• Information Systems Review
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• v.9 no.2
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• pp.129-147
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• 2007

At the time the local credit-card companies plunged into a liquidity crisis in 2002, S-Card was urged to take into account the estimated customer income (ECI) to enhance its customer credit evaluation function for the first time in Korean financial industry. Before this new attempt by S-Card, most credit-card companies including S-Card had performed a customer's credit evaluation based on the customer's behavioral factors such as the amount of purchase on credit, debt payment, and financial history that is provided from the Credit Bureau. However, this approach failed to measure customer's potential value which is one of the major factors in judging the customer's ability to pay, and hence, led to difficulties in risk management. The purpose of this case study is to present the better approach to sophisticated risk management for financial firms in Korea by reviewing S-Card's process of customer income estimation and its application to risk management.

• The Journal of Economics, Marketing and Management
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• v.9 no.5
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• pp.39-50
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• 2021

Purpose: This study is to analyze the changes of consumption patterns to diagnose the economic impacts on consumers' market during COVID-19, and to suggest implications to overcome the new social and economic crisis of Jeju Island. Research design, data, and methodology: We collected a set of credit card transaction records issued by BC Card Company from merchants in Jeju Special Self-Governing Province for past 4 years from 2017 to 2020 from the Jeju Data Hub run by Jeju Special Self-Governing Province. The big data contains details of approved credit card transactions including the approval numbers, amount, locations and types of merchants, time and age of users, etc. The researchers summed up amount in monthly basis, transforming big data to small data to analyze the changes of consumption before and after COVID-19. Results: Sales fell sharply in transportation industries including airlines, and overall consumption by age group decreased while the decrease in consumption among the seniors was relatively small. The sales of Yeon-dong and Yongdam-dong in Jeju City also fell significantly compared to other regions. As a result of the paired t-test of all 73 samples in Jeju City, the p-value of the mean consumption of the credit card in 2019 and 2020 is significant, statistically proven that the total consumption amount in the two years is different. Conclusions: We found there are sensitive spots that can be strategically approached based on the changes in consumption patterns by industry, region, and age although most of companies and small businesses have been hit by COVID-19. It is necessary for local companies and for the government to be focusing their support on upgrading services, in order to prevent declining sales and job instability for their employees, creating strategies to retain jobs and prevent customer churn in the face of the crisis. As Jeju Province is highly dependent on the tertiary industry, including tourism, it is suggested to create various strategies to overcome the crisis of the pandemic by constantly monitoring the sales trends of local companies.

• Asia Marketing Journal
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• v.15 no.4
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• pp.223-241
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• 2014

In May 2012, the collaboration of Hana Bank, top financial service company, and SK Planet, top telecommunication service provider, introduced a new credit card that was filled with all-in-one benefits into the market. Leveraging strong infrastructure of two companies, each top in its own industries, the awareness and preference of 'Club SK Card' brand rapidly increased to about 25% in less than one year. Moreover, this new card was enthroned in the most sold credit card of year 2012, accounting for a market share of 7.2% in the credit card market and more than 80% in the mobile credit card market. To make these results possible, 'Club SK Card' marketing team developed an effective marketing communication strategy which followed the 6M model. The mission of the marketing communication strategy was simple and clear. It was to deliver the card's inherent strengths on consumer benefits that come from the support of subsidiary and affiliated companies of SK Planet. According to OK Cashbag data, the marketing communication team selected the appropriate target consumers and approached them directly, inducing actual purchase behavior. The target consumers received straightforward messages about 'Club SK Card' and were led to join in the new membership at their most frequently visited supermarket or franchise restaurant. The straightforward communication message embedded in an eye-catching commercial ad with a hook song accompanied with a dance was delivered via public media. The ad became so popular that many other television programs quoted or made parodies of the ad. Courtesy of the commercial ad, the brand name disseminated rapidly and widely among the public. In October 2012, an ingenious planning and persistent implementation of the communication strategy results 'Club SK Card' to be ranked top in brand awareness as well as advertising preference tests.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.154-163
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• 2017

