The zero-inflated promotion cure rate regression model applied to fraud propensity in bank loan applications

TL;DR

This paper introduces a zero-inflated promotion cure rate regression model to better analyze fraud propensity in bank loan applications, accounting for excess zeros often seen in banking data.

Contribution

It extends the promotion cure rate model by incorporating zero-inflation, enabling covariate analysis of the zero fraction in loan fraud data.

Findings

Model fits real banking fraud data effectively

Zero-inflated model improves understanding of fraud propensity

Results compare favorably with previous methods

Abstract

In this paper we extend the promotion cure rate model proposed by Chen et al (1999), by incorporating excess of zeros in the modelling. Despite allowing to relate the covariates to the fraction of cure, the current approach, which is based on a biological interpretation of the causes that trigger the event of interest, does not enable to relate the covariates to the fraction of zeros. The presence of zeros in survival data, unusual in medical studies, can frequently occur in banking loan portfolios, as presented in Louzada et al (2015), where they deal with propensity to fraud in lending loans in a major Brazilian bank. To illustrate the new cure rate survival method, the same real dataset analyzed in Louzada et al (2015) is fitted here, and the results are compared.