The nexus of corruption and non-performing loan: machine learning approach

Abstract

Banking institutions around the world are facing a serious problem with non-performing loans (NPLs), which can jeopardize their financial stability and hinder their ability to issue new loans. The issue of NPLs has been linked to corruption, which has emerged as one of the contributing factors. Given the scarcity of research on the use of machine learning (ML) techniques to examine the relationship between corruption and NPLs, this paper provides an empirical evaluation of various ML algorithms for predicting NPLs. Besides ML performance comparisons, this paper presents the analysis of ML features importance to justify the effect of corruption factor in the different ML algorithms for predicting NPLs. The results indicated that most of the tested ML algorithms present good ability in the prediction models at accuracy percentages above 70% but corruption index has contributed very minimal effect to the ML performances. The most outperformed ML algorithm in the different proposed settings is random forest. The framework of this research is highly reproducible to be extended with a more in-depth analysis, particularly on problems of NPL as well as on the ML algorithms.