• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.380-381
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• 2023

최근 경제침체로 인해 지속되는 연체율 상승의 원인을 지역별 및 시차별로 분석하였다. 독립변수를 가계대출변수, 부동산지수변수, 경제지표변수로 나누었고 통계적 모델링을 통해 총 19 가지 변수로 연체율을 예측하였다. 각 지역마다 상이한 결과가 도출되었는데 이를 바탕으로 지역별 연체율 감소 정책을 제안한다.

• The Korean Journal of Financial Management
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• v.22 no.2
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• pp.213-226
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• 2005

Adverse selection is a heavily scrutinized subject within the financial intermediary industry. Consensus is reached regarding its effect on the loan interest rate. Despite the similar features of financial service offered by the credit card, we still have controversy regarding credit card interest rate on how is adverse selection incurred with the change of interest rate. Thus, this paper explores how does the adverse selection, if ever, take place and affect the credit card interest rate. Information asymmetry regarding the credit card users' type represented by the default probability is assumed. The users are assumed to be rational in that they want to minimize the per unit dollar expense associated with the commercial transaction and financing between the two typical payment methods, cash and credit card. Suppliers, i.e. credit card companies, would like to maximize their profit and would be better off with more pervasive use of credit cards over the cash. Then we could show that the increasing credit card interest rate is subject to the adverse selection, sharing the same tenet with that of the bank loan interest rate proposed by Stiglitz and Weiss. Hence the current theory predicts that credit card market also suffers from adverse selection with increasing interest rate.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.6
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• pp.773-782
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• 2019

Basel III (Third Basel Accord or Basel Standards) is a global, voluntary regulatory framework on bank capital adequacy, stress testing, and market liquidity risk. Basel III regulatory ratios include capital adequacy, asset soundness, and liquidity. The capital adequacy variables include BIS capital adequacy ratio, BIS tier 1 capital ratio, and tangible common equity ratio. The asset soundness variables include non-performing loan ratio and non-performing loan coverage ratio. The liquidity regulation variables include KRW liquidity coverage ratio and foreign currency liquidity coverage ratio. This study aims to investigate how capital adequacy standard affects efficiency of internet primary banks. As a result of this study, BIS capital adequacy ratio of domestic internet primary banks is lower than that of commercial banks. In order to maintain sustainable operation considering capital adequacy regulations, it is necessary to expand additional capital. In addition, the delinquency rate and non-performing loan ratio of domestic internet primary banks is gradually increasing due to the maturity of high-yield loans in 2019.

• Knowledge Management Research
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• v.21 no.3
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• pp.229-247
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• 2020

In the remarkable growth of P2P financial platform in the field of knowledge management, only companies with big data and machine learning technologies are surviving in fierce competition. The ability to analyze borrowers' credit is most important, and platform companies are also recognizing this capability as the most important business asset, so they are building a credit evaluation system based on artificial intelligence. Nonetheless, online P2P platform providers that offer related services only act as intermediaries to apply for investors and borrowers, and all the risks associated with the investments are attributable to investors. For investors, the only way to verify the safety of investment products depends on the reputation of P2P companies from newspaper and online website. Time series information such as delinquency rate is not enough to evaluate the early stage of Korean P2P makers' credit analysis capability. This study examines the credit analysis procedure of P2P loan platform using artificial intelligence through the case analysis method for well known the top three companies that are focusing on the credit lending market and the kinds of information data to use. Through this, we will improve the understanding of credit analysis techniques through artificial intelligence, and try to examine limitations of credit analysis methods through artificial intelligence.