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Your search keyword '"Credit risk modeling, Bayesian optimization, XGBoost, cost sensitive learning"' showing total 2 results
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2 results on '"Credit risk modeling, Bayesian optimization, XGBoost, cost sensitive learning"'

1. Statistical learning for credit risk modelling.

Author

Perna, C, Salvati, N, Schirripa Spagnolo, F, Bacino, V, Zoccarato, A, Liberati, C, Borrotti, M, Bacino,V, Perna, C, Salvati, N, Schirripa Spagnolo, F, Bacino, V, Zoccarato, A, Liberati, C, Borrotti, M, and Bacino,V

Abstract

The objective of credit scoring is to develop accurate rule of classification that aids to distinguish between good and bad clients. In this context, also Statistical Learning (SL) techniques have been explored, for building models that estimate the clients’ probability of insolvency. Although there are some encouraging results in literature, two main issues makes this classification task very hard: (i) high imbalance ratio between the two groups in the target variable and (ii) the effect of hyperparameter settings on overall performance. In this work, Bayesian Optimization (BO) is used to optimize the hyperparameters of a cost sensitive eXtreme Gradient Boosting (XGBoost) model. Experimental results reveal that the proposed solution is a promising starting point for future development

Published
2021

2. Statistical learning for credit risk modelling

Author

Bacino,V, Zoccarato, A, Liberati, C, Borrotti, M, Perna, C, Salvati, N, Schirripa Spagnolo, F, Bacino, V, Zoccarato, A, Liberati, C, and Borrotti, M

Subjects
SECS-S/03 - STATISTICA ECONOMICA, SECS-S/01 - STATISTICA, Credit risk modeling, Bayesian optimization, XGBoost, cost sensitive learning
Abstract

The objective of credit scoring is to develop accurate rule of classification that aids to distinguish between good and bad clients. In this context, also Statistical Learning (SL) techniques have been explored, for building models that estimate the clients’ probability of insolvency. Although there are some encouraging results in literature, two main issues makes this classification task very hard: (i) high imbalance ratio between the two groups in the target variable and (ii) the effect of hyperparameter settings on overall performance. In this work, Bayesian Optimization (BO) is used to optimize the hyperparameters of a cost sensitive eXtreme Gradient Boosting (XGBoost) model. Experimental results reveal that the proposed solution is a promising starting point for future development

Published
2021

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