Big Data Credit Report in Credit Risk Management of Consumer Finance

被引:8
作者
Gao, Lu [1 ]
Xiao, Jian [2 ]
机构
[1] Shandong Univ, Sch Business, Weihai 264209, Peoples R China
[2] Beijing Int Studies Univ, Sch Business, Beijing 100024, Peoples R China
关键词
PORTFOLIO; MARKETS; CDS;
D O I
10.1155/2021/4811086
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Traditional consumer finance is a modern financial service method that provides consumer loans to consumers of all classes. With the gradual improvement of China's credit reporting system, big data credit reporting has effectively made up for the lack of traditional credit reporting and has been widely used in the consumer finance industry. In this context, the in-depth analysis of the specific application of big data credit reporting in the credit risk management of consumer finance and the strengthening of the research on the application of big data credit reporting in the credit risk management of consumer finance are urgently needed to be resolved in the economic and financial theoretical and practical circles' problem. This article mainly studies the research on credit risk management of consumer finance by big data. The experimental results of this paper show that the model has a good forecasting ability, can distinguish between normal loan customers and default loan customers, and is suitable for practical personal credit risk control business. The prediction accuracy of the default model of the fusion model is 97.14%, and the default rate corresponding to the actual business is 2.86%. By combining the risk items such as the blacklist and gray list in the Internet finance industry, the bad debt rate and illegal usury can be well controlled to meet industry supervision.
引用
收藏
页数:7
相关论文
共 27 条
  • [11] Lee Y., 2016, J CREDIT RISK, V10, P62, DOI [10.21314/jcr.2014.1722-s2.0-84973477592, DOI 10.21314/JCR.2014.1722-S2.0-84973477592]
  • [12] Credit risk transfer in SME loan guarantee networks
    Leng, Aolin
    Xing, Guangyuan
    Fan, Weiguo
    [J]. JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY, 2017, 30 (05) : 1084 - 1096
  • [13] Semi-analytical Formula for Pricing Bilateral Counterparty Risk of CDS with Correlated Credit Risks
    Lin, Feng
    Xie, Si-yuan
    Yang, Jing-ping
    [J]. ACTA MATHEMATICAE APPLICATAE SINICA-ENGLISH SERIES, 2018, 34 (02): : 209 - 236
  • [14] ILLIQUIDITY COMPONENT OF CREDIT RISK
    Morris, Stephen
    Shin, Hyun Song
    [J]. INTERNATIONAL ECONOMIC REVIEW, 2016, 57 (04) : 1135 - 1148
  • [15] A dynamic approach merging network theory and credit risk techniques to assess systemic risk in financial networks
    Petrone, Daniele
    Latora, Vito
    [J]. SCIENTIFIC REPORTS, 2018, 8
  • [16] Identification of a standard AI based technique for credit risk analysis
    Punniyamoorthy, M.
    Sridevi, P.
    [J]. BENCHMARKING-AN INTERNATIONAL JOURNAL, 2016, 23 (05) : 1381 - 1390
  • [17] Smart Vehicular System based on the Internet of Things
    Reddy, Midde Ranjit
    Srinivasa, K. G.
    Reddy, B. Eswara
    [J]. JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING, 2018, 30 (03) : 45 - 62
  • [18] Schmitt TA, 2015, J CREDIT RISK, V11, P73
  • [19] Rating momentum in the macroeconomic stress testing and scenario analysis of credit risk
    Skoglund, Jimmy
    Chen, Wei
    [J]. JOURNAL OF RISK MODEL VALIDATION, 2017, 11 (01): : 21 - 47
  • [20] Skoglund J, 2016, J CREDIT RISK, V12, P1