• Journal of Family Resource Management and Policy Review
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• v.23 no.2
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• pp.99-115
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• 2019

This study examined the insolvency likelihood of young debtors from the 2018 Household Financial and Welfare Survey. This study used the Household Default Risk Index (HDRI), which considers the ratio of total debt to total assets (DTA), and a total debt service ratio (DSR) to examine the insolvency level of debtors. The descriptive analyses showed no difference in frequency of households with a high probability of insolvency between those less than 35 years of age and those over 35 years of age. However, the median HDRI value for those less than 35 years of age was higher than those over 35 years of age. The multivariate analyses indicated that educational expenses for young Korean debtors was a factor that increased their probability of insolvency, while income was the only variable that decreased their insolvency likelihood.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.1065-1075
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• 2008

For the model validation of credit rating models, Kolmogorov-Smirnov(K-S) statistic has been widely used as a testing method of discriminatory power from the probabilities of default for default and non-default. For the credit rating works, K-S statistics are to test two identical distribution functions which are partitioned from a distribution. In this paper under the assumption that the distribution is known, modified K-S statistic which is formulated by using known distributions is proposed and compared K-S statistic.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3627-3641
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• 2021

Fintech, which stands for financial technology, is growing fast globally since the economic crisis hit the United States in 2008. Fintech companies are striving to secure a competitive advantage over existing financial services by providing efficient financial services utilizing the latest technologies. Fintech companies can be classified into several areas according to their business solutions. Among the Fintech sector, peer-to-peer (P2P) lending companies are leading the domestic Fintech industry. P2P lending is a method of lending funds directly to individuals or businesses without an official financial institution participating as an intermediary in the transaction. The rapid growth of P2P lending companies has now reached a level that threatens secondary financial markets. However, as the growth rate increases, so does the potential risk factor. In addition to government laws to protect and regulate P2P lending, further measures to reduce the risk of P2P lending accidents have yet to keep up with the pace of market growth. Since most P2P lenders do not implement their own credit rating system, they rely on personal credit scores provided by credit rating agencies such as the NICE credit information service in Korea. However, it is hard for P2P lending companies to figure out the intentional loan default of the borrower since most borrowers' credit scores are not excellent. This study analyzed the voices of telephone conversation between the loan consultant and the borrower in order to verify if it is applicable to determine the personal credit score. Experimental results show that the change in pitch frequency and change in voice pitch frequency can be reliably identified, and this difference can be used to predict the loan defaults or use it to determine the underlying default risk. It has also been shown that parameters extracted from sample voice data can be used as a determinant for classifying the level of personal credit ratings.

• KDI Journal of Economic Policy
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• v.44 no.3
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• pp.1-26
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• 2022

This paper analyzes the effects of the cut in the legal maximum interest rate (from 27.4% to 24%) that occurred in February of 2018 on loan interest rates, the default rates, and the loan approval rate of borrowers in the non-banking sector. We use the difference-in-difference identification strategy to estimate the effect of the cut in the legal maximum interest rate using micro-level data from a major credit-rating company. The legal maximum rate cut significantly lowers the loan interest rate and default rate of low-credit borrowers (i.e., high-credit-risk borrowers) in the non-banking sector. However, this effect is limited to borrowers who have not been excluded from the market despite the legal maximum interest rate cut. The loan approval rate of low-credit borrowers decreased significantly after the legal maximum interest rate cut. Meanwhile, the loan approval rate of high-credit and medium-credit (i.e., low credit risk and medium credit risk) borrowers increased. This implies that financial institutions in the non-banking sector should reduce the loan supply to low-credit borrowers who are no longer profitable while increasing the loan supply to high- and medium-credit borrowers.

• 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.

• Nuclear Engineering and Technology
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• v.50 no.7
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• pp.1099-1105
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• 2018

How to choose defaults in risk-informed regulations depends on the conservatism implicated in regulatory defaults. Without a universal agreement on the approaches dealing with the conservatism of defaults, however, the desirability of conservatism in regulatory risk analyses has long been controversial. The opponent views it as needlessly costly and irrational, and the proponent as a form of protection against possible omissions or underestimation of risks. Moreover, the inherent ambiguity of risk makes it difficult to set suitable defaults in terms of risk. This paper, the extension of the previous work [1], focuses on the effects of different levels of conservatism implicated in regulatory defaults on the estimates of risk. According to the postulated behaviors of regulated parties and the diversity of interests of regulators, in particular, various measures for evaluating the effect of conservatism in defaults are developed and their properties are explored. In addition, a simple decision model for setting regulatory defaults is formulated, based on the understanding of the effect of conservatism implicated in them. It can help decision makers evaluate the levels of safety likely to result from their regulatory policies.

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.137-145
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• 2008

We present formulas for two types of cap pricing under fBm-HJM model reflecting the empirical long range dependence in the interest rate model. In particular, we propose a new approach to pricing the cap with the default risk.

• The Korean Journal of Applied Statistics
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• v.29 no.2
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• pp.309-319
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• 2016

For binary classification models, we consider a risk score that is a function of linear scores and estimate the coefficients of the linear scores. There are two estimation methods: one is to obtain MLEs using logistic models and the other is to estimate by maximizing AUC. AUC approach estimates are better than MLEs when using logistic models under a general situation which does not support logistic assumptions. This paper considers imbalanced data that contains a smaller number of observations in the default class than those in the non-default for credit assessment models; consequently, the AUC approach is applied to imbalanced data. Various logit link functions are used as a link function to generate imbalanced data. It is found that predicted coefficients obtained by the AUC approach are equivalent to (or better) than those from logistic models for low default probability - imbalanced data.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1289-1312
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• 2015

This paper studies the comovement of industry defaults among listed companies. Rank correlation coefficients of Spearman's ${\rho}$ and Kendall's ${\tau}$ measure the concordance of default. These non-parametric coefficients do not require distributional assumptions and are easily used even with less data and extreme values. This study predicts a future financial crisis by looking at the comovement of industry defaults. We expect our analyses will aid market participants (including company executives) in making investment or risk management decisions.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.605-615
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• 2017

Recently, financial supervisory authority of Korea and international credit rating agencies have been concerned about a stand-alone rating that is calculated without incorporating guaranteed support of parent companies. Guaranteed by parent companies, most foreign subsidiaries keeps good credit rate in spite of weak financial status. However, what if the parent companies stop supporting the foreign subsidiaries, they could have a probability to go bankrupt. In this paper, we have validated a credit rating model through statistical measurers such as performance, calibration, and stability for Korean companies owning foreign subsidiaries.