• Knowledge Management Research
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• v.22 no.3
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• pp.151-172
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• 2021

The government's implementation of customer-friendly financial policies, such as lowering commission fees for credit card merchants and lowering the maximum interest rate, put the specialized credit finance companies in a crisis of lowering profitability. In this unfavorable situation, the efficiency study of specialized credit finance companies is meaningful. Accordingly, this study measured the efficiency of 34 specialized credit finance companies through Data Envelopment Analysis (DEA) and meta-frontier analysis. For meta-frontier analysis, specialized credit finance companies were divided into two groups (card companies and non-card companies) by industry or three groups (AA0 and above, AA-, and A+ or below) by credit rating. The results of the analysis will provide general insight into the efficiency of specialized credit finance companies. The results of this study are as follows. First, the average meta-efficiency of card companies was analyzed higher than that of non-card companies. Second, 80% of non-card's decision-making units (DMUs) were inefficient by pure technology rather than by scale. Third, decision-making units (DMUs), which account for 62.5% of the credit card company group and 80% of the 'AA-' credit rating group, are in non-economic areas of scale. Fourth, there was no statistically significant difference in meta-efficiency values (TE and PTE) by industry (card companies, non-card companies) and credit rating (AA0 or higher, AA-, A+ or lower). The contribution of this study will provide strategic initiatives for establishing management strategies to improve inefficiency by measuring the efficiency level of companies under an unfriendly business environment for specialized credit finance companies.

• 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.