• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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• pp.1-12
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• 2022

Our study adds to the body of knowledge about the relationship between credit ratings and the capital structure of bond issuers. Using Bloomberg and Datastream databases and employing panel regression models, we study the capital structure changes of Japanese enterprises after credit rating changes by global rating agencies (S&P and Moody's) as well as their local counterparts (R&I and JCR) from 1998 to 2016. We find that after rating downgrades, Japanese enterprises considerably reduce net debt or net debt relative to net equity, similar to the findings of Kisgen (2009), who focused on U.S. industrial firms. They do not, however, make adjustments to their financial structure as a result of rating improvements. In comparison to downgrades by S&P and Moody's, Japanese corporations issue 1.89 percent less net debt and 1.50 percent less net debt relative to net equity after R&I and JCR rating downgrades. To put it another way, Japanese companies consider rating adjustments made by local agencies to be more significant than those made by global rating organizations. Our findings contradict earlier research that suggests S&P and Moody's are more prominent in the investment community than R&I and JCR in Japan.

• Smart Media Journal
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• v.13 no.2
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• pp.9-15
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• 2024

In this paper, a Diffusion Multi-step Classifier (DMC) is proposed to address the imbalance issue in credit prediction. DMC utilizes a Diffusion Model to generate continuous numerical data from credit prediction data and creates categorical data through a Multi-step Classifier. Compared to other algorithms generating synthetic data, DMC produces data with a distribution more similar to real data. Using DMC, data that closely resemble actual data can be generated, outperforming other algorithms for data generation. When experiments were conducted using the generated data, the probability of predicting delinquencies increased by over 20%, and overall predictive accuracy improved by approximately 4%. These research findings are anticipated to significantly contribute to reducing delinquency rates and increasing profits when applied in actual financial institutions.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.21-30
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• 2023

The study proposes a model that utilizes Python-based deep learning text classification techniques to detect the legality of illegal financial advertising posts on the internet. These posts aim to promote unlawful financial activities, including the trading of bank accounts, credit card fraud, cashing out through mobile payments, and the sale of personal credit information. Despite the efforts of financial regulatory authorities, the prevalence of illegal financial activities persists. By applying this proposed model, the intention is to aid in identifying and detecting illicit content in internet-based illegal financial advertisining, thus contributing to the ongoing efforts to combat such activities. The study utilizes convolutional neural networks(CNN) and recurrent neural networks(RNN, LSTM, GRU), which are commonly used text classification techniques. The raw data for the model is based on manually confirmed regulatory judgments. By adjusting the hyperparameters of the Korean natural language processing and deep learning models, the study has achieved an optimized model with the best performance. This research holds significant meaning as it presents a deep learning model for discerning internet illegal financial advertising, which has not been previously explored. Additionally, with an accuracy range of 91.3% to 93.4% in a deep learning model, there is a hopeful anticipation for the practical application of this model in the task of detecting illicit financial advertisements, ultimately contributing to the eradication of such unlawful financial advertisements.

• Journal of Families and Better Life
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• v.21 no.6
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• pp.43-51
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• 2003

The purpose of this research was to investigate the perception of financial risks and expenditures for insurance by household characteristics. Data were collected from 598 housewives by online survey on Dec., 2001. Results indicated that respondents had perceived the risk of unemployment most among three types of risks. Household characteristics reflecting financial needs in emergency case had positive effects on the perception of risks, and hence the expenditures for insurance, in general. On the other hand, the level of emergency preparation had negative effects on the perception of risks and the expenditures for insurance. However, only credit-related risk had a positive relationship with the expenditures for insurance.

• The Journal of Fisheries Business Administration
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• v.31 no.2
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• pp.93-117
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• 2000

Fisheries finance is divided into the policy time of long period of time and low interest and the special financing institutions, such as Fisheries Co-operatives. Union system finance is the system finance, which supports the fisheries system organization. Fisheries Co-operatives in cities, towns and villages are the independent management objects. Prefecture federation of Fisheries Co-operative is in prefecture stage. Norm Chukin Bank is in national stage. Each shares functions in these three stages, and finance is performed systematically, Fisheries policy finance comprises government financial institution capital such as the Agriculture, Forestry and Fishery Finance Corporation whish is based on the capital of a country or a prefecture financial fund, and fishery Modernization Capital used as financial funds through the government. Moreover, to complement such finance institutionally, Fisheries Credit Foundations, Agriculture and Fisheries Saving Insurance Corporation and National fisheries Co-operative Trust Enterprise Mutual Aid system have been established

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.10
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• pp.995-1001
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• 2014

In the aftermath of the financial crisis of 2008, while numerous members of the general public lost their homes and jobs, many of the largest banks held responsible for the crisis have been successfully rescued by bailouts. In this paper, through the analysis of income inequality, unemployment, tax cuts, and bailouts, we show that the interests of the general public are different from the interests of politicians and bankers. While the small elite group of politicians and bankers could set the deregulation policies with inordinate power based on full information, most people were ignorant and unconcerned about the policies, and hence did not oppose them. Specifically, we model the credit change in the financial markets of the United States by a finite state machine, and design three local supervisors representing three groups with different interests. It is then shown that the deregulation policies were adopted according to the difference of the supervisors' powers.

