• Journal of KIBIM
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• v.13 no.3
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• pp.12-20
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• 2023

Deep learning-based anomaly detection technology is used in various fields such as computer vision, speech recognition, and natural language processing. In particular, this technology is applied in various fields such as monitoring manufacturing equipment abnormalities, detecting financial fraud, detecting network hacking, and detecting anomalies in medical images. However, in the field of construction and architecture, research on deep learning-based data anomaly detection technology is difficult due to the lack of digitization of domain knowledge due to late digital conversion, lack of learning data, and difficulties in collecting and processing field data in real time. This study acquires necessary data through IoT (Internet of Things) from the viewpoint of monitoring for environmental management of architectural spaces, converts them into a database, learns deep learning, and then supports anomaly patterns using AI (Artificial Infelligence) deep learning-based anomaly detection. We propose an implementation process. The results of this study suggest an effective environmental anomaly pattern detection solution architecture for environmental management of architectural spaces, proving its feasibility. The proposed method enables quick response through real-time data processing and analysis collected from IoT. In order to confirm the effectiveness of the proposed method, performance analysis is performed through prototype implementation to derive the results.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.21-30
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• 2023

The study proposes a model that utilizes Python-based deep learning text classification techniques to detect the legality of illegal financial advertising posts on the internet. These posts aim to promote unlawful financial activities, including the trading of bank accounts, credit card fraud, cashing out through mobile payments, and the sale of personal credit information. Despite the efforts of financial regulatory authorities, the prevalence of illegal financial activities persists. By applying this proposed model, the intention is to aid in identifying and detecting illicit content in internet-based illegal financial advertisining, thus contributing to the ongoing efforts to combat such activities. The study utilizes convolutional neural networks(CNN) and recurrent neural networks(RNN, LSTM, GRU), which are commonly used text classification techniques. The raw data for the model is based on manually confirmed regulatory judgments. By adjusting the hyperparameters of the Korean natural language processing and deep learning models, the study has achieved an optimized model with the best performance. This research holds significant meaning as it presents a deep learning model for discerning internet illegal financial advertising, which has not been previously explored. Additionally, with an accuracy range of 91.3% to 93.4% in a deep learning model, there is a hopeful anticipation for the practical application of this model in the task of detecting illicit financial advertisements, ultimately contributing to the eradication of such unlawful financial advertisements.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8908-8914
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• 2015

The Internet continues to grow and develop, but there are going to generate a variety of Internet attacks that exploit it. In the initial Internet environment, the attackers maliciously exploited Internet environments for ostentations and hobbies. but these days many malicious attempts purpose the financial gain so systematic and sophisticated attacks that are associated with various crimes are occurred. The structures, such as viruses and worms were present in the form of one source multi-target before. but recently, APT(Advanced Persistent Threat, intelligent continuous attacks) in the form of multi-source single target is dealing massive damage. The performance evaluation analyzed whether to generate audit data and detect integrity infringement, and false positives for normal traffic, process detecting and blocking functions, and Agent policy capabilities with respect to the application availability.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.259-278
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• 2022

With the spread of Artificial Intelligence (AI), various AI-based services are expanding in the financial sector such as service recommendation, automated customer response, fraud detection system(FDS), credit scoring services, etc. At the same time, problems related to reliability and unexpected social controversy are also occurring due to the nature of data-based machine learning. The need Based on this background, this study aimed to contribute to improving trust in AI-based financial services by proposing a checklist to secure fairness in AI-based credit scoring services which directly affects consumers' financial life. Among the key elements of trustworthy AI like transparency, safety, accountability, and fairness, fairness was selected as the subject of the study so that everyone could enjoy the benefits of automated algorithms from the perspective of inclusive finance without social discrimination. We divided the entire fairness related operation process into three areas like data, algorithms, and user areas through literature research. For each area, we constructed four detailed considerations for evaluation resulting in 12 checklists. The relative importance and priority of the categories were evaluated through the analytic hierarchy process (AHP). We use three different groups: financial field workers, artificial intelligence field workers, and general users which represent entire financial stakeholders. According to the importance of each stakeholder, three groups were classified and analyzed, and from a practical perspective, specific checks such as feasibility verification for using learning data and non-financial information and monitoring new inflow data were identified. Moreover, financial consumers in general were found to be highly considerate of the accuracy of result analysis and bias checks. We expect this result could contribute to the design and operation of fair AI-based financial services.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.1119-1127
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• 2020

This study aims to identify the determinants of student interest in pursuing Accounting Professional Education (Indonesia: PPAk) in Province of Bali. The determinants which the author has used are: independent variables are social motivation, career motivation, quality motivation, and duration of education. The sample in this study comprises of 75 respondents who are students of Accounting discipline at the Faculty of Economics and Business at universities in the Province of Bali. Data was collected using a questionnaire and have been processed using multiple regression analysis. The results show that social motivation, career motivation, and quality motivation have a positive effect on students' interest in studying Accounting Professional Education. On the other hand, the duration of the accounting course has a negative effect on students' interest in studying this program. The importance of role of a professional accountant in realizing transparency in public life, and an economy that is free from financial deceit and fraud makes the role of professional accounting institutions very important. However, graduates' who desire to continue their studies in the PPAk program tends to be low. The findings of this research are expected to become the basis for policy makers in formulating rules related to the development of the accounting profession in the society, especially in Indonesia.

• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.291-301
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• 2018

The purpose of this study is to investigate the factors that affect the perceived awareness and the intention of continuous use by FinTech users and system characteristics. Data collection was carried out by targeting and surveying 600 people living in Gwangju, and office workers using smartphones. As a result, first, self-efficacy, innovation, and fitness for Fin-Tech services were found to influence the degree of perceptual awareness and intent to use of Fin-tech service users. Second, the system characteristics have a positive effect on perceived awareness and intention of using FinTech service. Third, the hypothesis about the dangers in the user attributes and system properties were dismissed. It seems that the priority concern was regarding the leakage of personal information and security as privacy and the increasing damage cases of financial fraud by electronic financial transactions spill. Therefore, in order to spread FinTech services, it would be effective if a Fin-Tech service strategy could eliminate inconveniences such as the risk of hindering convenience and intention to use by the marketing strategy established by the company.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.1
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• pp.25-38
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• 2022

With the diversification of payment methods and games, related financial accidents are causing serious problems for users and game companies. Recently, game companies have introduced an Fraud Detection System (FDS) for game payment systems to prevent financial incident. However, FDS is ineffective and cannot provide major evidence based on judgment results, as it requires constant change of detection patterns. In this paper, we analyze abnormal transactions among payment log data of real game companies to generate related features. One of the unsupervised learning models, Autoencoder, was used to build a model to detect abnormal transactions, which resulted in over 85% accuracy. Using X-FDS (Explainable FDS) with XAI-SHAP, we could understand that the variables with the highest explanation for anomaly detection were the amount of transaction, transaction medium, and the age of users. Based on X-FDS, we derive an improved detection model with an accuracy of 94% was finally derived by fine-tuning the importance of features that adversely affect the proposed model.

• Journal of the Korea Convergence Society
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• v.6 no.1
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• pp.85-91
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• 2015

Recently government has distributed precautionary measure and response procedures for smishing(SMS phishing), pharming, phishing, memory hacking and intensified Electronic Financial Transaction Act because of the sharp increase of electronic bank frauds. However, the methods of electronic bank frauds also developed and changed accordingly so much it becomes hard to cope with them. In contrast to earlier voice phishing targeted randomizing object, these new methods find out the personal information of targets and analyze them in detail making a big data base. And they are progressed into new kind of electronic bank frauds using those analyzed informations for voice phishing. This study analyze the attack method of voice phishing blended with the Big Data of personal informations and suggests response procedures for electronic bank frauds increasingly developed. Using the method to save meaningless data in a memory, attackers cannot deduct accurate information and try voice phishing properly even though they obtain personal information based on the Big Data. This study analyze newly developed social technologic attacks and suggests response procedures for them.

• The Journal of the Korea Contents Association
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• v.12 no.7
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• pp.176-182
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• 2012

Voice Phishing finds out personal information illegally using electrification and it is confidence game that withdraw deposit on the basis of this. It appeared by new social problem as damage instances increase rapidly. Target of the damage is invading indiscriminately to good civilian and is crime that commit by foreigners such as a most Chinese, Formosan. Voice Phishing can be crime type of new form in terms of criminal practice is achieved in the foreign countries. Therefore, this study wishes to analyze present occurrence actual conditions and example, and search effective confrontation plan regarding Voice Phishing. Voice Phishing criminal offense is growing as crime is not eradicated in spite of continuous public relations and control, and technique is diversified and specializes preferably. Hereafter, confrontation plan about problem may have to be readied in banking communication investigation to eradicate Voice Phishing. Also, polices control activity may have to be reinforce through quick investigation's practice and development of investigation technique, and relevant government ministry and international mutual assistance cooperation such as the Interpol should be reinforced because is shown international crime personality.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.123-131
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• 2016

Process mining in big data environment utilize a number of data were generated from the business process. It generates lots of knowledge and insights regarding implementation and improvement of the process through the event log of the company's enterprise resource planning (ERP) system. In recent years, various research activities engaged with the audit work of company organizations are trying actively by using the maximum strength of the mining process. However, domestic studies on applicable sales auditing system for the process mining are insufficient under big data environment. Therefore, we propose process-mining methods that can be optimally applied to online and traditional auditing system. In advance, we propose continuous monitoring information system that can early detect and prevent the risk under the big data environment by monitoring risk factors in the organizations of enterprise. The scope of the research of this paper is to design a pre-verification system for risk factor via practical examples in sales auditing. Furthermore, realizations of preventive audit, continuous monitoring for high risk, reduction of fraud, and timely action for violation of rules are enhanced by proposed sales auditing system. According to the simulation results, avoidance of financial risks, reduction of audit period, and improvement of audit quality are represented.