Original scientific paper
https://doi.org/10.1080/1331677X.2021.1997625
Implications of macroeconomic conditions on Romanian portfolio credit risk. A cost-sensitive ensemble learning methods comparison
Ana-Maria Sandica
Alexandra (Adam) Fratila
Abstract
Credit risk assessment represents a key instrument in the decision-making of the banking and financial institutions. In this article, we present a framework for credit risk strategies to improve
portfolio efficiency under a change of macroeconomic regime.
The aim is to compare the accuracy of several ensemble methods
(AdaBoost, Logit Boost, Gentle Boost and Random Forest) on a
default retail Romanian loan portfolio under different risk adversity scenarios, a priori and posteriori the financial distress. Using
cost-sensitive ensemble learning models, we concluded that
regime-based credit strategy can offer a better alternative in both
terms of loss allocated to the strategy as well as defaulters’ classification accuracy
Keywords
Credit policy; financial distress; risk aversion; boosting; random forest
Hrčak ID:
302613
URI
Publication date:
31.3.2023.
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