Publication

Dynamic Valuation of Delinquent Credit-Card Accounts

2015
Working Papers

Abstract

This paper introduces a dynamic model of the stochastic repayment behavior exhibited by delinquent credit-card accounts. Based on this model, we construct a dynamic collectability score (DCS) which estimates the account-speciﬁc probability of collecting a given portion of the outstanding debt over any given time horizon. The model integrates a variety of information sources, including historical repayment data, account-speciﬁc, and time-varying macroeconomic covariates, as well as scheduled account-treatment actions. Two model-identiﬁcation methods are examined, based on maximum-likelihood estimation and the generalized method of moments. The latter allows for an operational-statistics approach, combining model estimation and performance optimization by tailoring the estimation error to business-relevant loss functions. The DCS framework is applied to a large set of account-level repayment data. The improvements in classiﬁcation and prediction performance compared to standard bank-internal scoring methods are found to be signiﬁcant.

Official source

https://infoscience.epfl.ch/entities/publication/aaa074a3-d0da-4d92-afff-eb9fe614a31d

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2015
Working Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/aaa074a3-d0da-4d92-afff-eb9fe614a31d

About this result

Related concepts (32)

Maximum likelihood estimation

In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data. This is achieved by maximizing a likelihood function so that, under the assumed statistical model, the observed data is most probable. The point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate. The logic of maximum likelihood is both intuitive and flexible, and as such the method has become a dominant means of statistical inference.

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Foundations of statistics

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