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

Multicriterial Optimization of Technical Systems Considering Multiple Load and Availability Scenarios

2015 ◽  
Vol 807 ◽  
pp. 247-256 ◽  
Author(s):  
Lena C. Altherr ◽  
Thorsten Ederer ◽  
Philipp Pöttgen ◽  
Ulf Lorenz ◽  
Peter F. Pelz
Keyword(s):  
System Design ◽  
Cost Effective ◽  
Optimization Methods ◽  
Technical System ◽  
Multicriterial Optimization ◽  
Fluid Systems ◽  
Rule Number ◽  
Technical Systems ◽  
Multiple Load ◽  
Modern Optimization

Cheap does not imply cost-effective -- this is rule number one of zeitgeisty system design. The initial investment accounts only for a small portion of the lifecycle costs of a technical system. In fluid systems, about ninety percent of the total costs are caused by other factors like power consumption and maintenance. With modern optimization methods, it is already possible to plan an optimal technical system considering multiple objectives. In this paper, we focus on an often neglected contribution to the lifecycle costs: downtime costs due to spontaneous failures. Consequently, availability becomes an issue.

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 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.

Sign in / Sign up

Export Citation Format

Share Document