Focusing on credit risk modelling, this paper introduces a novel approach for ensemble
modelling based on a normative linear pooling. Models are first classified as dominant and …

The paper introduces a novel approach to ensemble modeling as a weighted model
average technique. The proposed idea is prudent, simple to understand, and easy to …

Default prediction through probability of default modeling has attracted lots of research
interests in the past literature and recent studies have shown that Artificial Intelligence (AI) …

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to
predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 …

Credit risk assessment is a critical topic for finance activity and bankruptcy prediction that
has been broadly explored using statistical models and Machine Learning methods …

In this paper we introduce and discuss statistical models aimed at predicting default
probabilities of Small and Medium Enterprises (SME). Such models are based on two …

In credit risk modeling, banks and insurance companies routinely use a single model for
estimating key risk parameters. Combining several models to make a final prediction is not …

Academics and practitioners have studied over the years models for predicting firms
bankruptcy, using statistical and machine-learning approaches. An earlier sign that a …

Forecasting models based on machine learning (ML) algorithms have been shown to
outperform traditional models in several applications. The lack of an easily interpretable …

Purpose–The purpose of this paper is to assess and compare the forecast ability of existing
credit risk models, answering three questions: Can these methods adequately predict …