Collaborative filtering, one of the most widely used techniques to build recommender systems, is based on the idea that users with similar preferences can help one another find useful items. Credit card user behavior analytics show that most customers hold three or less credit cards without duplicates. This behavior is one of the most influential factors to data sparsity. The 'cold-start' problem caused by data sparsity prevents recommender system from providing recommendation properly in the personalized credit card recommendation scenario. We propose a personalized credit card recommender system to address the cold-start problem, using multiple user profiles. The proposed system consists of a training process and an application process using five user profiles. In the training process, the five user profiles are transformed to five user networks based on the cosine similarity, and an integrated user network is derived by weighted sum of each user network. The application process selects k-nearest neighbors (users) from the integrated user network derived in the training process, and recommends three of the most frequently used credit card by the k-nearest neighbors. In order to demonstrate the performance of the proposed system, we conducted experiments with real credit card user data and calculated the F1 Values. The F1 value of the proposed system was compared with that of the existing recommendation techniques. The results show that the proposed system provides better recommendation than the existing techniques. This paper not only contributes to solving the cold start problem that may occur in the personalized credit card recommendation scenario, but also is expected for financial companies to improve customer satisfactions and increase corporate profits by providing recommendation properly.

• Convergence Security Journal
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• v.16 no.5
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• pp.33-40
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• 2016

Credit card is means of payment used like cash in terms of function and its users have increased consistently. With development of Internet and electronic commerce a role as payment method of credit card has been growing. But as the risk which results from centralized information and online increases, credit card fraud is also growing. Card theft and loss are decreasing due to countermeasure of card companies and financial supervisory authorities, while card forge and identity theft are increasing. Recently because of frequent personal information leakage and deregulation of financial security following easy-to-use payment enforcement, customer's anxiety about card fraud is growing. And the increase of card fraud lowers trust on credit system as well as causes social costs. In this paper, the security problems of card operating system are addressed in depth and the measures such as immediate switch to IC card terminals, introduction of new security technology, supervision reinforcement of the authorities are proposed.

• International Commerce and Information Review
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• v.15 no.2
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• pp.189-206
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• 2013

In this paper, we investigate if the new merchant fee system has problems. The fee was fixed by government policies and there were no processes of listening to market participants. Therefore, we suggest improvements as follows. First, large-sized stores take burdens for the rest of stores so credit card companies and the government should try to minimize the increasing rate for large-sized stores. Second, credit card firms need to give VAN companies roles of stabilizing authorizing processes. Third, periodic update requires because of credibility. Fourth, contents of merchant fee should be disclosed to merchants. Fifth, there needs discussions for merchant fees of debit cards.

• Journal of the Korea Convergence Society
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• v.8 no.11
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• pp.263-269
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• 2017

Credit card holders are showing a higher level of psychology to show their identity beyond the means of payment. The purpose of this study is to investigate which design factors affect the 30th and 40th generation housewives when issuing credit cards. A total of 200 people in the 30s and 40s housewives who live in Seoul and the metropolitan area were selected from among the customers who use the credit card, and the effect of the design factors of the domestic credit card on the selection of the credit card, Based on previous studies, questionnaires were prepared and surveyed. As a result of the survey, it was found that the color design of the credit card design element was the achromatic system and the main image was the simple design which only used the color without emphasizing the image or the logo, and then the character type was preferred. I prefer not to have a card decoration, and I have found that a metal-like card material is the most preferred. Through this, I would like to suggest directions for the design development for 30 and 40 housewives in credit card companies.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.211-219
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• 2008

Customer Loyalty is very important for a company to be survived and to make profit for a long time. Especially, since the credit card company has to manage proper card members and merchants, the CRM(Customer Relationship Management) is much emphasized. A loyalty score is more essential to credit card companies which provide differential financial services based on card members and merchants than any other companies. In this paper, we discuss behavioral measures to define customer loyalty and suggest a method to make loyalty score with an example of a credit card company. The loyalty score developed is considered easy to understand and simple to apply in card industry. In the development of loyalty score, first, we define the loyal customers and non-loyal customers by measuring variables indicating loyalty. And we perform individual logistic regression by each exploratory measuring variable and obtain the weight of measure variables using Chi-square statistics which is used for model fitness. The loyalty score suggested shows very stable results in terms of PSI (Population Stability Index) as time goes.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.1-23
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• 2019

