Big Data Algorithm Applied to Credit Risk Assessment Model

2016 ◽  
Author(s):  
Guest Editor Jianping Du
Keyword(s):  
Risk Assessment ◽  
Big Data ◽  
Credit Risk ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Credit Risk Assessment

scholarly journals Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Aiwen Niu ◽  
Bingqing Cai ◽  
Shousong Cai
Keyword(s):  
Risk Assessment ◽  
Big Data ◽  
Credit Risk ◽  
Big Data Analytics ◽  
Assessment Model ◽  
Effective Control ◽  
Credit Score ◽  
Credit Risk Assessment ◽  
Index Weight ◽  
Online Lending

With the continuous development of big data technology, the data of online lending platform witness explosive development. How to give full play to the advantages of data, establish a credit risk assessment model, and realize the effective control of platform credit risk have become the focus of online lending platform. In view of the fact that the network loan data are mainly unbalanced data, the smote algorithm is helpful to optimize the model and improve the evaluation performance of the model. Relevant research shows that stochastic forest model has higher applicability in credit risk assessment, and cart, ANN, C4.5, and other algorithms are also widely used. In the influencing factors of credit evaluation, the weight of the applicant’s enterprise scale, working years, historical records, credit score, and other indicators is relatively high, while the index weight of marriage and housing/car production (loan) is relatively low.

scholarly journals Credit Risk Assessment Model for Small and Micro-Enterprises: The Case of Lithuania

Risks ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 67 ◽  
Author(s):  
Rasa Kanapickiene ◽  
Renatas Spicas
Keyword(s):  
Risk Assessment ◽  
Credit Risk ◽  
Trade Credit ◽  
Assessment Model ◽  
Financial Ratios ◽  
Risk Assessment Model ◽  
Credit Risk Assessment ◽  
Financial Variables ◽  
The Empirical Analysis ◽  
Micro Enterprises

In this research, trade credit is analysed form a seller (supplier) perspective. Trade credit allows the supplier to increase sales and profits but creates the risk that the customer will not pay, and at the same time increases the risk of the supplier’s insolvency. If the supplier is a small or micro-enterprise (SMiE), it is usually an issue of human and technical resources. Therefore, when dealing with these issues, the supplier needs a high accuracy but simple and highly interpretable trade credit risk assessment model that allows for assessing the risk of insolvency of buyers (who are usually SMiE). The aim of the research is to create a statistical enterprise trade credit risk assessment (ETCRA) model for Lithuanian small and micro-enterprises (SMiE). In the empirical analysis, the financial and non-financial data of 734 small and micro-sized enterprises in the period of 2010–2012 were chosen as the samples. Based on the logistic regression, the ETCRA model was developed using financial and non-financial variables. In the ETCRA model, the enterprise’s financial performance is assessed from different perspectives: profitability, liquidity, solvency, and activity. Varied model variants have been created using (i) only financial ratios and (ii) financial ratios and non-financial variables. Moreover, the inclusion of non-financial variables in the model does not substantially improve the characteristics of the model. This means that the models that use only financial ratios can be used in practice, and the models that include non-financial variables can also be used. The designed models can be used by suppliers when making decisions of granting a trade credit for small or micro-enterprises.

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