This paper reviews the rationale of monetary and banking intervention by an outside authority, either the government or the central bank, and seeks to delineate clearly the optimal limits to the monetary and banking deregulation currently underway in Korea as well as on a global scale. Furthermore, this paper seeks to establish an objective and balanced view on the role of the central bank, especially in light of the current discussion on the restructuring of Korea's central bank, which has been severely contaminated by interest-group politics. The discussion begins with the recognition that the modern free banking school and the new monetary economics are becoming formidable challenges to the traditional role of the government or the central bank in the monetary and banking sector. The paper reviews six arguments that have traditionally been presented to support intervention: (1) the possibility of an over-issue of bank notes under free banking instead of central banking; (2) externalities in and the public good nature of the use of money; (3) economies of scale and natural monopoly in producing money; (4) the need for macro stabilization policy due to the instability of the real sector; (5) the external effects of bank failure due to the inherent instability of the existing banking system; and (6) protection for small banknote users and depositors. Based on an analysis of the above arguments, the paper speculates on the optimal role of the government or central bank in the monetary and banking system and the optimal degree of monetary and banking deregulation. By contrast to the arguments for free banking or laissez-faire monetary systems, which become fashionable in recent years, monopoly and intervention by the government or central bank in the outside money system can be both necessary and optimal. In this case, of course, an over-issue of fiat money may be possible due to political considerations, but this issue is beyond the scope of this paper. On the other hand, the issue of inside monies based on outside money could indeed be provided for optimally under market competition by private institutions. A competitive system in issuing inside monies would help realize, to the maxim urn extent possible, external economies generated by using a single outside money. According to this reasoning, free banking activities will prevail in the inside money system, while a government monopoly will prevail in the outside money system. This speculation, then, also implies that the monetary and banking deregulation currently underway should and most likely will be limited to the inside money system, which could be liberalized to the fullest degree. It is also implied that it will be impractical to deregulate the outside money system and to allow market competition to provide outside money, in accordance with the arguments of the free banking school and the new monetary economics. Furthermore, the role of the government or central bank in this new environment will not be significantly different from their current roles. As far as the supply of fiat money continues to be monopolized by the government, the control of the supply of base money and such related responsibilities as monetary policy (argument(4)) and the lender of the last resort (argument (5)) will naturally be assigned to the outside money supplier. However, a mechanism for controlling an over-issue of fiat money by a monopolistic supplier will definitely be called for (argument(1)). A monetary policy based on a certain policy rule could be one possibility. More importantly, the deregulation of the inside money system would further increase the systemic risk inherent in the current fractional banking system, while enhancing the efficiency of the system (argument (5)). In this context, the role of the lender of the last resort would again become an instrument of paramount importance in alleviating liquidity crises in the early stages, thereby disallowing the possibility of a widespread bank run. Similarly, prudential banking supervision would also help maintain the safety and soundness of the fully deregulated banking system. These functions would also help protect depositors from losses due to bank failures (argument (6)). Finally, these speculations suggest that government or central bank authorities have probably been too conservative on the issue of the deregulation of the financial system, beyond the caution necessary to preserve system safety. Rather, only the fullest deregulation of the inside money system seems to guarantee the maximum enjoyment of external economies in the single outside money system.
Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.