• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.485-488
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• 2006

기업의 올바른 경영과 꾸준한 성장을 위해선 원활한 자금흐름이 필요하다. 더욱이 장기간의 경기 침체와 급변하는 금융환경의 상황에서 기업은 자금에 대한 유동성 확보 및 향후 예측력이 필요하고 체계적으로 자금흐름을 관리하여야 한다. 즉 정확한 자금흐름을 예측하여 합리적 자금조달과 운용을 실시하며 그 결과에 대한 실적 및 분석을 통해 의사결정 정보를 적시에 제공함으로써 기업 경쟁력을 강화 시키기 위한 시스템이 필요하다. 따라서 본 연구는 Cash flow 중점관리라는 효율적인 자금관리 측면의 새로운 개념의 업무 개선 방향을 정립하고 자금수지관리를 체계화 시킬 수 있는 표준 자금수지 시스템 모델을 제시하여 향후 업무 자동화 및 그룹 업무 표준화를 이루고자 한다.

• Informatization Policy
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• v.19 no.2
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• pp.85-105
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• 2012

So far, the importance of informatization, as well as investment into it, has been growing steadily. Due to the uncertainties and risks in adopting information technologies, systematic decision-making is definitely needed in investing in a large scale information system. Based on the existing theories about the economic life span of information systems and in consideration of the actual cost involved in the adoption and operation of the systems by the financial institutions in Korea, this study presents the optimal economic life span for all types of information systems in terms of the economic cost and generalizes the optimal life span. The ultimate purpose of this study is to develop a model that could be used in anticipating the timing of economic replacement of the information system of the same type and making decisions on IT investment.

• Korean Security Journal
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• no.62
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• pp.185-203
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• 2020

While overall crime has been on the decline since 2009, voice phishing has rather been on the rise. The government and academia have presented various measures and conducted research to eradicate it, but it is not enough to catch up with evolving voice phishing. In the study, researchers focused on catching criminals and preventing damage from voice phishing, which is difficult to recover from. In particular, a voice phishing prediction method using the Fraud Detection System (FDS), which is being used to detect financial fraud, was studied based on the fact that the victim engaged in financial transaction activities (such as account transfers). As a result, it was conceptually derived to combine big data such as call details, messenger details, abnormal accounts, voice phishing type and 112 report related to voice phishing in machine learning-based Fraud Detection System(FDS). In this study, the research focused mainly on government measures and literature research on the use of big data. However, limitations in data collection and security concerns in FDS have not provided a specific model. However, it is meaningful that the concept of voice phishing responses that converge FDS with the types of data needed for machine learning was presented for the first time in the absence of prior research. Based on this research, it is hoped that 'Voice Phishing Damage Prediction System' will be developed to prevent damage from voice phishing.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.19-43
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• 2016

This study examines the causes of the Asian exchange rate crisis and compares it to the European Monetary System crisis. In 1997, emerging countries in Asia experienced financial crises. Previously in 1992, currencies in the European Monetary System had undergone the same experience. This was followed by Mexico in 1994. The objective of this paper lies in the generation of useful insights from these crises. This research presents a comparison of South Korea, United Kingdom and Mexico, and then compares three different models for prediction. Previous studies of economic crisis focused largely on the manual construction of causal models using linear techniques. However, the weakness of such models stems from the prevalence of nonlinear factors in reality. This paper uses a structural equation model to analyze the causes, followed by a neural network model to circumvent the linear model's weaknesses. The models are examined in the context of predicting exchange rates In this paper, data were quarterly ones, and Consumer Price Index, Gross Domestic Product, Interest Rate, Stock Index, Current Account, Foreign Reserves were independent variables for the prediction. However, time periods of each country's data are different. Lisrel is an emerging method and as such requires a fresh approach to financial crisis prediction model design, along with the flexibility to accommodate unexpected change. This paper indicates the neural network model has the greater prediction performance in Korea, Mexico, and United Kingdom. However, in Korea, the multiple regression shows the better performance. In Mexico, the multiple regression is almost indifferent to the Lisrel. Although Lisrel doesn't show the significant performance, the refined model is expected to show the better result. The structural model in this paper should contain the psychological factor and other invisible areas in the future work. The reason of the low hit ratio is that the alternative model in this paper uses only the financial market data. Thus, we cannot consider the other important part. Korea's hit ratio is lower than that of United Kingdom. So, there must be the other construct that affects the financial market. So does Mexico. However, the United Kingdom's financial market is more influenced and explained by the financial factors than Korea and Mexico.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.121-133
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• 2017

The propose of study is to examine the characteristics of companies with high possibility to form an internal control weakness using forecasting model. This study use the actual listed/unlisted companies' data from K_financial institution. The first conclusion is that discriminant model is more valid than logit model to predict internal control weak companies. A discriminant model for predicting the vulnerability of internal control has high classification accuracy and has low the Type II error that is incorrectly classifying vulnerable companies to normal companies. The second conclusion is that the characteristic of weak internal control companies have a low credit rating, low asset soundness assessment, high delinquency rates, lower operating cash flow, high debt ratios, and minus operating profit to the net sales ratio. As not only a case of listed companies but unlisted companies which did not occur in previous studies are extended in this study, research results including the forecasting model can be used as a predictive tool of financial institutions predicting companies with high potential internal control weakness to prevent asset losses.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.564-567
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• 2013

