• The Journal of Asian Finance, Economics and Business
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• 제6권3호
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• pp.55-65
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• 2019

The purpose of this study is to discover the interaction between credit growth and operational self-sustainability and to examine factors that affect credit growth and operational self-sustainability of people's credit funds (PCFs). Credit growth and operational self-sustainability are factors affecting the operations and the goals of people's credit funds (PCFs) in the Mekong Delta region of Vietnam. After regression analysis on a set of panel data from 2013 to 2018 of 24 PCFs, it appears that deposit growth and loan-to-deposit ratio have positive relationships with credit growth, while capital adequacy ratio and operational self-sustainability have negative relationships with credit growth of PCFs; capital adequacy ratio, deposit growth and income have positive relationships with operational self-sustainability, while credit growth and non-performing loan ratio have negative relationships with the operational self-sustainability of PCFs. At the same time, credit growth and operational self-sustainability have a relationship to interact with each other in a contrary trend. The results of this research are accurate according to the characteristics and development history of PCFs in the Mekong Delta region of Vietnam from 2013-2018. This study helps researchers and managers to understand the key determinants for better management of PCFs.

• 한국정보시스템학회지:정보시스템연구
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• 제16권3호
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• pp.1-20
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• 2007

Most researches on the corporate credit rating are generally classified into the area of bankruptcy prediction and bond rating. The studies on bankruptcy prediction have focused on improving the performance in binary classification problem, since the criterion variable is categorical, bankrupt or non-bankrupt. The other studies on bond rating have predicted the credit ratings, which was already evaluated by bond rating experts. The financial institute, however, should perform effective loan evaluation and risk management by employing the corporate credit rating model, which is able to determine the credit of corporations. Therefore, this study presents a corporate credit rating method using business failure probability map(BFPM) and AHP(Analytic Hierarchy Process). The BFPM enables us to rate the credit of corporations according to business failure probability and data distribution or frequency on each credit rating level. Also, we developed AHP model for credit rating using non-financial information. For the purpose of completed credit rating model, we integrated the BFPM and the AHP model using both financial and non-financial information. Finally, the credit ratings of each firm are assigned by our proposed method. This method will be helpful for the loan evaluators of financial institutes to decide more objective and effective credit ratings.

• 수산경영론집
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• 제39권1호
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• pp.43-61
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• 2008

Excessive public loan with low interest and other tax benefits have been provided for fishermen, but much of them turned out to be little performed. There were the moral hazards of Suhyup in the process of executing the public loans. As the government gave the reimbursement on the financial loss of Suhyup resulting from the public loans, Suhyup had no responsibility of the bad debt loss. Therefore, Suhyup gave little efforts to reduce the non-performing. The government perceived this problem and tried to reduce the under-performing loans. Thus, the government decided to take limited responsibilities. Suhyup made the progress to reduce the under-performing public loans. Suhyup dealt with these situation and made the credit evaluation model of the fisherman's public loan. This paper is for the credit evaluation model in the fisherman's public loan, which explains the model development methodology and the model characteristics in detail. This evaluation model is composed of two sub-component model. the one is the quantitative model and the other is the qualitative model. The quantitative sub-model is for the identification of fishermen financial status and is based on the financial transaction information. Its development methodology is the CSS modeling for the consumer market. The qualitative sub-model is for the evaluation the business prospect and is based on the business information such as fisherman's management skills, technology, equipment. Its development methodology is the AHP. It provides the detailed information in the model development methodology, which is the ideal example such as the public loan. In addition it gives the information to the interest parties such as policy makers, suhyup and fishermen.

