• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.6
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• pp.13-24
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• 2017

The purpose of this study is to examine the effect of the management of receivables on the management performance of micro manufacturing industries. The results of the survey are as follows. First, among the factors of management of pre- and post-trade receivables in the micro manufacturing industries, management organization and regulations, contract execution management, bad debt control, which are the subordinate factors of credit control, are positive (+) significant effect on stability. In terms of profitability, management organizations and regulations, which are subordinate factors of credit control management, have a positive (+) significant effect on profitability. The recovery management, which is a factor of management of post - receivable receivables, did not have a significant effect on the stability and profitability of financial management performance. Second, the effect of financial performance on organizational satisfaction is positively related to stability, while profitability has no significant effect on organizational satisfaction. The implication of this study is that pre - trade receivables management is more important than post - trade receivables management in the management of accounts receivables of micro manufacturing industries. Proactive credit management refers to the procedure of establishing and managing personal guarantees and physical guarantees in order to smooth the execution of the obligations at the same time as the contract is concluded through processes such as credit investigation, analysis and evaluation, and sales decision before the contract is concluded. Post receivables management based on the assumption of default is a receivables management procedure from receipt of receivables that are already defaulted to bad debts to bad debt processing. If the collection of receivables is delayed or bad debt is increased, Furthermore, a corporation may be subject to bankruptcy risk (insolvency by paper profits). Therefore, it is meaningful that this study suggests direction to induce change of contract type in advance by understanding the possibility of settlement of accounts receivable and recovery of bad debts within the day of transition in case of contract of micro manufacturing industries.

• Information Systems Review
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• v.10 no.3
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• pp.89-109
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• 2008

Korean card firms suffered harsh setbacks due to high credit defaults in 2002 and 2003, after issuing cards recklessly. Their key principle is changed to grow without damaging profitability and financial soundness. However, competition in the credit card market is heating up rapidly. Bank-affiliated card firms, having stronger sales networks and more capital than independent issuers, have increased their investments in card affiliates in a bid to develop new cash cows. Moreover, newly emerging independent card firms have waged fiercer campaigns to raise their credit card market share. In order to overcome these business conditions, S-card has settled on a strategy that focuses on stepping up marketing aimed at increasing charge card spending rather than credit card loans or cash lending services. Accordingly, S-card reformed the current business classification system of merchants, which was out-of-dated and originally built for the purpose of deciding merchant service fees only. They also drove customer segmentation planning to deliver the right customers to the right merchants. In this paper, we emphasize the problems of business classification systems of merchants with which most credit card firms have faced, and the need for reforming them not only to provide customer-tailored services but also to raise their business promotion excellence by reviewing S-card's process of customer segmentation.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.61-67
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• 2013

The B2B e-commerce assurance system supports e-commerce purchases by Credit Guarantee Fund. This process seeks to replace a variety of current systems, including B2C, the credit card payment method on B2B, 2001 Credit Guarantee Fund and the Bank, logistics, e-MP (Market Place), and Business-to-business e-MP (business-to-business electronic payment settlement system). Over the past 10 years of its operation, the e-MP service (B2B e-commerce electronic payment systems) has contributed much to the growth of SMEs. The development of business-to-business e-commerce transactions systems and limits have provided a stable purchasing platform, improving corporate competitiveness. However. the project-based scale of credit guarantee institutions has limitations. To overcome these limitations, we propose a new model of direct or indirect government support for small business e-MP projects. This new model will support the B2B electronic commerce by allowing it to directly involve guarantee institutions directly in B2B online transactions. Therefore, this study urges government backing of the SME based B2B online business model with e-MP service.

