• Journal of Information Technology Services
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• v.6 no.2
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• pp.1-18
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• 2007

Many researches have been done in insurance fraud as the amount and frequency of insurance fraud have been increasing continuously. In particular, the development of insurance fraud detection system using large database management techniques including data mining or link analysis based on visual method have been the main research topic in insurance fraud. However, this kinds of detection system were very ineffective to find unintentional insurance fraud happened by accident even though it was so good to find intentional and organized crime insurance fraud. Therefore, this research suggests insurance fraud as an ethical decision making and applies TPB(Theory of Planned Behavior) for the finding of reasons and prevention strategies of unintentional insurance fraud happened by accident. The results of research show that TPB is very appropriate model to explain the behavior of insurance fraud and that insurance agents force to do insurance fraud as affecting perceived behavior control. Therefore, education and pubic relations for insurance fraud are very effective for preventing insurance fraud and developing insurance service industry.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.117-127
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• 2020

Recently, proving insurance fraud has become increasingly difficult because it occurs intentionally and secretly via organized and intelligent conspiracy by specialists such as medical personnel, maintenance companies, insurance planners, and insurance subscribers. In the case of car accidents, it is difficult to prove intentions; in particular, an insurance company with no investigation rights has practical limitations in proving the suspicions. This paper aims reveal that the detection of organized and conspired insurance fraud, which had previously been difficult, could be dramatically improved through conspiring insurance fraud detection modeling using social network analysis and visualization of the relation between suspected group entities and by seeking developmental research possibilities of data analysis techniques.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.241-250
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• 2022

Due to the COVID-19 pandemic, with increased 'untact' services and with unstable household economy, the bike insurance fraud is expected to surge. Moreover, the fraud methodology gets complicated. However, the fraud detection model for bike insurance is absent. we deal with the issue of skewed class distribution and reflect the criterion of fraud detection expert. We utilize a balanced random-forest algorithm to develop an efficient bike insurance fraud detection model. As a result, while the predictive performance of balanced random-forest model is superior than it of non-balanced model. There is no significant difference between the variables used by the experts and the confirmatory models. The important variables to detect frauds are turned out to be age and gender of driver, correspondence between insured and driver, the amount of self-repairing claim, and the amount of bodily injury liability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.1006-1027
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• 2022

In the crowdfunding market, various crowdfunding platforms can offer founders the possibilities to collect funding and launch someone's next campaign, project or events. Especially, healthcare crowdfunding is a field that is growing rapidly on health-related problems based on online platforms. One of the largest platforms, GoFundMe, has raised US$ 5 billion since 2010. Unfortunately, while providing crucial help to care for many people, it is also increasing risk of fraud. Using the largest platform of crowdfunding market, GoFundMe, we conduct an exhaustive search of detection on fraud from October 2016 to September 2019. Data sets are based on 6 main types of medical focused crowdfunding campaigns or events, such as cancer, in vitro fertilization (IVF), leukemia, health insurance, lymphoma and, surgery type. This study evaluated a detect of fraud process to identify fraud from non-fraud healthcare crowdfunding campaigns using various machine learning technics.

• Korean Security Journal
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• no.53
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• pp.37-59
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• 2017

This study aims to contribute to the early detection of occupational fraud in the Korean financial industry by analyzing fraud symptoms. Firstly, the definition, cause of occupational fraud, and fraud symptoms were discussed through literature review. Secondly, survey data were collected from the employees of the financial industry such as bank, insurance, and securities companies to conduct statistical analysis. The result of analysis showed that the symptoms of 'excessive stock investment' and 'unsettled life style' were statistically significant predictors of fraud detection experience. Plus, 'tips and complaints' were the most frequent method for detecting occupational fraud in the Korean financial industry. The financial institutions can minimize the loss of occupational fraud by early detection through educating their employees and vendors on these important symptoms of occupational fraud.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.125-131
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• 2021

Detecting fraudulent insurance claims is difficult due to small and unbalanced data. Some research has been carried out to better cope with various types of fraudulent claims. Nowadays, technology for detecting fraudulent insurance claims has been increasingly utilized in insurance and technology fields, thanks to the use of artificial intelligence (AI) methods in addition to traditional statistical detection and rule-based methods. This study obtained meaningful results for a fraudulent insurance claim detection model based on machine learning (ML) and deep learning (DL) technologies, using fraudulent insurance claim data from previous research. In our search for a method to enhance the detection of fraudulent insurance claims, we investigated the reinforcement learning (RL) method. We examined how we could apply the RL method to the detection of fraudulent insurance claims. There are limited previous cases of applying the RL method. Thus, we first had to define the RL essential elements based on previous research on detecting anomalies. We applied the deep Q-network (DQN) and double deep Q-network (DDQN) in the learning fraudulent insurance claim detection model. By doing so, we confirmed that our model demonstrated better performance than previous machine learning models.

