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 Improved forecast assessment for the expected credit losses in credit risk monitoring in commercial banks in the context of international and Russian practices

2020 ◽  
Vol 15 (3) ◽  
pp. 445-457
Author(s):  
Elena Vladimirovna Travkina ◽  
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Credit Risk ◽  
Commercial Banks ◽  
Banking Sector ◽  
Assessment Model ◽  
Financial Instruments ◽  
Credit Risk Assessment ◽  
Ifrs 9 ◽  
Credit Losses

Current banking sector’s performance raises the issues connected with the IFRS 9 Financial Instruments driven transformation of the forecast assessment for the expected credit losses during monitoring and credit risk assessment in commercial banks. In this regard, it becomes important to conduct a comprehensive systematization of the existing Russian and international practices for monitoring and evaluating credit risk in commercial banks. The purpose of the study is to develop a comprehensive approach to the use of an effective model for the impairment of expected losses in banking activities. The novelty of the study includes the enhancement of the tools for the forecast assessment of the expected credit losses among the commercial banks’ clients to improve the credit risk management efficiency. The results from the implementation of IFRS 9 Financial Instruments in the banking area show that modern conditions maintain the uncertainty of the long-term impact of the credit risk on the commercial banks’ performance. What is more, a huge amount of additional information gives significant difficulties, which contributes into the sophisticated calculations of the future credit losses of the banks. It has been justified that a forecast assessment model for the expected credit losses of the clients during the monitoring and bank’s credit risk assessment should be based on the collective or individual ground. The efficient application of the expected losses impairment in the banking performance has been described as a fundamental tool to simulate the expected credit losses to provision for impairment. This model has been shown to be determined by the features of the credit activities and bank portfolio, types of its financial tools, sources of the available information, as well as the applied IT systems. The proposed model validation algorithm for the expected impairment losses could reduce the expected credit losses, decrease the volume of the created assessed reserves, as well as improve the overall commercial bank performance efficiency. Theoretically, the study develops the credit losses risk management in the context of the transformations in the global and Russian banking practices. From the perspective of the practical value, the research gives an opportunity to create an efficient forecast assessment model for the expected credit losses of the commercial banks’ clients, this model contributing into the cost effectiveness of the bank’s credit activities. A promising further research is considered to be aimed at developing the tools for the assessment of the commercial banks’ credit activity results in the context of the adopted changes connected with the introduction of IFRS 9 Financial Instruments in the Russian banking sector.

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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