An enhanced cascading model for E-commerce consumer credit default prediction

Abstract

As an important global policy guide to promote economic transformation and upgrading, the upsurge of e-commerce has been 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.