• Journal of Science and Technology Studies
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• v.19 no.1
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• pp.91-135
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• 2019

As we live a life increasingly mediated by computers, we often outsource our critical judgments to artificial intelligence(AI)-based algorithms. Most of us have become quite dependent upon algorithms: computers are now recommending what we see, what we buy, and who we befriend with. What happens to our lives and identities when we use statistical models, algorithms, AI, to make a decision for us? This paper is a preliminary attempt to chronicle a historical trajectory of judging people's economic and moral worth, namely the history of credit-rating within the context of the history of capitalism. More importantly this paper will critically review the history of credit-rating from its earlier conception to the age of big data and algorithmic evaluation, in order to ask questions about what the political implications of outsourcing our judgments to computer models and artificial intelligence would be. Some of the questions I would like to ask in this paper are: by whom and for what purposes is the computer and artificial intelligence encroached into the area of judging people's economic and moral worth? In what ways does the evolution of capitalism constitute a new mode of judging people's financial and personal identity, namely the rated self? What happens in our self-conception and identity when we are increasingly classified, evaluated, and judged by computer models and artificial intelligence? This paper ends with a brief discussion on the political implications of the outsourcing of human judgment to artificial intelligence, and some of the analytic frameworks for further political actions.

• KDI Journal of Economic Policy
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• v.46 no.1
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• pp.91-129
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• 2024

The Bank of Korea, through a legal amendment in 2011 following the financial crisis, was entrusted with the additional responsibility of financial stability beyond its existing mandate of price stability. Since then, concerns have been raised about the prolonged increase in household debt compared to income conditions, which could constrain consumption and growth and increase the possibility of a crisis in the event of negative economic shocks. The current accumulation of financial imbalances suggests a critical period for the government and central bank to be more vigilant, ensuring it does not impede the stable flow of our financial and economic systems. This study examines the applicability of the Integrated Inflation Targeting (IIT) framework proposed by the Bank for International Settlements (BIS) for macro-financial stability in promoting long-term economic stability. Using VAR models, the study reveals a clear increase in risk appetite following interest rate cuts after the financial crisis, leading to a rise in household debt. Additionally, analyzing the central bank's conduct of monetary policy from 2000 to 2021 through DSGE models indicates that the Bank of Korea has operated with a form of IIT, considering both inflation and growth in its policy decisions, with some responsiveness to the increase in household debt. However, the estimation of a high interest rate smoothing coefficient suggests a cautious approach to interest rate adjustments. Furthermore, estimating the optimal interest rate rule to minimize the central bank's loss function reveals that a policy considering inflation, growth, and being mindful of household credit conditions is superior. It suggests that the policy of actively adjusting the benchmark interest rate in response to changes in economic conditions and being attentive to household credit situations when household debt is increasing rapidly compared to income conditions has been analyzed as a desirable policy approach. Based on these findings, we conclude that the integrated inflation targeting framework proposed by the BIS could be considered as an alternative policy system that supports the stable growth of the economy in the medium to long term.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.520-526
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• 2016

This study aims to develop a distress prediction model, in order to evaluate the distress prediction power for first-class hotels and to calculate the average financial ratio in the Seoul area by using the financial ratios of hotels in 2015. The sample data was collected from 19 first-class hotels in Seoul and the financial ratios extracted from 14 of these 19 hotels. The results show firstly that the seven financial ratios, viz. the current ratio, total borrowings and bonds payable to total assets, interest coverage ratio to operating income, operating income to sales, net income to stockholders' equity, ratio of cash flows from operating activities to sales and total assets turnover, enable the top-level corporations to be discriminated from the failed corporations and, secondly, by using these seven financial ratios, a discriminant function which classifies the corporations into top-level and failed ones is estimated by linear multiple discriminant analysis. The accuracy of prediction of this discriminant capability turned out to be 87.9%. The accuracy of the estimates obtained by discriminant analysis indicates that the distress prediction model's distress prediction power is 78.95%. According to the analysis results, hotel management groups which administrate low level corporations need to focus on the classification of these seven financial ratios. Furthermore, hotel corporations have very different financial structures and failure prediction indicators from other industries. In accordance with this finding, for the development of credit evaluation systems for such hotel corporations, there is a need for systems to be developed that reflect hotel corporations' financial features.