• The Journal of Asian Finance, Economics and Business
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• 제7권7호
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• pp.59-72
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• 2020

This paper examines whether lending structure can lower credit risk by employing econometric techniques of panel data for the Vietnamese banking system at the bank level used by economic sectors from 2011 to 2016. New light is being shed on assessing the impact of each industry's debt outstanding on credit risk. Adopting findings from previous studies, we assess credit risk from two different sources, including loan loss provision and non-performing loan. Moreover, we also focus on observing lending structure in many different aspects, from concentrative levels to the short-term and long-term stability levels of lending structure. The Generalized Method of Moments (GMM) estimator was applied to analyze the relationship between concentration and banking risks. In general, the results show that lending concentration may decrease credit risk. It is interesting to observe that the Vietnamese commercial bank lending portfolios have, on average, higher levels of diversity across different sectors. In particular, the increase in hotel and restaurant lending contributes to decrease credit risk while the lending portfolios of banks in agriculture, electricity, gas and water increase credit risk. This study suggests the need for further analysis and research about portfolio risks in lending activities for maintaining efficiency and stability in the commercial banking system.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2002년도 춘계공동학술대회
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• pp.1044-1051
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• 2002

This paper proposes a two-phase mathematical programming approach by considering classification gap to solve the proposed credit scoring problem so as to complement any theoretical shortcomings. Specifically, by using the linear programming (LP) approach, phase 1 is to make the associated decisions such as issuing grant of credit or denial of credit to applicants. or to seek any additional information before making the final decision. Phase 2 is to find a cut-off value, which minimizes any misclassification penalty (cost) to be incurred due to granting credit to 'bad' loan applicant or denying credit to 'good' loan applicant by using the mixed-integer programming (MIP) approach. This approach is expected to and appropriate classification scores and a cut-off value with respect to deviation and misclassification cost, respectively. Statistical discriminant analysis methods have been commonly considered to deal with classification problems for credit scoring. In recent years, much theoretical research has focused on the application of mathematical programming techniques to the discriminant problems. It has been reported that mathematical programming techniques could outperform statistical discriminant techniques in some applications, while mathematical programming techniques may suffer from some theoretical shortcomings. The performance of the proposed two-phase approach is evaluated in this paper with line data and loan applicants data, by comparing with three other approaches including Fisher's linear discriminant function, logistic regression and some other existing mathematical programming approaches, which are considered as the performance benchmarks. The evaluation results show that the proposed two-phase mathematical programming approach outperforms the aforementioned statistical approaches. In some cases, two-phase mathematical programming approach marginally outperforms both the statistical approaches and the other existing mathematical programming approaches.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.624-626
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• 2023

Credit scoring is a technique used by financial institutions to assess the creditworthiness of potential borrowers. This involves evaluating a borrower's credit history to predict the likelihood of defaulting on a loan. This paper presents an ensemble of two Transformer based models within a framework for discriminating the default risk of loan applications in the field of credit scoring. The first model is FinBERT, a pretrained NLP model to analyze sentiment of financial text. The second model is FT-Transformer, a simple adaptation of the Transformer architecture for the tabular domain. Both models are trained on the same underlying data set, with the only difference being the representation of the data. This multi-modal approach allows us to leverage the unique capabilities of each model and potentially uncover insights that may not be apparent when using a single model alone. We compare our model with two famous ensemble-based models, Random Forest and Extreme Gradient Boosting.

• 한국콘텐츠학회논문지
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• 제10권2호
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• pp.361-371
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• 2010

본 논문은 국내유가증권 상장기업들을 대상으로 영업부채가 관행적인 영업상 지급결제역할 이외에도 해당기업의 주요한 재원조달역할 및 정보창출기능도 수행하는지를 분석하였다. 국내기업들의 경우 정보관련 비용부담이 큰 기업일수록 영업부채 의존도가 유의적으로 높은 것으로 나타났는데, 이는 납품기업에 의한 영업부채가 구매기업관련 신용정보 서비스를 제공하고 있으며, 이로 인해 정보비대칭관련 비용부담이 큰 구매기업들에게 매우 효과적인 재원이 될 수 있음을 나타내는 결과이다. 그러나 재벌소속 기업들의 경우에도 상대적으로 영업부채 의존도가 높은 것으로 나타났는데, 이러한 결과는 재벌소속 기업들이 비재벌소속 기업들에 비해 구매력 우위를 바탕으로 납품기업들로부터 영업부채 제공을 더 많이 받기 때문인 것으로 분석되었다. 한편, 영업부채를 많이 사용하는 기업들은 은행으로부터의 차입이 상대적으로 적은 것으로 나타났는데, 이는 영업부채와 은행부채가 정보관련 비용부담이 큰 기업들에게 상호 대체재일 가능성을 증거한다.

