• 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.

• TTA Journal
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• s.172
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• pp.39-45
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• 2017

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.876-878
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• 2019

본 연구에서는 금융 분야에서 AI 기술을 이용하여 챗봇 기반의 예측 시스템을 구축하는데 목적이 있다. 사용자가 이해하기 쉽게 챗봇 기반으로 실시간 서비스를 제공하며 투자 경험이 없는 사용자를 타겟으로 투자 추천을 하는 것을 목표로 개발하였다. 챗봇 기반의 금융 어플리케이션에서는 종목 주가조회, 코스피 상위 조회, 예측결과 조회, ELS상품추천 등으로 크게 네 가지의 의도파악을 하며 자연어 처리와 단어 매칭 처리를 통해서 사용자에게 최적화된 정보를 제공한다. 정보의 질을 높이기 위해서 인공지능 학습은 10년 치의 데이터를 학습시켰으며 비슷한 패턴을 예측해서 제공한다. 상장기업의 주식과 은행에서 판매하는 ELS를 추천하고 있으며, 챗봇 서비스를 통해 사용자와 실시간적으로 소통할 수 있는 AI기반의 금융 시스템을 제공한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.1
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• pp.149-164
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• 2019

Digital Transformation and the Fourth Industrial Revolution, electronic financial services should be provided safely in accordance with rapidly changing technology changes in the times of change. However, telecommunication finance fraud (voice phishing) accidents are currently ongoing, and various efforts are being made to eradicate accidents such as legal amendment and improvement of policy system in order to cope with continuous increase, intelligence and advancement of accidents. In addition, financial institutions are trying to prevent fraudulent accidents by improving and upgrading the abnormal financial transaction detection system, but the results are not very clear. Despite these efforts, telecommunications and financial fraud incidents have evolved to evolve against countermeasures. In this paper, we propose an intelligent over - the - counter financial transaction system modeled through scenario - based Rule model and artificial intelligence algorithm to prevent financial transaction accidents by voice phishing. We propose an implementation model of artificial intelligence abnormal financial transaction detection system and an optimized countermeasure model that can block and respond to analysis and detection results.

• Journal of Convergence for Information Technology
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• v.11 no.3
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• pp.20-33
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• 2021

This study is to suggest improvements of personal information protection in South Korea, according to requiring the safety of process and protection of personal information. Accordingly, based on data collection and analysis through literature research, this study derived the issues and suitable standards of personal information for major artificial intelligence services. In addition, this cases studies were reviewed, focusing on the legal compliance and porcessing compliance for personal information proection in major countries. And it suggested the improvement plan applied in South Korea. As the results, in legal compliance, it is required reorganization of related laws, responsibility and compliance to develop and provide AI, and operation of risk management for personal information protection laws in AI services. In terms of processing compliance, first, in pre-processing and refining, it is necessary to standardize data set reference models, control data set quality, and voluntarily label AI applications. Second, in development and utilization of algorithm, it is need to establish and apply a clear regulation of the algorithm. As such, South Korea should apply suitable improvement tasks for personal information protection of safe AI service.

• The Journal of the Convergence on Culture Technology
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• v.5 no.1
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• pp.77-82
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• 2019

AI is applied increasingly to overall industries such as living, medical, financial service, autonomous car, etc. thanks to rapid technology development. AI-leading countries are strengthening their competency to secure competitiveness since AI is positioned as the core technology in $4^{th}$ Industrial Revolution. Although Korea has the competitive IT infra and human resources, it lags behind traditional AI-leaders like United States, Canada, Japan and, even China which devotes all its might to develop intelligent technology-intentive industry. AI is the critical technology influencing on the national industry in the near future according to advancement of intelligent information society so that concentration of capability is required with national interest. Also, joint development with global AI-leading companies as well as development of own technology are crucial to prevent technology subordination. Additionally, regulatory reform and preparation of related law are very urgent.

• Journal of the HCI Society of Korea
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• v.14 no.2
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• pp.61-69
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• 2019

Recently, as a new ICT trend, emerging chatbots are actively introduced in the field of finance. Chatbot conducts services through the interaction of communication with users. The purpose of this study is to investigate the effect of interaction dialogue type on the efficiency, usability, sensibility and perceived security of financial service chatbot. Based on theoretical considerations, I have divided into closed conversation, open conversation, and mixed conversation type based on the conversation style based on the implementation method of chatbot. Three types of Financial Chatbot prototypes were made and the experiments were conducted after account inquiry, account transfer, Q & A financial task execution. As a result of experimental research analysis, chatbot's interaction dialogue type was found to affect efficiency and usability. Users have shown that the interaction of closed conversations and mixed conversations is an intuitive interface that allows financial services to be easily manipulated without error. This study will be used as a resource to improve the user experience that requires deep understanding of financial chatbot users who should consider both the emotional element of artificial intelligence that provides services through natural conversation and the functional elements that perform financial business can be.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.71-90
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• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.268-270
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• 2023

기존 Web2.0 시대의 플랫폼 기업은 서비스를 통해 생성된 개인 데이터로 다양한 비즈니스를 창출해왔다. 하지만 데이터 제공자인 개인은 해당 수익에서 제외되는 모순된 상황에 놓였다. 이에 개인이 자신의 데이터를 적극 관리·통제하면서 능동적으로 활용할 수 있는 개념인 마이데이터(MyData)가 등장했다. 국내에서는 '20.8월 데이터3법(개인정보보호법, 신용정보법, 정보통신망법)이 통과되면서 신용정보법에 근거해 금융 분야 마이데이터 서비스가 활성화되기 시작했다. 그러나 현존하는 마이데이터 플랫폼은 중앙화된 시스템으로 본래 취지와 다르게 개인의 데이터 소유권과 통제권을 보장하기에 부족하다. 이에 본 논문에서는 기존 마이데이터 플랫폼의 한계점을 분석하고, Web3.0 등 변화하는 환경에서 개인의 데이터 주권을 보장하고, 데이터 가치를 공정하게 분배받을 수 있는 DID 플랫폼 기반의 마이데이터 서비스 모델을 제안한다.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.287-295
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• 2017

Since Google's AlphaGo defeated a world champion of Go players in 2016, there have been many interests in the deep learning. In the financial sector, a Robo-Advisor using deep learning gains a significant attention, which builds and manages portfolios of financial instruments for investors.In this paper, we have proposed the a deep learning algorithm geared toward identification and forecast of the KOSPI index direction,and we also have compared the accuracy of the prediction.In an application of forecasting the financial market index direction, we have shown that the Robo-Advisor using deep learning has a significant effect on finance industry. The Robo-Advisor collects a massive data such as earnings statements, news reports and regulatory filings, analyzes those and recommends investors how to view market trends and identify the best time to purchase financial assets. On the other hand, the Robo-Advisor allows businesses to learn more about their customers, develop better marketing strategies, increase sales and decrease costs.