Credit default prediction for micro-enterprise financing in India using ensemble models

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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CMARS and GAM & CQP—Modern optimization methods applied to international credit default prediction

Journal of Computational and Applied Mathematics ◽

10.1016/j.cam.2010.04.039 ◽

2011 ◽

Vol 235 (16) ◽

pp. 4639-4651 ◽

Cited By ~ 16

Author(s):

Özge Sezgin Alp ◽

Erkan Büyükbebeci ◽

Ayşegül İşcanog˜lu Çekiç ◽

Fatma Yerlikaya Özkurt ◽

Pakize Taylan ◽

...

Keyword(s):

Optimization Methods ◽

Default Prediction ◽

Credit Default ◽

International Credit ◽

Modern Optimization

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

Journal of Organizational and End User Computing ◽

10.4018/joeuc.20211101oa13 ◽

2021 ◽

Vol 33 (6) ◽

pp. 0-0

Keyword(s):

Consumer Credit ◽

Economic Transformation ◽

Data Sets ◽

Fraud Risk ◽

Multiple Data ◽

Default Prediction ◽

Credit Default ◽

Multiple Data Sets ◽

Credit Data ◽

Short Time

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.

Download Full-text