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Two-stage consumer credit risk modelling using heterogeneous ensemble learning
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2019 | Decision Support Systems | Citations: 0
Authors: Papouskova, Monika; Hajek, Petr

Abstract: Modelling consumer credit risk is a crucial task for banks and non-bank financia ... Expand

Abstract: 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. Collapse

Semantic filters: genetic optimization mean squared error

Topics: peer-to-peer model enterprise information system missing data

Methods: computational algorithm ensemble learning meta learning machine learning support vector machine
Learning experiments with genetic optimization of a generalized regression neural network
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1996 | Decision Support Systems | Citations: 1
Authors: Hansen, James V.; Meservy, Rayman D.

Abstract: This paper reports a study unifying optimization by genetic algorithm with a gen ... Expand

Abstract: This paper reports a study unifying optimization by genetic algorithm with a generalized regression neural network. Experiments compare hill-climbing optimization with that of a genetic algorithm, both in conjunction with a generalized regression neural network. Controlled data with nine independent variables are used in combination with conjunctive and compensatory decision forms, having zero percent and 10 percent noise levels. Results consistently favor the GRNN unified with the genetic algorithm. Collapse

Semantic filters: genetic optimization mean squared error

Topics: artificial intelligence

Methods: computational algorithm experiment artificial neural network simulation optimization model

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Two-stage consumer credit risk modelling using heterogeneous ensemble learning

Learning experiments with genetic optimization of a generalized regression neural network