Implications of macroeconomic conditions on Romanian portfolio credit risk. A cost-sensitive ensemble learning methods comparison

Ana-Maria Sandica and Alexandra Fratila (Adam)

Economic Research-Ekonomska Istraživanja, 2022, vol. 35, issue 1, 3571-3590

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.

Date: 2022
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DOI: 10.1080/1331677X.2021.1997625

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