• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.419-437
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• 2023

As the number of thin filers in Korea surpasses 12 million, there is a growing interest in enhancing the accuracy of assessing their credit default risk to generate additional revenue. Specifically, researchers are actively pursuing the development of default prediction models using machine learning and deep learning algorithms, in contrast to traditional statistical default prediction methods, which struggle to capture nonlinearity. Among these efforts, Graph Neural Network (GNN) architecture is noteworthy for predicting default in situations with limited data on thin filers. This is due to their ability to incorporate network information between borrowers alongside conventional credit-related data. However, prior research employing graph neural networks has faced limitations in effectively handling diverse categorical variables present in credit information. In this study, we introduce the Transformer embedded Graph Convolutional Network (TeGCN), which aims to address these limitations and enable effective default prediction for thin filers. TeGCN combines the TabTransformer, capable of extracting contextual information from categorical variables, with the Graph Convolutional Network, which captures network information between borrowers. Our TeGCN model surpasses the baseline model's performance across both the general borrower dataset and the thin filer dataset. Specially, our model performs outstanding results in thin filer default prediction. This study achieves high default prediction accuracy by a model structure tailored to characteristics of credit information containing numerous categorical variables, especially in the context of thin filers with limited data. Our study can contribute to resolving the financial exclusion issues faced by thin filers and facilitate additional revenue within the financial industry.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.467-474
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• 2024

This study analyses customer data from a US online lending platform to empirically document the discriminatory treatment that low- and middle-income borrowers face in financial markets. Researchers are using financial data from nearly 2.93 million loans between 2007~2020 of the Lending Club on the open-source Kaggle platform. We find that thin-filers borrowers, especially those with lower credit scores, receive loans at higher interest rates. This discriminatory treatment undermines financial inclusion and has the potential to increase social inequality. The significance of this research is that it sheds substantial light on the problem of inequality in financial markets and, based on the findings, suggests concrete measures to ensure equitable access to finance for all customers and enhance sustainable financial inclusion. In doing so, we propose a shift towards enhancing the social responsibility of institutions.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.6
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• pp.135-154
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• 2020

This paper analyzes the 5,521 samples of the small and medium-sized businesses(SMBs) obtained from the Korea Credit Guarantee Fund. From January 2014 to September 2019, 85% of the SMBs have 5 or fewer full-time employees. The proportion of SMBs is overwhelmed by the elderly men, and most founders are the CEO. Also, about 87% of the workplace types are rented, while 64% of the CEO's residence types are owner-occupation. 47% of the financial grade score is less than 10 points out of 100 and 80% of SMBs have less than 200 million won of the loan guarantee. In particular, the total guarantee loan amount or the days of net guarantee have significantly positive relations with the working period of the CEO in the same industry, the number of employees, the operation period of SMBs, and the corporate business type. In the case of the financial grading score which has the highest weight in overall credit rating gets higher with the higher number of employees, the longer the operation period, and the corporate business type. However, the quantified non-financial grading score has no significant relationship with other explanatory variables, except for the corporate business type. This implies that a non-financial grade score is measured by other determinants that are not observed by the Korea credit guarantee fund. The pure non-financial grade score has positive relations with the working period of the CEO. Overall, this paper would help Korean SMBs upgrade their credit ratings and expand the money supply when there is no standardized credit rating model or no publicly available evaluation criteria for SMBs. We expect this paper provides important insights for further research and policy-makers for SMBs. In particular, to address the financial needs of thin-filers such as SMBs, technology-based financial services (TechFin) would use alternative data to evaluate the financial capabilities of thin-filers and to develop new financial services.