Big data refers to data that is difficult to store, manage, and analyze by existing software. As the lifestyle changes of consumers increase the size and types of needs that consumers desire, they are investing a lot of time and money to understand the needs of consumers. Companies in various industries utilize Big Data to improve their products and services to meet their needs, analyze unstructured data, and respond to real-time responses to products and services. The financial industry operates a decision support system that uses financial data to develop financial products and manage customer risks. The use of big data by financial institutions can effectively create added value of the value chain, and it is possible to develop a more advanced customer relationship management strategy. Financial institutions can utilize the purchase data and unstructured data generated by the credit card, and it becomes possible to confirm and satisfy the customer's desire. CRM has a granular process that can be measured in real time as it grows with information knowledge systems. With the development of information service and CRM, the platform has change and it has become possible to meet consumer needs in various environments. Recently, as the needs of consumers have diversified, more companies are providing systematic marketing services using data mining and advanced CRM (Customer Relationship Management) techniques. KB Kookmin Card, which started as a credit card business in 1980, introduced early stabilization of processes and computer systems, and actively participated in introducing new technologies and systems. In 2011, the bank and credit card companies separated, leading the 'Hye-dam Card' and 'One Card' markets, which were deviated from the existing concept. In 2017, the total use of domestic credit cards and check cards grew by 5.6% year-on-year to 886 trillion won. In 2018, we received a long-term rating of AA + as a result of our credit card evaluation. We confirmed that our credit rating was at the top of the list through effective marketing strategies and services. At present, Kookmin Card emphasizes strategies to meet the individual needs of customers and to maximize the lifetime value of consumers by utilizing payment data of customers. KB Kookmin Card combines internal and external big data and conducts marketing in real time or builds a system for monitoring. KB Kookmin Card has built a marketing system that detects realtime behavior using big data such as visiting the homepage and purchasing history by using the customer card information. It is designed to enable customers to capture action events in real time and execute marketing by utilizing the stores, locations, amounts, usage pattern, etc. of the card transactions. We have created more than 280 different scenarios based on the customer's life cycle and are conducting marketing plans to accommodate various customer groups in real time. We operate a smart offering system, which is a highly efficient marketing management system that detects customers' card usage, customer behavior, and location information in real time, and provides further refinement services by combining with various apps. This study aims to identify the traditional CRM to the current CRM strategy through the process of changing the CRM strategy. Finally, I will confirm the current CRM strategy through KB Kookmin card's big data utilization strategy and marketing activities and propose a marketing plan for KB Kookmin card's future CRM strategy. KB Kookmin Card should invest in securing ICT technology and human resources, which are becoming more sophisticated for the success and continuous growth of smart offering system. It is necessary to establish a strategy for securing profit from a long-term perspective and systematically proceed. Especially, in the current situation where privacy violation and personal information leakage issues are being addressed, efforts should be made to induce customers' recognition of marketing using customer information and to form corporate image emphasizing security.

• Journal of Information Technology Services
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• v.11 no.4
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• pp.233-250
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• 2012

In this paper, credit card delinquency means the possibility of occurring bad debt within the certain near future from the normal accounts that have no debt and the problem is to predict, on the monthly basis, the occurrence of delinquency 3 months in advance. This prediction is typical binary classification problem but suffers from the issue of data imbalance that means the instances of target class is very few. For the effective prediction of bad debt occurrence, Support Vector Machine (SVM) with kernel trick is adopted using credit card usage and payment patterns as its inputs. SVM is widely accepted in the data mining society because of its prediction accuracy and no fear of overfitting. However, it is known that SVM has the limitation in its ability to processing the large-scale data. To resolve the difficulties in applying SVM to bad debt occurrence prediction, two stage clustering is suggested as an effective data reduction method and ensembles of SVM models are also adopted to mitigate the difficulty due to data imbalance intrinsic to the target problem of this paper. In the experiments with the real world data from one of the major domestic credit card companies, the suggested approach reveals the superior prediction accuracy to the traditional data mining approaches that use neural networks, decision trees or logistics regressions. SVM ensemble model learned from T2 training set shows the best prediction results among the alternatives considered and it is noteworthy that the performance of neural networks with T2 is better than that of SVM with T1. These results prove that the suggested approach is very effective for both SVM training and the classification problem of data imbalance.