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Two-stage consumer credit risk modelling using heterogeneous ensemble learning

ČlánekOmezený přístuppeer-reviewedpostprint
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Datum publikování

2019

Vedoucí práce

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Vydavatel

Elsevier Science BV

Abstrakt

Modelling consumer credit risk is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. To model the overall credit risk of a consumer loan in terms of expected loss (EL), three key credit risk parameters must be estimated: probability of default (PD), loss given default (LGD) and exposure at default (EAD). Research to date has tended to model these parameters separately. Moreover, a neglected area in the field of LGD/EAD modelling is the application of ensemble learning, which by benefitting from diverse base learners reduces the over-fitting problem and enables modelling diverse risk profiles of defaulted loans. To overcome these problems, this paper proposes a two-stage credit risk model that integrates (1) class-imbalanced ensemble learning for predicting PD (credit scoring), and (2) an EAD prediction using a regression ensemble. Furthermore, multi-objective evolutionary feature selection is used to minimize both the misclassification cost (root mean squared error) of the PD and EAD models and the number of attributes necessary for modelling. For this task, we propose a misclassification cost metric suitable for consumer loans with fixed exposure because it combines opportunity cost and LGD. We show that the proposed credit risk model is not only more effective than single-stage credit risk models but also outperforms state-of-the-art methods used to model credit risk in terms of prediction and economic performance.

Rozsah stran

p. 33-45

ISSN

0167-9236

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Projekt

GA16-19590S/Analýza témat a sentimentu vícenásobných textových zdrojů pro finanční rozhodování

Zdrojový dokument

Decision Support Systems, volume 118, issue: March

Vydavatelská verze

https://www.sciencedirect.com/science/article/pii/S0167923619300028

Přístup k e-verzi

Text článku ve verzi postprint bude přístupný od 09.01.2021

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Klíčová slova

Credit risk, Ensemble learning, Credit scoring, Expected loss, Exposure at default, úvěrové riziko, meta-učení, úvěrové skórování, očekávaná ztráta, expozice při selhání

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