• Convergence Security Journal
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• v.14 no.4
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• pp.109-116
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• 2014

Personal information leakage in financial corporations including three card corporations has occurred constantly this year. It is due to incomplete encryption system and negligent personal security. Solicitors are known as a cause of information leakage because they operate with leaked information. Information leakage can cause secondary damage with mental demage to person and result in a drop in reliability as well as an operating loss in financial corporations. Also because it can destroy a base of credit society, prevention of recurrence is badly needed. The government finally announced 'general measures for prevention of information leakage in the field of finance' with sanctions reinforcement and restriction to collect, possess, provide personal information as the main agenda. And a related law revision is going in the National Assembly. In this paper, effectiveness of government measures is weighed with the cause analysis of information leakage and countermeasure for prevention of information leakage is found.

• Smart Media Journal
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• v.13 no.4
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• pp.9-15
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• 2024

Tree-based algorithms have been the dominant methods used build a prediction model for tabular data. This also includes personal credit data. However, they are limited to compatibility with categorical and numerical data only, and also do not capture information of the relationship between other features. In this work, we proposed an ensemble model using the Transformer architecture that includes text features and harness the self-attention mechanism to tackle the feature relationships limitation. We describe a text formatter module, that converts the original tabular data into sentence data that is fed into FinBERT along with other text features. Furthermore, we employed FT-Transformer that train with the original tabular data. We evaluate this multi-modal approach with two popular tree-based algorithms known as, Random Forest and Extreme Gradient Boosting, XGBoost and TabTransformer. Our proposed method shows superior Default Recall, F1 score and AUC results across two public data sets. Our results are significant for financial institutions to reduce the risk of financial loss regarding defaulters.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.881-888
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• 2015

Financial Authorities have added security controls to the Electronic Financial Transaction Act and the Supervisory Regulation according to the recent frequent personal credit information leakages. Accordingly, the security level has been upgraded. But it is necessary to study more security controls to add. This paper deduces 19 security controls over the mean value to be added to the financial area receiving 15 security consultant's help.

• Journal of the Korea Society for Simulation
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• v.30 no.1
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• pp.75-85
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• 2021

In this paper, we present a deep neural network-based prediction model that processes and analyzes the corporate credit and personal credit information of individual business owners as a new method to predict the default rate of individual business more accurately. In modeling research in various fields, feature selection techniques have been actively studied as a method for improving performance, especially in predictive models including many features. In this paper, after statistical verification of macroeconomic indicators (macro variables) and credit information (micro variables), which are input variables used in the default rate prediction model, additionally, through the credit information feature selection method, the final feature set that improves prediction performance was identified. The proposed credit information feature selection method as an iterative & hybrid method that combines the filter-based and wrapper-based method builds submodels, constructs subsets by extracting important variables of the maximum performance submodels, and determines the final feature set through prediction performance analysis of the subset and the subset combined set.

• Journal of Korean Society for Quality Management
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• v.36 no.1
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• pp.72-79
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• 2008

The policy for credit card approval/disapproval is based on the applier's personal and financial information. In this paper, we will analyze 2 credit card approval data with several classification methods. We identify which variables are important factors to decide the approval of credit card. Our main tool is an open-source statistical programming environment R which is freely available from http://www.r-project.org. It is getting popular recently because of its flexibility and a lot of packages (libraries) made by R-users in the world. We will use most widely used methods, LDNQDA, Logistic Regression, CART (Classification and Regression Trees), neural network, and SVM (Support Vector Machines) for comparisons.

• Journal of Industrial Convergence
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• v.21 no.6
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• pp.1-11
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• 2023

The purpose of this study is to verify the factors affecting corporate bankruptcy by using non-financial information of companies invested by credit guarantee institutions. In this study, 594 companies (525 normal companies, 69 insolvent companies) invested in by the Korea Credit Guarantee Fund from March 2014 to the end of December 2022 were selected as samples. Non-financial information of companies was divided into founder characteristics information, company characteristics information, and corporate investment information, and cross-analysis and logistic regression analysis were conducted. As a result of the cross-analysis, personal credit rating, industry, and joint investment were selected as significant variables, and logistic regression analysis was conducted for those variables, and two variables, personal credit rating and joint investment, were selected as important factors for bankruptcy. In business management, the founder's personal credit and the importance of joint investment in investment support were found out. It will help to minimize bankruptcy if institutions that support investment in SMEs reflect these results in their screening and systematically build cooperative relationships with private investment institutions. It is hoped that this study will provide an opportunity to pay more attention to the factors that affect the bankruptcy of companies that receive direct investment from public institutions.

