Monetary loss surveillance for credit models

There is a vast collection of statistical methodologies devoted tomeasure the customer's credit risk. Well-known statistical techniques are logistic regression, genetic algorithms, and support vector machines, among others. However, there is a lack of statistical tools for monitoring monetary l...

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Bibliographic Details
Published inSequential analysis Vol. 35; no. 3; pp. 347 - 357
Main Authors Silva, Ivair R., Barros, Vincius B. M.
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 02.07.2016
Taylor & Francis Ltd
Subjects
Collection
Exact solutions
Hypothesis testing
Logistics
Mathematical analysis
Monitoring
Regression
Risk management
sequential analysis
Support vector machines
Surveillance
Online AccessGet full text
ISSN0747-4946
1532-4176
DOI10.1080/07474946.2016.1206379

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Summary:There is a vast collection of statistical methodologies devoted tomeasure the customer's credit risk. Well-known statistical techniques are logistic regression, genetic algorithms, and support vector machines, among others. However, there is a lack of statistical tools for monitoring monetary losses implied by a given credit model in operation. This article introduces a sequential procedure to favor such monitoring. Our method favors early detection of increased expected monetary losses. Analytical expressions are derived for the calculation of the statistical power performance of the proposed method. An application for a credit portfolio of a German bank is offered.
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ISSN:0747-4946
1532-4176
DOI:10.1080/07474946.2016.1206379