• The Journal of Asian Finance, Economics and Business
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• 제8권8호
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• pp.365-373
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• 2021

Loan monitoring is a fundamental element of credit control. The importance of loan monitoring is to reduce loan defaults and to increase loan repayment performance. The ex-post monitoring can also increase the loan quality and thus a concern to many creditors in the market economy. This study identifies the role of ex-post monitoring on loan repayment performance in the context of rural Vietnam. Employing primary data from rural areas of Vietnam, this study investigates the impacts of ex-post monitoring on loan repayment performance of individual borrowers in terms of on-time loan repayment and the rate of loan repayment. Descriptions of borrower demographics, loan information, and ex-post monitoring show the initial picture of borrowers and loans in Vietnam's rural areas. Quantitative estimations that use the Probit and the Tobit model confirm the argument that better ex-post monitoring would result in better loan repayment performance in both on-time repayment and repayment rate. Thus, some policy suggestions have been made to improve the monitoring system in each financial institution. The study also indicates that some other factors too affect loan repayment performance such as borrower's characteristics (education, agricultural working area, and income) as well as loan's characteristics (loan size, maturity, and loan purpose).

• Journal of the Korean Data and Information Science Society
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• 제24권3호
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• pp.411-425
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• 2013

데이터마이닝이란 대용량의 자료로부터 의미있는 패턴과 규칙을 찾기 위해서 자동화되거나 반자 동화된 도구를 이용하여 데이터를 탐색하고 분석하는 과정이다. 이러한 데이터마이닝 기법을 통해 정보의 연관성을 파악함으로써 가치 있는 정보를 만들어 합리적인 의사 결정이 가능하게 된다. 금융분야에서도 데이터베이스 마케팅, 신용평가, 서비스 품질개선, 부정행위 적발 등에 데이터마이닝 기법이 다양하게 사용되고 있다. 금융거래에서 대출의 중요도와 필요성이 시간이 지날수록 점점 높아지고 있으나, 대출을 이용하는 사람과 대출건수가 증가할수록 부실대출의 위험이 함께 증가하기 때문에 대출을 해주는 여신기관의 손실을 막기 위해서는 대출여부를 정확하게 예측할 필요성이 존재한다. 본 연구에서는 국내 A 여신기관의 실제 데이터를 사용하여 대출심사에 관한 연구를 진행하였으며, 모형 구축에 있어서 안정적이고 정확한 예측을 보이는 모형을 찾기 위하여 원 데이터에서의 샘플 정제와 여러가지 모형, 데이터마이닝 기법 등을 사용하여 다양한 모형을 구축하고 비교, 평가하였다.

• 한국벤처창업학회:학술대회논문집
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• 한국벤처창업학회 2007년도 추계학술대회
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• pp.81-109
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• 2007

정책자금의 대출금리는 기준금리에 정책목적성 스프레드와 신용위험 스프레드를 합한 것으로 결정할 수 있다. 중진공의 중산기금 대출사업은 정책목적성 중심의 저금리 대출이라는 공공성에 치우쳐져 있기 때문에 기금의 안정성과 수익성은 크게 훼손되고 있다. 중산기금이 설치목적과 공익에 맞도록 운영되더라도 기금의 안정성과 수익성이 훼손되어서는 아니 된다. 결국 중산기금 중 대출사업 관리주체인 중진공은 대출금리 설정에 있어서 최소한 기금에 손실이 발생하지 않게끔 대출금리를 설정해야 한다. 이는 정부의 정책목적성 스프레드를 현재 부(-)에서 영(0)으로 바꾸어 나가야 함을 의미한다.