A systematic review of machine learning models applied in debt collection operations Uma revisão sistemática de modelos de machine learning aplicados em operações financeiras de cobranças de dívidas

Martins J.A.; Vallim-Filho A.R.A.

A systematic review of machine learning models applied in debt collection operations Uma revisão sistemática de modelos de machine learning aplicados em operações financeiras de cobranças de dívidas

Tipo

Artigo

Data de publicação

2024

Periódico

RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao

Citações (Scopus)

0

Autores

Martins J.A.
Vallim-Filho A.R.A.

Resumo

© 2024, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.Brazil is facing high default rates, due in part to the pandemic, leading to the search for new debt collection strategies. Machine Learning (ML), successfully used in numerous areas, is an ally to increase the effectiveness of these operations. This article seeks to present a current overview of research on ML applications in debt collection operations, through a Systematic Literature Review. The PICO methodology was used, initially identifying 41 documents, of which 11 underwent systematic review. The results showed four objectives pursued by the studies: default prediction, personalization of collection strategies, optimization of debt recovery actions and credit recovery prediction. And the main algorithms used were Decision Tree, Logistic Regression, Random Forest, Naive Bayes, Artificial Neural Network and Deep Learning. The results revealed that ML is still little explored in this area, offering potential for substantial research advances.