Zero-adjusted defective regression models applied for modeling credit risk data
- Biometrics & Biostatistics International Journal
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Cleide Mayra Menezes,1
Crystiane Fernanda
de Souza,2
Lima Vera Lucia Damasceno
Tomazella2
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Abstract
As the consumption of goods, services, and granting of credit increase, it becomes
necessary to control the risk of the process. This measure aims to avoid possible defaults
greater than what financial institutions can support while allowing for profit generation.
Various statistical techniques can be used to build models that present the risk panorama,
one of which is survival analysis. The application of this technique in the financial market
seeks to study, for example, the time it takes for an individual to recover credit after the
end of a financial crisis in their country. The use of such data can support the prediction
of the ideal amount of credit to be provisioned in possible crisis scenarios and infer when
the resumption of credit operations may occur. In this context, this work aims to study
two defective regression models for modeling zero-adjusted survival data in the credit
risk scenario. This approach accommodates three types of units: customers with “zero”
survival times, that is, early failures, customers susceptible, and not susceptible to the event
of interest. The methodology studied will be applied to a database provided by a leading
institution in credit services and information in Brazil.
Keywords
survival analysis, financial data, credit risk, cure fraction, defective distribution, zero-adjusted