ID/Password 방식은 노출 및 예측 공격에 대한 위험성을 안고 있다. 이를 해결하기 위한 방법으로 OTP(One time Password)를 인증 시스템에 적용할 수 있다. OTP는 매번 다른 패스워드를 생성하여 사용하는 사용자 인증 방식이다. 전자금융감독규정에 의해 OTP는 인터넷뱅킹, 모바일뱅킹, 텔레뱅킹 등 전자 금융 거래 시 보안카드를 대체하는 1등급 보안매체로 지정되었지만 OTP 단말기는 배포 및 사용의 편의성의 문제로 대중화의 어려움과 동기화 실패의 문제점이 존재하게 된다. 본 논문은 통신 기술의 발달로 현재 많이 대중화되어 있고 하나의 개인 컴퓨터와 같이 정보를 저장하고 연산이 가능한 Mobile 장치를 이용하여 HOTP기반의 OTP를 생성하여 사용자 인증을 제공함으로써 OTP 단말기의 배포 및 편의성의 문제를 해결할 수 있는 방식을 제한한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.810-812
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• 2017

최근 기계학습에 대한 관심이 높아지면서 금융 분야에서는 인공지능을 이용하여 투자 포트폴리오를 제안하는 로보어드바이저(robo-advisor)를 출시하고 있다. 이는 고객에게 저렴한 수수료를 제공하며 높은 접근성, 인건비의 절감 등의 장점으로 이를 도입하여 다양한 상품을 개발하고 있다. 본 연구에서는 머신러닝 알고리즘인 SVM(support vector machine)과 kNN(k-nearest neighbor)을 활용하여 매월 12개월 이전의 KOSPI 지수 데이터를 학습시킨 후 예측하는 투자 시스템을 구현하였다. 실험결과 SVM이 2.90413배의 성적으로 가장 우수했으며 수익률은 Precision(예측정확도)와 비례함을 보였다. 또한 수익곡선은 추세에 따라 유사한 형태를 보인 성과를 도출하였다.

• Proceedings of the Safety Management and Science Conference
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• 2009.04a
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• pp.371-380
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• 2009

본 연구는 국내에 있는 한 금융사에서 사용하고 있는 기존 인사평가시스템을 사례로 하여 시스템 운영자와 사용자(모든 임직원)가 시스템을 효율적으로 운영하고 사용할 수 있도록 인사평가시스템을 재구축하였다. 새로 구축될 인사평가시스템의 효율을 보다 높이기 위해 인사평가 업무와 평가 절차를 분석하고 그러한 평가체계를 기존 legacy 시스템(인사평가시스템)에 어떻게 반영이 되었는지도 분석하였다. 해당 금융사는 기존 평가시스템을 통하여 임원과 직원뿐 아니라, 비정규직원까지 년마다 상반기와 하반기로 나누어 업적과 역량평가를 각각 두번씩 실시하고 있었다. 평가결과는 나중에 보상이나 승진 시에 중요한 고과자료로 활용하고 있었다. 업적과 역량평가 뿐만 아니라, 신업사원을 대상으로 하는 정규직 수습평가와 계약직을 대상으로 한 역량평가 그리고 전직원이 대상인 다면평가도 실시하고 있었다. 그런데 여러 해에 걸쳐 인사평가를 실시하는 과정에서 평가프로세스가 여러 차례 변하였고, 많은 평가대상자를 평가하다 보니 예외적으로 처리해야 하는 경우도 발생하였다. 이런 변화와 예외적인 사항을 기존 Lagacy시스템에 반영하다 보니 평가데이터를 관리하는 테이블이 처음보다는 많이 늘어나게 되어 현재 약 170개에 달하고 있었다. 이렇게 많은 테이블 중에는 당시에는 사용했으나, 현재는 사용하지 않게 된 것도 포함되어 있었다. 프로그램 소스도 마찬가지로 새로운 요구사항과 많은 예외사항 처리로 인해 복잡해지고 프로그램 소스의 수도 늘어나게 되었다. 이로 인해 평가시스템 담당자는 시스템을 관리하기가 복잡해지고, 새로운 변화나 요구사항에 대응하기가 어려운 사항이었다. 그리고 담당자가 시스템 관리에 더 더욱 어려움을 겪게된 것은 시스템 담당자가 여러 번 바뀌는 과정에서 인수인계에 제대로 이루어 지지 않게 되었고 인사평가시스템 관리문서도 시스템 변화에 따른 히스토리 내용이 제대로 관리되지 못하고 있었기 때문이다. 그래서 이번 연구에서는 기존 legacy 시스템의 테이블과 프로그램 소스를 분석하여 방만하게 늘어날 데이터 관리 테이블을 효율적으로 줄이고, 예외적인 사항을 새로운 인사평가시스템에서 포괄적으로 수용할 수 있도록 재설계하였다. 또한 인사평가시스템 운영중에도 사용자(임직원)들로부터 있을 수 있는 예외적 요구사항을 미리 예측하여 운영담당자가 신속하고 정확하게 대응 할 수 있도록 하였다.

• Journal of Korea Port Economic Association
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• v.31 no.2
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• pp.23-40
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• 2015

This study measures how Korean shipping policies influence the expansion of the country's merchant fleet using system dynamics. It uses various indexes as factors influencing the gross tonnage of the Korean merchant fleet, such as the Baltic Dry Index, Howe Robinson Container Index, China Containerized Freight Index, and Worldscale Index, as well as the US dollar-Korean won exchange rate, world merchant fleet statistics, and the debt ratio of Korean shipping companies. After establishing the simulation model, the mean absolute percentage error is found to be less than 10%, confirming the accuracy of the model. Therefore, a sensitivity analysis is conducted to measure the influence of the selected shipping policies, including the gross tonnage of vessels registered under the Korean second registry system, loans of publicly owned financial institutions to shipping companies, ship investment fund, and the number of shipping companies participating in the tonnage tax scheme. The sensitivity analysis reveals that the influence of vessel tonnage and loans to shipping companies is the most significant, while that of the number of companies participating in the tonnage tax scheme is minimal.