Recovery Risk: Application of the Latent Competing Risks Model to Non performing Loans

TL;DR

This paper introduces a latent competing risks model to estimate recovery probabilities and risks in non-performing loans, enhancing collection strategies by analyzing latent causes and timing of recoveries.

Contribution

It applies a novel competing risks model with Poisson and Weibull distributions to assess recovery risks and timing in non-performing loans, providing a new analytical tool.

Findings

Estimated recovery probabilities for different loan groups

Identified key factors influencing recovery likelihood

Demonstrated model effectiveness on real loan data

Abstract

This article proposes a method for measuring the latent risks involved in the recovery process of non performing loans in financial institutions and business firms that deal with collection and recovery processes. To that end, we apply the competing risks model referred to in the literature as the promotion time model. The result achieved is the probability of credit recovery for a portfolio segmented into groups based on the information available. Within the context of competing risks, application of the technique yielded an estimation of the number of latent events that concur to the credit recovery event. With these results in hand, we were able to compare groups of defaulters in terms of risk or susceptibility to the recovery event during the collection process, and thereby determine where collection actions are most efficient. We specify the Poisson distribution for the number of latent causes leading to recovery, and the Weibull distribution for the time up to recovery. To estimate the model parameters, we use the maximum likelihood method. Finally, the model was applied to a sample of defaulted loans from a financial institution.