• 정보화사회
• /
• s.125
• /
• pp.58-61
• /
• 1998

통신서비스 요금 연체.미납자에 대한 신용정보 공동관리시스템을 한국정보통신진흥협회에 구축하여 동 시스템에 신용불량정보를 집중하고 사업자는 가입자 유치시 동 시스템을 조회하여 신용불량자로 등록되어 있는 자에 대해서는 가입을 제한하도록 한다. 또한 악성 신용불량자에 대해서는 신용정보업자의 전산망에 등록하여 각종 신용거래에 제한을 받게 함으로써 신용정보공동 관리의 실효성을 높여 나갈 방침이다.

• The Korean Journal of Financial Studies
• /
• v.2 no.1
• /
• pp.23-42
• /
• 1995

본 논문은 단기신용등급의 변화가 주가에 미치는 영향을 실증적으로 고찰함으로써 신용등급의 정보기능과 우리나라 주식시장의 효율성을 함께 검증하는데 그 목적이 있다. 단기신용등급이 변화한 104 상장기업을 대상으로 한 실증분석 결과, 우리나라의 주식시장이 기업의 재무상태와 영업상태를 수시로 주가에 반영하여 신용등급변화의 발표 자체가 갖는 정보효과는 극히 작다는 것을 보임으로써 주식시장의 효율성이 지지되었다. 신용등급변화의 정보효과가 평균적으로는 영에 가까우나 발표일 이전에 관련 정보가 주식시장에 적게 반영된 기업의 경우 신용등급변화의 정보효과가 크게 나타남을 보여줌으로써 신용등급변화의 발표가 기존 정보의 오차를 수정하는 효과가 있음을 시사하였다.

• Korean Security Journal
• /
• no.9
• /
• pp.27-68
• /
• 2005

Recently, because credit card crime using a personal credit information is increasing, professionalizing, and spreading the area, the loss occurring from credit card crime is enormous and is difficult to arrest and punish the criminals. At past, crime from forging and counterfeiting the credit card was originated by minority criminals, but at present, the types and appearance of credit card crime is very different to contrasting past crime. The numbers of people using credit card in the middle of 1990's was increasing and barometer of living conditions was evaluated by the number having credit card, therefore this bad phenomenon occurring from credit card crime was affected by abnormal consumption patterns. There is no need emphasizing the importance of personal credit card in this credit society. so, because credit card crime using personal credit card information has a bad effect, and brings the economic loss and harms to individuals, credit card company, and members joining credit card. Credit card crime using personal credit card information means the conduct using another people's credit card information(card number, expiring duration, secret number) that detected by unlawful means. And crime using dishonest means from another people's credit information is called a crime profiting money-making and a crime lending an illegal advance by making false documents. A findings on countermeasures of this study are as follows: Firstly, Diverting user's mind, improving the art of printing, and legitimating password from payment gateway was suggested. Secondly, Complementing input of password, disseminating the system of key-board protection, and promoting legitimations of immediate notification duty was suggested. Thirdly, Certificating the electronic certificates as a personal certificates, assuring the recognition by sense organ of organism, and lessening the ratio of crime occurrence, and restricting the ratio of the credit card crime was suggested.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.6
• /
• pp.1285-1303
• /
• 2019

In the current credit scoring system, the credit bureau gathers credit information from financial institutions and calculates a credit score based on it. However, because all sensitive credit information is stored in one central authority, there are possibilities of privacy violations and successful external attacks can breach large amounts of personal information. To handle this problem, we propose privacy-preserving credit scoring in which a user gathers credit information from financial institutions, calculates a credit score and proves that the score is calculated correctly using a zero-knowledge proof and a blockchain. In addition, we propose a zero-knowledge proof scheme that can efficiently prove committed inputs to check whether the inputs of a zero-knowledge proof are actually provided by financial institutions with a blockchain. This scheme provides perfect zero-knowledge unlike Agrawal et al.'s scheme, short CRSs and proofs, and fast proof and verification. We confirmed that the proposed credit scoring can be used in the real world by implementing it and experimenting with a credit score algorithm which is similar to that of the real world.

• Journal of Digital Convergence
• /
• v.15 no.1
• /
• pp.199-208
• /
• 2017

This study investigates the components and rating system of reliable technology credit information for a technology finance donor who is a consumer of the information and aims to create an effective and optimal technology credit appraisal system to enlarge technology finance supply. Firstly, we calculate the optimal TCAR which becomes the maximum AUROC through the combination of ratio change, verify the substitution possibility between TAR and CR through the existing CR and system gap simulation, and propose a rating system by which financial institutes can utilize the TCAR as a credit rating. As a result, 70% : 30% is the most suitable as the weighted combination ratio of credit rating : technology rating. As a result of this study, we confirmed the possibility that the technical credit rating information could be substituted by the credit rating or the technology appraisal rating. Furthermore, it also suggests that sophisticated risk management is possible through using technology credit rating that are combined with credit and technology appraisal rating.

