The zero-inflated cure rate regression model: Applications to fraud detection in bank loan portfolios

TL;DR

This paper presents a zero-inflated cure rate regression model for fraud detection in bank loans, effectively distinguishing between fraudsters, default-prone, and non-prone applicants, with demonstrated application on real data.

Contribution

It introduces a novel zero-inflated cure rate model that handles zero-inflated times and classifies different applicant types, enhancing fraud detection capabilities.

Findings

Successfully applied to real loan data with the zero-inflated Weibull model

Parameter estimation via maximum likelihood and Monte Carlo simulations

Improved differentiation of applicant types in loan portfolios

Abstract

In this paper, we introduce a methodology based on the zero-inflated cure rate model to detect fraudsters in bank loan applications. In fact, our approach enables us to accommodate three different types of loan applicants, i.e., fraudsters, those who are susceptible to default and finally, those who are not susceptible to default. An advantage of our approach is to accommodate zero-inflated times, which is not possible in the standard cure rate model. To illustrate the proposed method, a real dataset of loan survival times is fitted by the zero-inflated Weibull cure rate model. The parameter estimation is reached by maximum likelihood estimation procedure and Monte Carlo simulations are carried out to check its finite sample performance.