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

2022 ◽  
Vol 26 (1) ◽  
pp. 84
Author(s):  
Pankaj Kumar Gupta ◽  
K.K. Jain
Keyword(s):  
Ensemble Models ◽  
Default Prediction ◽  
Credit Default

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

2021 ◽  
Vol 33 (6) ◽  
pp. 1-18
Author(s):  
Jun Hou ◽  
Qianmu Li ◽  
Yaozong Liu ◽  
Sainan Zhang
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.

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

2011 ◽  
Vol 235 (16) ◽  
pp. 4639-4651 ◽  
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

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

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.

An alternative statistical framework for credit default prediction

2020 ◽  
Author(s):  
Mohammad Shamsu Uddin ◽  
Guotai Chi ◽  
Tabassum Habib ◽  
Ying Zhou
Keyword(s):  
Statistical Framework ◽  
Default Prediction ◽  
Credit Default

scholarly journals Credit default prediction and parabolic potential theory

2017 ◽  
Vol 124 ◽  
pp. 121-125 ◽  
Author(s):  
Matteo L. Bedini ◽  
Michael Hinz
Keyword(s):  
Potential Theory ◽  
Parabolic Potential ◽  
Default Prediction ◽  
Credit Default

Credit default prediction modeling: an application of support vector machine

2017 ◽  
Vol 19 (2) ◽  
pp. 158-187 ◽  
Author(s):  
Fahmida E. Moula ◽  
Chi Guotai ◽  
Mohammad Zoynul Abedin
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Prediction Modeling ◽  
Default Prediction ◽  
Credit Default
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