A Dynamic Credit Scoring Model Based on Survival Gradient Boosting Decision Tree Approach

Xia, Yufei; He, Lingyun; Li, Yinguo; Fu, Yating; Xu, Yixin

doi:10.3846/tede.2020.13997

Cited by 32 publications

(8 citation statements)

References 55 publications

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“…Xia Y and other scholars proposed a SurvXGBoost model based on the Gradient Boosting Decision Tree (GBDT) method, and applied it to the consumer loan dataset. The performance is excellent [11].…”

Section: Related Workmentioning

confidence: 99%

Research on Intellectual Dichotomiser 3 Decision Tree Algorithm Model for Financial Analysis of Colleges and Universities

Guo¹

2022

IJACSA

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The rapid development of college information construction has promoted the processing and analysis of a large number of data in the college system. Decision tree algorithm is often used in the field of financial data analysis, but it has a bias in the selection of attributes. Aiming at the defects of the decision tree algorithm in attribute selection, ID3 algorithm in the decision tree algorithm is selected for weighted improvement, and it is optimized based on Synthetic Minority Oversampling Technique (SMOTE) algorithm and Bagging algorithm to balance the positive and negative data of its training samples, thus obtaining the DSB-ID3 financial analysis model. Using this model to analyze the financial data of a university, its G value and F value are both about 0.78, the recognition accuracy rate for normal samples is 0.7345, and the total recognition accuracy rate is 0.7893, which are the highest among the four models. Compared with other models, model designed in this study has significantly improved classification performance, and its distribution is the most centralized, showing superior stability.The experimental results show that the classification effect of model designed in this study is the best, and it shows superior accuracy and stability in the analysis of financial data. Its superior classification performance shows the potential of decision tree algorithm optimization and the feasibility and necessity of improving it. From the experimental data, it can be seen that the service life and parameters of the model designed in this study are obviously better than those commonly used in the financial analysis industry of colleges and universities. It can be seen from the overall analysis that this model provides a practical reference for the application of decision tree optimization in college financial analysis, and greatly improves the accuracy of financial system data analysis.

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Section: Related Workmentioning

confidence: 99%

Research on Intellectual Dichotomiser 3 Decision Tree Algorithm Model for Financial Analysis of Colleges and Universities

Guo¹

2022

IJACSA

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“…Ensemble credit scoring models are mainly established by bagging, boosting or stacking algorithms (He et al, 2018;Xia et al, 2018a). Bagging artificial neural network (Tsai & Wu, 2008), random forests (RF) (Tang et al, 2019), GBDT (Xia et al, 2021a;Xia et al, 2017b), and heterogeneous ensemble models (Schotten & Morais, 2019) are typical applications of ensemble credit scoring models. The empirical results also demonstrate the advantages of ensemble models (Lessmann et al, 2015).…”

Section: Modelling Approaches Of Credit Scoringmentioning

confidence: 99%

From Credit Scoring to Regulatory Scoring: Comparing Credit Scoring Models From a Regulatory Perspective

Xia

Liao

et al. 2022

Technological and Economic Development of Economy

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Conventional credit scoring models evaluated by predictive accuracy or profitability typically serve the financial institutions and can hardly reflect their contribution on financial stability. To remedy this, we develop a novel regulatory scoring framework to quantify and compare the corresponding regulatory capital charge errors of credit scoring models. As an application of RegTech, the proposed framework considers the characteristic of example-dependence and costsensitivity in credit scoring, which is expected to enhance the ability of risk absorption of financial institutions and thus benefit the regulators. Validated on two real-world credit datasets, empirical results reveal that credit scoring models with good predictive accuracy or profitability do not necessarily provide low capital charge requirement error, which further highlights the importance of regulatory scoring framework. The family of gradient boosting decision tree (GBDT) provides significantly better average performance than industry benchmarks and deep multilayer perceptron network, especially when financial stability is the primary focus. To further examine the robustness of the proposed regulatory scoring, sampling techniques, cut-off value modification, and probability calibration are employed within the framework and the main conclusions hold in most cases. Furthermore, the analysis on the interpretability via TreeSHAP algorithm alleviates the concerns on transparency of GBDT-based models, and confirms the important roles of loan characteristics, borrowers’ solvency and creditworthiness as powerful predictors in credit scoring. Finally, the managerial implications for both financial institutions and regulators are discussed.

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“…They concluded that the XGBoost with DNN provides comparatively better performance relative to certain other widely used techniques and methodologies. Based on the survival gradient boosting decision tree (GBDT) approach, Xia et al 10 proposed a novel dynamic credit scoring model, SurvXGBoost. The authors combined the GBDT and survival analysis approach.…”

Section: Background and Previous Workmentioning

confidence: 99%

A Gaussian process‐based approach toward credit risk modeling using stationary activations

Mahajan

Nayyar

Raina

et al. 2021

Concurrency and Computation

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The task of predicting the risk of defaulting of a lender using tools in the domain of AI is an emerging one and in growing demand, given the revolutionary potential of AI. Various attributes like income, properties acquired, educational status, and many other socioeconomic factors can be used to train a model to predict the possibilities of nonrepayment of a loan or its chances. Most of the techniques and algorithms used in this regard previously do not submit any attention to the uncertainty in predictions for out of distribution (OOD) in a dataset, which contributes to overfitting, leading to relatively lower accuracy for predicting these data points. Specifically, for credit risk classification, this is a serious concern, given the structure of the available datasets and the trend they follow. With a focus on this issue, we propose a robust and better methodology that uses a recent and efficient family of nonlinear neural network activation functions, which mimics the properties induced by the widely‐used Matérn family of kernels in Gaussian process (GP) models. We tested the classification performance metrics on three openly available datasets after prior preprocessing. We achieved a high mean classification accuracy of 87.4% and a lower mean negative log predictive density loss of 0.405.

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A Dynamic Credit Scoring Model Based on Survival Gradient Boosting Decision Tree Approach

Cited by 32 publications

References 55 publications

Research on Intellectual Dichotomiser 3 Decision Tree Algorithm Model for Financial Analysis of Colleges and Universities

Research on Intellectual Dichotomiser 3 Decision Tree Algorithm Model for Financial Analysis of Colleges and Universities

From Credit Scoring to Regulatory Scoring: Comparing Credit Scoring Models From a Regulatory Perspective

A Gaussian process‐based approach toward credit risk modeling using stationary activations

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