• Journal of the Korea Society of Computer and Information
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• v.23 no.6
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• pp.59-66
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• 2018

Recently, the use of mobile payment, called "Pay" payment, is increasing such as 'KakaoPay' and 'SamsungPay'. In Korea, various mobile payment services are running and emerging. The purpose of this study is to examine the intention to switch from using existing credit cards or check card to mobile payment service based on the two-factor theory. We empirically examined the effect of the two factors, the factors that enable switching and the factors that inhibit switching. Therefore, the study use switching cost and switching benefit as parameters to confirm the effect on the switching intention. In addition, this study intends to analyze the differences between two subgroups by the degree of personal innovativeness. We survey smartphone users and credit card or check card users, and analyzed the Smart PLS 3.0 using structural equation model to verify the significance of the research hypothesis.

• Asia pacific journal of information systems
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• v.22 no.1
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• pp.53-77
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• 2012

Financial firms, especially large scaled firms such as KB bank, NH bank, Samsung Card, Hana SK Card, Hyundai Capital, Shinhan Card, etc. should be securely dealing with the personal financial information. Indeed, people have tended to believe that those big financial companies are relatively safer in terms of information security than typical small and medium sized firms in other industries. However, the recent incidents of personal information privacy invasion showed that this may not be true. Financial firms have increased the investment of information protection and security, and they are trying to prevent the information privacy invasion accidents by doing all the necessary efforts. This paper studies how effectively a financial firm will be able to avoid personal financial information privacy invasion that may be deliberately caused by internal staffs. Although there are several literatures relating to information security, to our knowledge, this is the first study to focus on the behavior of internal staffs. The big financial firms are doing variety of information security activities to protect personal information. This study is to confirm what types of such activities actually work well. The primary research model of this paper is based on Theory of Planned Behavior (TPB) that describes the rational choice of human behavior. Also, a variety of activities to protect the personal information of financial firms, especially credit card companies with the most customer information, were modeled by the four-step process Security Action Cycle (SAC) that Straub and Welke (1998) claimed. Through this proposed conceptual research model, we study whether information security activities of each step could suppress personal information abuse. Also, by measuring the morality of internal staffs, we checked whether the act of information privacy invasion caused by internal staff is in fact a serious criminal behavior or just a kind of unethical behavior. In addition, we also checked whether there was the cognition difference of the moral level between internal staffs and the customers. Research subjects were customer call center operators in one of the big credit card company. We have used multiple regression analysis. Our results showed that the punishment of the remedy activities, among the firm's information security activities, had the most obvious effects of preventing the information abuse (or privacy invasion) by internal staff. Somewhat effective tools were the prevention activities that limited the physical accessibility of non-authorities to the system of customers' personal information database. Some examples of the prevention activities are to make the procedure of access rights complex and to enhance security instrument. We also found that 'the unnecessary information searches out of work' as the behavior of information abuse occurred frequently by internal staffs. They perceived these behaviors somewhat minor criminal or just unethical action rather than a serious criminal behavior. Also, there existed the big cognition difference of the moral level between internal staffs and the public (customers). Based on the findings of our research, we should expect that this paper help practically to prevent privacy invasion and to protect personal information properly by raising the effectiveness of information security activities of finance firms. Also, we expect that our suggestions can be utilized to effectively improve personnel management and to cope with internal security threats in the overall information security management system.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.63-72
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• 2018

The human voice is one of the easiest methods for the information transmission between human beings. The characteristics of voice can vary from person to person and include the speed of speech, the form and function of the vocal organ, the pitch tone, speech habits, and gender. The human voice is a key element of human communication. In the days of the Fourth Industrial Revolution, voices are also a major means of communication between humans and humans, between humans and machines, machines and machines. And for that reason, people are trying to communicate their intentions to others clearly. And in the process, it contains various additional information along with the linguistic information. The Information such as emotional status, health status, part of trust, presence of a lie, change due to drinking, etc. These linguistic and non-linguistic information can be used as a device for evaluating the individual's credit worthiness by appearing in various parameters through voice analysis. Especially, it can be obtained by analyzing the relationship between the characteristics of the fundamental frequency(basic tonality) of the vocal cords, and the characteristics of the resonance frequency of the vocal track.In the previous research, the necessity of various methods of credit evaluation and the characteristic change of the voice according to the change of credit status were studied. In this study, we propose a personal credit discriminator by machine learning through parameters extracted through voice.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.47-49
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• 2021

According to the revision of the Data 3 Act in 2020, personal information of medical data can be processed anonymously for statistical purposes, research, and public interest record keeping. However, unidentified data can be re-identified using genetic information, credit information, etc., and personal health information can be abused as sensitive information. In this paper, we derive the need for homomorphic encryption to protect the privacy of personal information separated by sensitive information.