Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects

Dumitrescu, Elena Ivona; Hué, Sullivan; Hurlin, Christophe; Tokpavi, Sessi

doi:10.1016/j.ejor.2021.06.053

Cited by 224 publications

(92 citation statements)

References 53 publications

(62 reference statements)

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“…The results of the A-LASSO model are even worse as it selects a small number variables compared to LASSO and AM models. In line with the findings of Dumitrescu et al (2022), our results suggest that these parametric functions of the raw data are not flexible enough to capture the non-linearity of this data. In contrast, nonparametric functions of r7s GAM accurately capture non-linearities, with the MSE of GAM being closer to that of sophisticated ML algorithms than linear models.…”

Section: Regression Problem: Boston Housing Marketsupporting

confidence: 78%

“…We also implement r11s random forest and r12s XGBoost algorithms and consider these models as benchmarks to evaluate the predictive performance of the other approaches. Finally, we compare the GAM(L)A model to the r13s penalized logistic tree regression model (PLTR) of Dumitrescu et al (2022). 20 Like GAM(L)A, the PLTR is also intended to improve the predictive performance of traditional linear models by automatically capturing non-linearities.…”

Section: Regression Problem: Boston Housing Marketmentioning

confidence: 99%

“…While GAM(L)A is based on GAM non-parametric functions and interaction effects, the PLTR relies on univariate and bivariate threshold effects obtained from short-depth decision trees. See Appendix B for more details on the PLTR model of Dumitrescu et al (2022).…”

Section: Regression Problem: Boston Housing Marketmentioning

confidence: 99%

“…Finally, we conclude the article in Section 5. Additional figures and tables are provided in Appendix A while Appendix B presents the penalized logistic tree regression of Dumitrescu et al (2022).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

GAM(L)A: An econometric model for interpretable Machine Learning

Flachaire¹,

Hacheme²,

Hué³

et al. 2022

Preprint

Self Cite

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Despite their high predictive performance, random forest and gradient boosting are often considered as black boxes or uninterpretable models which has raised concerns from practitioners and regulators. As an alternative, we propose in this paper to use partial linear models that are inherently interpretable. Specifically, this article introduces GAM-lasso (GAMLA) and GAM-autometrics (GAMA), denoted as GAM(L)A in short. GAM(L)A combines parametric and non-parametric functions to accurately capture linearities and non-linearities prevailing between dependent and explanatory variables, and a variable selection procedure to control for overfitting issues. Estimation relies on a two-step procedure building upon the double residual method. We illustrate the predictive performance and interpretability of GAM(L)A on a regression and a classification problem. The results show that GAM(L)A outperforms parametric models augmented by quadratic, cubic and interaction effects. Moreover, the results also suggest that the performance of GAM(L)A is not significantly different from that of random forest and gradient boosting.

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Section: Regression Problem: Boston Housing Marketsupporting

confidence: 78%

Section: Regression Problem: Boston Housing Marketmentioning

confidence: 99%

Section: Regression Problem: Boston Housing Marketmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

GAM(L)A: An econometric model for interpretable Machine Learning

Flachaire¹,

Hacheme²,

Hué³

et al. 2022

Preprint

Self Cite

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show abstract

“…Dumitrescu et al. ( 2022 ) presented penalised logistic tree regression (PLTR), a high-performance and interpretable credit scoring system that utilises information from decision trees to boost the effectiveness of logistic regression. Yang et al ( 2021 ) proposed a credit scoring model construction method that combines memetic optimization algorithm and neural architecture search to achieve efficient search of credit scoring networks.…”

Section: Related Workmentioning

confidence: 99%

BACS: blockchain and AutoML-based technology for efficient credit scoring classification

Yang

Qiao

et al. 2022

Ann Oper Res

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Credit evaluation is of high scientific significance and practical use, especially in today’s plight of the world suffering from the COVID-19 epidemic. However, due to the difficulties inherent in credit scoring model building which involves a large number of data mining steps and requires a lot of time to process the data and build the model, efficient and accurate credit scoring methods are are urgently required. Aiming to solve this problem, we propose BACS, an blockchain and automated machine learning based classification model using credit dataset so that the credit modelling processes are performed in the pipeline in an automated manner to eventually obtain the classification results of credit scoring. BACS scheme consists of credit data storage to blockchain, feature extraction, feature selection, modelling algorithm and hyperparameter optimization, and model evaluation. Firstly, we propose a mechanism for credit data management and storage using blockchain to ensure that the entire credit scoring system is traceable and that the information of each scoring candidate is securely, efficiently and tamper-proofly stored on the blockchain nodes. Next, we design a pipeline using a random forest model to effectively integrate the key steps of credit data feature extraction, feature selection, credit model construction, and model evaluation. The experimental results demonstrate that our proposed automated machine learning-based credit scoring classification scheme BACS can assess the credit condition efficiently and accurately.

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A submodular optimization approach to trustworthy loan approval automation

Lee,

Yoo,

Shin

et al. 2024

AI Magazine

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In the field of finance, the underwriting process is an essential step in evaluating every loan application. During this stage, the borrowers' creditworthiness and ability to repay the loan are assessed to ultimately decide whether to approve the loan application. One of the core components of underwriting is credit scoring, in which the probability of default is estimated. As such, there has been significant progress in enhancing the predictive accuracy of credit scoring models through the use of machine learning, but there still exists a need to ultimately construct an approval rule that takes into consideration additional criteria beyond the score itself. This construction process is traditionally done manually to ensure that the approval rule remains interpretable to humans. In this paper, we outline an automated system for optimizing a rule‐based system for approving loan applications, which has been deployed at Hyundai Capital Services (HCS). The main challenge lays in creating a high‐quality rule base that is simultaneously simple enough to be interpretable by risk analysts as well as customers, since the approval decision should be easily understandable. We addressed this challenge through principled submodular optimization. The deployment of our system has led to a 14% annual growth in the volume of loan services at HCS, while maintaining the target bad rate, and has resulted in the approval of customers who might have otherwise been rejected.

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Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects

Cited by 224 publications

References 53 publications

GAM(L)A: An econometric model for interpretable Machine Learning

GAM(L)A: An econometric model for interpretable Machine Learning

BACS: blockchain and AutoML-based technology for efficient credit scoring classification

A submodular optimization approach to trustworthy loan approval automation

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