• The Korean Society of Law and Medicine
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• v.13 no.1
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• pp.41-69
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• 2012

First introduced in 1977, Korean health care system reached to national coverage in short period of time never seen before in any other countries, and rated as successful system protecting the health of the public at relatively low price. However, despite those positive evaluations, some of fraudulent medical organizations or pharmacies are hindering the sound development of the national health care system with meticulous false billing exaggerating the number of patients or the days of their treatment. To prevent aforementioned nursing home fraud and false billing, the misconduct should be punished as subject to the criminal law and severally punished for fines and payments which far exceed the expected amount of illicit gains as it is basically violation of criminal fraud, other than the forced return of illicit gains based on civil laws. Furthermore, the Health Insurance Review and Assessment Service should strengthen and complement the fraud investigators, the review process, and the professional training to raise the detection rates. It might also want to review ways to implement whistleblower rewarding system and rewards for evidences of healthcare fraud to overcome the limits of external review.

• Journal of Korea Trade
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• v.24 no.1
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• pp.59-81
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• 2020

Purpose - This study analyzes the scope of due diligence and risks of banks and K-Sure in trade finance covered by EFF focusing on Moneual case, one of the latest and biggest trade finance fraud cases in Korea. Also, we suggest anti-fraud measures in trade finance on the part of banks and K-Sure in order to give them a desirable way of due diligence and reasonable risk management of export insurance. Design/methodology - Based on Moneual case of trade finance fraud, this study employs the methodology of an extended literature review and analysis of court decisions. Findings - Seoul High Court of Korea failed to decide whether K-Sure was wholly obliged to pay the insurance against the banks' EFF claims, but issued a compulsory mediation order, judging that both the banks and K-Sure were responsible by 50:50. The court may have judged that both the parties had lacked their due diligence in the trade finance. It is quite difficult for trade finance providers to manually investigate whether the transaction is suspected of trade finance fraud, so digitalization of trade finance which can facilitate the prevention and detection of trade fraud needs to be realized quickly. Since there has been no international rule available for open account trade finance up till now, clearly stipulated EFF terms on the exporter's genuine export obligation might have protected K-Sure from the disaster. Originality/value - This study investigates the due diligence of the banks and K-Sure in Moneual case which few researchers have considered, to the best of our knowledge. This study also suggests several practical methods (including block chain) to prevent complicating trade finance fraud amid increasing use of an open account, and further offers reasonable risk management of EFF employing international factoring rule which is also related to problematic open account trade finance.

• 한국IT서비스학회:학술대회논문집
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• 2009.05a
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• pp.357-360
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• 2009

본 연구는 데이터마이닝 기법을 이용하여 건강보험청구료에 있어서 이상정도가 심한 요양기관을 탐지하고, 실제 의료영역에 적용하기 위한 시스템 개발을 목적으로 한다. 현재 건강보험 심사평가원의 이상탐지시스템은 평가대상이 되는 항목을 개별적으로 평가하고, 탐지된 기관의 선정 이유에 대한 근거제시가 부족한 단점을 가지고 있다. 따라서 본 연구에서는 항목을 종합적으로 평가할 수 있는 정량적 지표를 설계하고, 항목들의 상대적 중요도를 파악할 수 있도록 항목들에 대한 가중치 부여한다. 또한 지표에서 얻어진 값으로 등급을 구분하고, 의사결정나무기법(decision tree)를 이용하여 해석력을 높이는 방법을 제시한다.

• Journal of Digital Convergence
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• v.11 no.10
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• pp.593-608
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• 2013

According to increasing number of injury claims, the challenge is reducing investigation of cases of injuries by selecting them more delicately, while also increasing the redemption rates and the amount of restitution. In this regards, we developed the fraud detection model for injury claims of self-employed insured by using decision tree after collecting medical claim data from 2006 to 2011 of the National Health Insurance in Korea. As a result of this model, subject types were classified into 18 types. If applying these types to the actual survey compared with if not applying, the redumption collecting rate will be increasing by 12.8%. Also, the effectiveness of this model will be maximize when the number of claims handlers considering their survey volume and management plans are examined thoroughly.