• The Journal of Small Business Innovation
• /
• v.20 no.3
• /
• pp.19-30
• /
• 2017

Today's new engine of economic growth is innovative technology intensive SMEs. However, they have limited access to funding because of asymmetric information problems. Sharing of SME information helps reduce information asymmetry. This paper explains the Korean system of SME technology information sharing, as well as SME credit information sharing. It also provides theoretical analysis about the effects of the SME information sharing on banks' credit analysis activities, based on Karapetyan and Stacescu (2013). Sharing of SME credit and technology information expands the data set of banks and it will enhance their credit analysis. In addition, SME information sharing increases banks' investments in credit analysis activities. To encourage SME information sharing and production, the government can subsidize the production of SME technology information.

• Journal of the Korea Society for Simulation
• /
• v.30 no.1
• /
• pp.75-85
• /
• 2021

In this paper, we present a deep neural network-based prediction model that processes and analyzes the corporate credit and personal credit information of individual business owners as a new method to predict the default rate of individual business more accurately. In modeling research in various fields, feature selection techniques have been actively studied as a method for improving performance, especially in predictive models including many features. In this paper, after statistical verification of macroeconomic indicators (macro variables) and credit information (micro variables), which are input variables used in the default rate prediction model, additionally, through the credit information feature selection method, the final feature set that improves prediction performance was identified. The proposed credit information feature selection method as an iterative & hybrid method that combines the filter-based and wrapper-based method builds submodels, constructs subsets by extracting important variables of the maximum performance submodels, and determines the final feature set through prediction performance analysis of the subset and the subset combined set.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.10 no.12
• /
• pp.3768-3774
• /
• 2009

This paper explored how soft information and hard information were used when SBC(Small Business Corporation, Korea) reviewed government loan applications. The data set is made up of financial and non-financial data of small-business firms since 2004. A non-financial data set is considered as soft information. Relative importance of three kinds information such as credit information, soft information, financial information is compared with each other by using the logit model. As a result, credit information is most critical to the loan approval, and then soft information follows, lastly financial information has the smallest effect on the loan approval. This is because the credit information is made up of the non-linear combination of soft information and financial information. When the relative importance of soft information and financial information is considered, soft information is relatively more critical to the loan approval then financial information. This is because financial ratios provided by small-business firms are not reliable enough.

• 정보보호뉴스
• /
• s.136
• /
• pp.48-51
• /
• 2009

금년 들어 미국의 신용카드 결제처리업체인 하트랜드 시스템즈(Heartland Payment Systems)에서 사상 최악의 신용카드 정보 유출 사건이 발생했으며, 이로 인한 금융 피해금액이 수억 달러에 이를 것으로 추산되고 있다. 이 회사는 매달 1억건에 달하는 신용카드 거래를 처리한다고 하니 그 유출된 개인 금융정보의 양은 상상을 초월할 것으로 보인다. 이렇게 유출된 정보는 카드복제, 신용도용 등 쉽게 예상할 수 있는 범죄 이외에도 어떠한 형태의 범죄로 당사자들에게 피해가 되돌아 올 지는 가늠하기 어렵다. 최근 국내에서 발생한 일련의 개인정보 유출사건과 마찬가지로 정보화와 개인정보보호 문제에 경종을 울리는 또 하나의 사건이라 하겠다.

• Journal of Intelligence and Information Systems
• /
• v.5 no.1
• /
• pp.49-64
• /
• 1999

본 연구는 백화점 고객이 신용 카드 신청 요구 시에 작성되는 가입 정보 및 사용되고 있는 고객의 거래 정보는 카드 사용 패턴으로 신용도를 예측하는 여러 방법론을 제시하고 성능을 비교하였다. 가입 정보를 분석하기 위해 역전파 신경망(Back-Propagation Neural Network, BPNN), 사례기반추론(Case-Based reasoning)을, 거래 정보를 분석하기 위해 역전파 신경망과 더불어 시간지연 신경망(Time-Delayed Neural Network, TDNN)을 각각 사용하여 그 결과를 비교하였다. 또한 전체시스템의 적중률을 높이기 위햐여, ID3와 신경망을 이용한 Meta-Leaning 방법을 제시하였으며, Meta-Learning 방법과 다른 방법들을 비교, 분석을 하였다. 본 연구에서는 모형 수립과 검증을 위하여 T백화점의 실제 신용 카드 가입 고객 데이터를 이용하여 실험하였다. 데이터의 성격에 따라 각 모델의 예측력에는 차이가 나타났으나, 신경망 모형의 예측력이 우수하였으며, 시간적 특성을 고려하는 시간지연 신경회로망 모형의 예측력은 더욱 우수하게 나타났다. 또한 Meta-Learning 모형을 사용하면 예측력이 더 높아진다는 것을 확인할 수 있었다.