default prediction
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Author(s):  
Lorena Poenaru-Olaru ◽  
Judith Redi ◽  
Arthur Hovanesyan ◽  
Huijuan Wang
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PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261737
Author(s):  
Jong Wook Lee ◽  
So Young Sohn

Potential relationship among loan applicants can provide valuable information for evaluating default risk. However, most of the existing credit scoring models either ignore this relationship or consider a simple connection information. This study assesses the applicants’ relation in terms of their distance estimated based on their characteristics. This information is then utilized in a proposed spatial probit model to reflect the different degree of borrowers’ relation on the default prediction of loan applicant. We apply this method to peer-to-peer Lending Club Loan data. Empirical results show that the consideration of information on the spatial autocorrelation among loan applicants can provide high predictive power for defaults.


2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jing Gao ◽  
Wenjun Sun ◽  
Xin Sui

The credit card business has become an indispensable financial service for commercial banks. With the development of credit card business, commercial banks have achieved outstanding results in maintaining existing customers, tapping potential customers, and market share. During credit card operations, massive amounts of data in multiple dimensions—including basic customer information; billing, installment, and repayment information; transaction flows; and overdue records—are generated. Compared with preloan and postloan links, user default prediction of the on-loan link has a huge scale of data, which makes it difficult to identify signs of risk. With the recent growing maturity and practicality of technologies such as big data analysis and artificial intelligence, it has become possible to further mine and analyze massive amounts of transaction data. This study mined and analyzed the transaction flow data that best reflected customer behavior. XGBoost, which is widely used in financial classification models, and Long-Short Term Memory (LSTM), which is widely used in time-series information, were selected for comparative research. The accuracy of the XGBoost model depends on the degree of expertise in feature extraction, while the LSTM algorithm can achieve higher accuracy without feature extraction. The resulting XGBoost-LSTM model showed good classification performance in default prediction. The results of this study can provide a reference for the application of deep learning algorithms in the field of finance.


Equilibrium ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. 859-883
Author(s):  
Michal Karas ◽  
Mária Režňáková

Research background: SMEs face financial constraints in their development, which limits their access to external funds, tightens their investment possibilities, and limits their growth. Much research effort has been devoted to understanding the nature and sources of this phenomenon. In sharp contrast to this, very little has been said about the role of these factors in explaining the default probability of these types of enterprises. Understanding such interrelationships could help to adopt policies to alleviate the situation of constrained SMEs and lower their default rates. Purpose of the article: This study analyses the role of financial constraint factors in SME defaults. This is done by utilising the financial constraint factors in a newly derived default prediction model. A comparison of the derived model and other SME default prediction models is carried out to assess the potential of financial constraints in the discrimination power of the model. Methods: In this study, we use the Cox semiparametric model, while leaving the baseline hazard rate unspecified and employing macroeconomic variables as explanatory variables. The discrimination power was addressed in terms of the area under the curve (AUC), resulting in out-of-sample testing. The DeLong test was used to compare the AUC of the created and analysed models. The model was estimated on a set of over 213,731 SMEs from 28 counties, covering the period 2014?2019. Findings & value added: It was found that adopting the financial constraint measures can explain the default of small and medium enterprises with high accuracy; however, they do not explain the default of micro enterprises.


2021 ◽  
Vol 9 (4) ◽  
pp. 65
Author(s):  
Daniela Rybárová ◽  
Helena Majdúchová ◽  
Peter Štetka ◽  
Darina Luščíková

The aim of this paper is to assess the reliability of alternative default prediction models in local conditions, with subsequent comparison with other generally known and globally disseminated default prediction models, such as Altman’s Z-score, Quick Test, Creditworthiness Index, and Taffler’s Model. The comparison was carried out on a sample of 90 companies operating in the Slovak Republic over a period of 3 years (2016, 2017, and 2018) with a narrower focus on three sectors: construction, retail, and tourism, using alternative default prediction models, such as CH-index, G-index, Binkert’s Model, HGN2 Model, M-model, Gulka’s Model, Hurtošová’s Model, Model of Delina and Packová, and Binkert’s Model. To verify the reliability of these models, tests of the significance of statistical hypotheses were used, such as type I and type II error. According to research results, the highest reliability and accuracy was achieved by an alternative local Model of Delina and Packová. The least reliable results within the list of models were reported by the most globally disseminated model, Altman’s Z-score. Significant differences between sectors were identified.


2021 ◽  
Vol 33 (6) ◽  
pp. 1-18
Author(s):  
Jun Hou ◽  
Qianmu Li ◽  
Yaozong Liu ◽  
Sainan Zhang

As an important global policy guide to promote economic transformation and upgrading, the upsurge of E-Commerce has been continuously upgraded with continuous breakthroughs in information technology. In recent years, China’s e-commerce consumer credit has developed well, but due to its short time of production and insufficient experience for reference, credit risk, fraud risk, and regulatory risk continue to emerge. Aiming at the problem of E-Commerce Consumer Credit default analysis, this paper proposes a Fusion Enhanced Cascade Model (FECM). This model learns feature data of credit data by fusing multi-granularity modules, and incorporates random forest and GBDT trade-off variance and bias methods. The paper compares FECM and gcForest on multiple data sets, to prove the applicability of FECM in the field of E-commerce credit default prediction. The research results of this paper are helpful to the risk control of financial development, and to construct a relatively stable financial space for promoting the construction and development of E-Commerce.


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