WT Model &amp; Applications in Loan Platform Customer Default Prediction Based on Decision Tree Algorithms

Pang, Shaoning; Yuan, Jinmeng

doi:10.1007/978-3-319-95930-6_33

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…The traditional data mining models also have the advantages of simple structure and quick decision-making (Galindo & Tamayo, 2000;Wang et al, 2007). Most credit default models are based on these models or are improved in different ways (Pang & Yuan, 2018). However, with the development of support vector machines and ensemble learning methods, the performance of traditional methods in credit default prediction is not as good as that of the expanded models and methods (Li et al, 2018;Qianmu, Yanjun, Jing et al, 2020;Sharif et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Cascading Model for E-Commerce Consumer Credit Default Prediction

Hou

Liu

et al. 2021

Journal of Organizational and End User Computing

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As an important global policy guide to promote economic transformation and upgrading, the upsurge of E-Commerce has been continuously upgraded with continuous breakthroughs in information technology. In recent years, China’s e-commerce consumer credit has developed well, but due to its short time of production and insufficient experience for reference, credit risk, fraud risk, and regulatory risk continue to emerge. Aiming at the problem of E-Commerce Consumer Credit default analysis, this paper proposes a Fusion Enhanced Cascade Model (FECM). This model learns feature data of credit data by fusing multi-granularity modules, and incorporates random forest and GBDT trade-off variance and bias methods. The paper compares FECM and gcForest on multiple data sets, to prove the applicability of FECM in the field of E-commerce credit default prediction. The research results of this paper are helpful to the risk control of financial development, and to construct a relatively stable financial space for promoting the construction and development of E-Commerce.

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