Instance-based credit risk assessment for investment decisions in P2P lending

By Yanhong Guo, Wenjun Zhou, Chunyu Luo, Chuanren Liu, Hui Xiong. European Journal of Operational Research, 2016

311 citations recorded by the source corpus.

Subfields: Accounting, Economics and Econometrics, Management Information Systems

View Paper: https://www.semanticscholar.org/paper/34c42ad2e5cfca1992636321360f9db9a6eb79ba

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

1. Yufei Xia
2. Fanyin Zhou
3. Feng Shen
4. Zhiyong Li
5. Chuanzhe Liu
6. Keqin Li
7. Yuying Li
8. Nana Liu

In Economics and Econometrics

1. Fanyin Zhou
2. Feng Shen
3. Zhiyong Li
4. Keqin Li
5. Yuying Li
6. Xinyi Hu

Citations in the corpus

• Inferring the outcomes of rejected loans: an application of semisupervised clustering (2019, methodology): “Credit scoring is the tool that is used to predict credit risk in the borrowing and lending markets, which uses information about applicants’ payment history, assets and income, and other financial characteristics that are typically sourced from credit bureaus (Hand and Adams, 2000; Sohn and Shin, 2”
• Evaluating borrower’s default risk in peer-to-peer lending: evidence from a lending platform in China (2017, background): “(2016) proposed an instance-based credit risk assessment model to quantitatively assess the risk of loans in the P2P lending market (Guo et al. 2016).”
• A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring (2017, background): “A few interesting studies in this field include Serrano-Cinca and Gutiérrez-Nieto (2016), and Guo et al. (2016), which aim to provide practical decision support system for lenders in P2P lending.”