• Journal of Information Technology Services
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• v.21 no.3
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• pp.1-25
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• 2022

The financial MyData service has implemented in January 2022 and launched 45 services by banks, securities, credit cards and fintech companies. This study applied the Q methodology, to identify the user types of MyData services and compared the perceptions of employees of financial institutions who plan and develop the MyData services. There are three types of MyData service users: active users, limited users who focus on consumption and asset status inquiry, and sensitive users for personal information. There were two types of recognition of financial company employees. One is the active user support other is the sensitive user for personal information support. The analysis of subjective perceptions can be used as a reference for establishing a company's MyData service marketing strategy and establishing related policies to improve the MyData ecosystem.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.1
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• pp.133-144
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• 2017

This study investigates firm-specific financial variables that determine investment or speculative grades from the viewpoint of firms, which are one of the major stakeholders related to the credit rating. We employ an ordered probit model for our analysis with the sample data from 1999 to 2015 for listed firms in the Korean stock markets. For investment grades, operating margin, sales, market-to-book, dividend payment, capital expenditure ratio, and tangible asset ratio have a significantly positive impact on credit ratings. In the subsample for speculative grades, the coefficients of the dividend payment, retained earnings ratio, and capital expenditure ratio are significantly positive while short-term debt ratio and R&D expenditures have a significantly negative impact on credit ratings. For the analysis before and after 2009, when the Credit Information Use and Protection Act was strengthened after the global financial crisis, the coefficients of the capital expenditure ratio, cash ratio, and tangible asset ratio are significantly positive in the subsample for investment grades before 2009, but not significant after 2010. The coefficient of the long-term debt ratio is more significantly negative than that of the short-term debt ratio before 2009, for speculative grades, but short-term debt ratio has a more negative effect on ratings than long-term debt ratio after 2010. Surprisingly, the coefficient of the R&D expenditures is significantly negative in both investment and speculative grades since 2010. Our findings are inconsistent with the conjecture that the increase in R&D expenditures enhances the possibility of creating cash-flow by raising the investment growth opportunity, and thus affects positively the credit rating.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.3
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• pp.125-140
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• 2018

According to statistics, it is shown that domestic SMEs rely on bank loans for the majority of fund procurement. From financial information shortage (Thin file) that does not provide information necessary for credit evaluation from banks such as financial statements. In order to overcome these problems, recently, in alternative finance such as P2P, using differentiated information such as demographics, trading information and the like utilizing Fintech instead of existing financial information, small funds A new credit evaluation method has been expanding to provide SMEs with small amounts of money. In this paradigm of environmental change, in this research, credit evaluation which can expand fund supply to SMEs by utilizing big data based on trade area information such as sales fluctuation, location conditions etc. In this research, we try to find such a solution. By analyzing empirically the big data generated in the trade area, we verify the effectiveness as a credit evaluation factor and try to derive the main parameters necessary for the business performance evaluation of the founder of SMEs. In this research, for 17,116 material businesses in Seoul City that operate the service industry from 2009 to February 2018, we collect trade area information generated for each business location from Big Data specialized company NICE Zini Data Co., Ltd.. We collected and analyzed the data on the locations and commercial areas of the facilities that were difficult to obtain from SMEs and analyzed the data that affected the Corporate financial Distress. It is possible to refer to the variable of the existing unused big data and to confirm the possibility of utilizing it for efficient financial support for SMEs, This is to ensure that commercial lenders, even in general commercial banks, are made to be more prominent in one sector of the financing of SMEs. In this research, it is not the traditional financial information about raising fund of SMEs who have basically the problem of information asymmetry, but a trade area analysis variable is derived, and this variable is evaluated by credit evaluation There is differentiation of research in that it verified through analysis of big data from Trading-area whether or not there is an effect on.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.6
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• pp.1525-1540
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• 2015

Due to a rapid advancement in the electronic commerce technology, the payment method varies from cash to electronic settlement such as credit card, mobile payment and mobile application card. Therefore, financial fraud is increasing notably for a purpose of personal gain. In response, financial companies are building the FDS (Fraud Detection System) to protect consumers from fraudulent transactions. The one of the goals of FDS is identifying the fraudulent transaction with high accuracy by analyzing transaction data and personal information in real-time. Data mining techniques are providing great aid in financial accounting fraud detection, so it have been applied most extensively to provide primary solutions to the problems. In this paper, we try to provide an overview of the research on data mining based fraud detection. Also, we classify researches under few criteria such as data set, data mining algorithm and viewpoint of research.