Xiaoxiao Zhan1, Xueyan Li1, Wendong Su2
1Faculty of Statistics and Applied Mathematics, Anhui University of Finance and Economics
2School of international trade and economics, Anhui University of Finance and Economics, Anhui, Bengbu, 233000
In order to solve the problem of bank credit risk quantification and credit alloca-tion strategy for small, medium and micro enterprises, This paper deals with the data indicators of 123 enterprises with credit records, Logistic regression model and RAROC (risk-adjusted return) model are used comprehensively, the default probability of an enterprise is obtained as the quantitative result of credit risk, and according to the risk quantification results to develop credit strategy. Finally, comprehensive consideration of a variety of emergent factors on the different impact of enterprises, and make adjustments to your credit strategy.
Credit risk quantification; Credit strategy; Logistic; RAROC
Xiaoxiao Zhan, Xueyan Li, Wendong Su. Comprehensive Study of Small Micro-Enterprise Credit Risk Quantification and Credit Decisions. Academic Journal of Business & Management (2021) Vol. 3, Issue 5: 28-34. https://doi.org/10.25236/AJBM.2021.030505.
 Li Jin. Research on Credit Risk Measurement of Commercial Banks in China Based on KMV Model [D]. Shanxi University of Finance and Economics, 2016.
 Yang Xiaoyong. Research on Quantitative Model of Commercial Bank Credit Risk [D]. Southwest Jiaotong University, 2004.
 Zeng Fanlong, Jing Ni. Research on Bank Credit Decisions with Absence of Historical Data -- Model Construction Based on Prudential Trust Field and Machine Learning [J]. Research on Financial Regulation, 2020, (3): 85-98.
 Wang Yue. A Comparative Study of Double Model Forecasting Methods for Credit Risk Identification of Listed Private Enterprises [D]. Inner Mongolia University of Science and Technology, 2020.
 Yuandong Lan. Research on Theory, Algorithm and Application of Semi-supervised Learning Based on Graph [D]. South China University of Technology, 2012.
 Xiaoyan Yang. Study on Simulation of Logistic Regression Model and Rare Event Logistic Regression Model [D]. Sichuan University, 2005.
 Yunqi. Research on Brand Recommendation of E-commerce Website Based on Time-varying Characteristics of User Behavior [D]. Hunan University, 2018.
 Fu Guobao, Ma Tingting. An Empirical Study on the Credit Risk Assessment of Shipping Finance Leasing Enterprises Based on Logistic Model [J]. Journal of Shanghai Institute of Shipping Science, 2019, 42(3): 62-66+72.
 YIN Zhentao. Research on Random Missing Value Filling and Its Effect [D]. Shanghai Normal University, 2018.
 Xiao Hongshan. Research on Integrated Learning Model and Algorithm for Risk-Oriented Decision Making Problem [D]. Chongqing University, 2017.