. This paper explores the extent to which, given the previous cutoff score applied to decide on accepted applicants, the number of included variables influences the efficacy of a commonly used reject inference technique, reweighting. The analysis benefits from the availability of a rare sample, where virtually no applicant was denied credit. The general indication is that the efficacy of reject inference is little influenced by either model leanness or interaction between model leanness and the rejection rate that determined the sample. However, there remains some hint that very lean models may benefit from reject inference where modelling is conducted on data characterized by a very high rate of applicant rejection. Journal of the Operational Research Society (2005) 56, 1072-1081. doi:10.1057/palgrave.jors.2602017 Published online 8 June 2005 Keywords: reject inference; credit scoring; augmentation

One of the aims of credit scoring models is to predict the probability of repayment of any applicant and yet such models are usually parameterised using a sample of accepted applicants only. This may lead to biased estimates of the parameters. In this paper we examine two issues. First, we compare the classification accuracy of a model based only on accepted applicants, relative to one based on a sample of all applicants. We find only a minimal difference, given the cutoff scores for the old model used by the data supplier. Using a simulated model we examine the predictive performance of models estimated from bands of applicants, ranked by predicted creditworthiness. We find that the lower the risk band of the training sample, the less accurate the predictions for all applicants. We also find that the lower the risk band of the training sample, the greater the overestimate of the true performance of the model, when tested on a sample of applicants within the same risk band--as a financial institution would do. The overestimation may be very large. Second, we examine the predictive accuracy of a bivariate probit model with selection (BVP). This parameterises the accept-reject model allowing for (unknown) omitted variables to be correlated with those of the original good-bad model. The BVP model may improve accuracy if the loan officer has overridden a scoring rule. We find that a small improvement when using the BVP model is sometimes possible. Keywords: credit scoring; reject inference; sample selection

Coventry Patmore's architectural criticism is often unjustly overlooked. Patmore understood the best architecture uses gravity to aesthetic advantage, and rejected the concept of decoration as either an autonomous form or as structural expression. Patmore felt Egyptian, Greek, and Gothic styles of architecture were the only ones to properly utilize the material, rational, and spiritual purposes of structures.

Laurence Ward THE LONDON COUNTY COUNCIL BOMB DAMAGE MAPS 1939-1945 288pp. Thames and Hudson. 48 [pounds sterling]. 978 0 500 51825 4 Sunday morning, September 8,1940. Ivan Maisky, Soviet ambassador [...]

Clive Aslet AN EXUBERANT CATALOGUE OF DREAMS The Americans who revived the country house in Britain 208pp. Aurum. 35 [pounds sterling] (US $55). 978 1 78131 094 6 James Peill [...]

This paper aims to discover whether the predictive accuracy of a new applicant scoring model for a credit card can be improved by estimating separate scoring models for applicants who are predicted to have high or low usage of the card. Two models are estimated. First we estimate a model to explain the desired usage of a card, and second we estimate separately two further scoring models, one for those applicants whose usage is predicted to be high, and one for those for whom it is predicted to be low. The desired usage model is a two-stage Heckman model to take into account the fact that the observed usage of accepted applicants is constrained by their credit limit. Thus a model of the determinants of the credit limit, and one of usage, are both estimated using Heckman's ML estimator. We find a large number of variables to be correlated with desired usage. We also find that the two stage scoring methodology gives only very marginal improvements over a single stage scoring model, that we are able to predict a greater percentage of bad payers for low users than for high users and a greater percentage of good payers for high users than for low users. Keywords: credit scoring; Heckman; selection model

James W. P. Campbell THE LIBRARY A world history 320pp. Thames and Hudson. 48 [pounds sterling]. 978 0 500 342 886 'Will this study serve merely as a memorial to [...]

Philip Davies LONDON Hidden interiors Photography by Derek Kendall 448pp. Atlantic. 40 [pounds sterling]. 978 0 9568642 4 6 As long ago as 1841, a slim quarto volume entitled London [...]

Caroline Shenton THE DAY PARLIAMENT BURNED DOWN 333pp. Oxford University Press. 18.99 [pounds sterling] (US $35). 978 019 964670 8 One night in October 1834, the ancient Palace of Westminster--home [...]

Merlin Waterson A NOBLE THING The National Trust and its benefactors 227pp. Scala. 39.95 [pounds sterling]. 978 1 85759 669 4 John Martin Robinson FELLING THE ANCIENT OAKS How England [...]

Strawberry Hill house in the UK was originally designed by Horace Walpole in the Victorian Gothic design, with modern extensions having retained the original style. Walpole is attributed with being pivotal to the Gothic Revival movement. The house has been owned by St Mary's University College since 1925 and has been adequately maintained, although the garden requires restoration. The building will ultimately be run by English Heritage, which could lead to the recovery of some of its contents.