• Korean Journal of Construction Engineering and Management
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• v.19 no.2
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• pp.96-106
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• 2018

The government adopted activation policy of real estate to overcome low economic growth rate. Real estate activation plan adopted by the government raised credit limit by lowering the regulation, and reduced real estate investment cost by reducing the base rate. Also, delayed transfer tax on multi-house owner to activate real estate investment and resolved purchase right resale. Relief of real estate regulate caused increase of housing sales and price increase, and the real estate market changed to overheating aspect such as premium upon completion of lot sale in a short time. Such market atmosphere greatly increased household debs as owners own houses based on 'financial debt' instead of their income. Since 2017, real estate policy was reinforced to reduce household debts and lending rate was raised due to rise of base rate, accordingly, burden of household debt is expected to increase. This research suggested a plan for the Financial Supervisory Service to efficiently manage the financial world by analyzing the cause and problem of household debs.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.169-185
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• 2024

Due to levitate and rapid growth of E-Commerce, most of the organizations are moving towards cashless transaction Unfortunately, the cashless transactions are not only used by legitimate users but also it is used by illegitimate users and which results in trouncing of billions of dollars each year worldwide. Fraud prevention and Fraud Detection are two methods used by the financial institutions to protect against these frauds. Fraud prevention systems (FPSs) are not sufficient enough to provide fully security to the E-Commerce systems. However, with the combined effect of Fraud Detection Systems (FDS) and FPS might protect the frauds. However, there still exist so many issues and challenges that degrade the performances of FDSs, such as overlapping of data, noisy data, misclassification of data, etc. This paper presents a comprehensive survey on financial fraud detection system using such data mining techniques. Over seventy research papers have been reviewed, mainly within the period 2002-2015, were analyzed in this study. The data mining approaches employed in this research includes Neural Network, Logistic Regression, Bayesian Belief Network, Support Vector Machine (SVM), Self Organizing Map(SOM), K-Nearest Neighbor(K-NN), Random Forest and Genetic Algorithm. The algorithms that have achieved high success rate in detecting credit card fraud are Logistic Regression (99.2%), SVM (99.6%) and Random Forests (99.6%). But, the most suitable approach is SOM because it has achieved perfect accuracy of 100%. But the algorithms implemented for financial statement fraud have shown a large difference in accuracy from CDA at 71.4% to a probabilistic neural network with 98.1%. In this paper, we have identified the research gap and specified the performance achieved by different algorithms based on parameters like, accuracy, sensitivity and specificity. Some of the key issues and challenges associated with the FDS have also been identified.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• KDI Journal of Economic Policy
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• v.14 no.1
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• pp.191-212
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• 1992

This paper discusses the role of money in the process of capital accumulation where financial markets are impeded by contract enforcement problems in the context of overlapping generations framework. In particular, in less developed countries (LDCs) creditors may know little about the repayment capability of potential debtors due to incomplete information so that financial instruments other than money may not acceptable to them. In this paper the impediments to the operation of the private finanical markets are explicitly modelled. We argue that creditors cannot observe actual investment decisions made by the potential borrowers, and as a result, loan contracts may not be fully enforceable. Therefore, a laissez-faire regime may fail to provide the economy with the appropriate financial instruments. Under these circumstances, we introduce a government operated discount window (DW) that acts as an open market buyer of private debt. This theoretical structure represents the practice of governments of many LDCs to provide loans (typically at subsidized interest rates) to preferred borrowers either directly or indirectly through the commercial banking system. It is shown that the DW can substantially overcome impediments to trade which are caused by the credit market failure. An appropriate supply of the DW loan enables producers to purchase the resources they cannot obtain through direct transactions in the credit market. This result obtains even if the DW is subject to the same enforcement constraint that is responsible for the market failure. Thus, the DW intervention implies higher investment and output. However, the operation of the DW may cause inflation. Furthermore, the provision of cheap loans through the DW results in a worse income distribution. Therefore, there is room for welfare enhancing schemes that utilize the higher output to develop. We demonstrate that adequate lump sum taxes-cum-transfers along with the operation of the DW can support an allocation that is Pareto superior to the laissez-faire equilibrium allocation.