Affiliations: [a] Departments of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China
| [b] School of Statistics, Jiangxi University of Finance & Economics, Nanchang, China
Correspondence:
[*]
Corresponding author. Lu Guo, School of Statistics, Jiangxi University of Finance & Economics, Nanchang, 330013, China. E-mail: [email protected].
Abstract: Nowadays, the Expansion and evolution of the global financial system oblige lenders to develop stricter requirements for assessing creditworthiness of borrowers. This paper analyses the problems prevalent in the existing credit models of coastal cities in China Pearl River Delta, including data centralization, difficulties in detecting forged data and delay in data transmission; we constructed a CDDC model based fuzzy sets that employs all the issues. The related results showed that the technology fuzzy sets decentralizes and expands data sources, acquires and processes data automatically and self-perfects its ability to rank borrowers into cohorts of creditworthiness. Moreover, the CDDC model out-performs the traditional model in assessing creditworthiness and reducing delinquencies and defaults. That means our fuzzy sets model employs decentralized data sources, destroys historical data regularly and facilitates training and improvement. It ranks creditworthy borrowers in a better fashion than the statistics-based traditional credit model.
