• 한국IT서비스학회지
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• 제6권2호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권3호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권12호
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• pp.3218-3241
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• 2023

Financial fraud undermines the sustainable development of financial markets. Financial statements can be regarded as the key source of information to obtain the operating conditions of listed companies. Current research focuses more on mining financial digital data instead of looking into text data. However, text data can reveal emotional information, which is an important basis for detecting financial fraud. The audit opinion of the financial statement is especially the fair opinion of a certified public accountant on the quality of enterprise financial reports. Therefore, this research was carried out by using the data features of 4,153 listed companies' financial annual reports and audits of text opinions in the past six years, and the paper puts forward a financial fraud detection model integrating audit opinions. First, the financial data index database and audit opinion text database were built. Second, digitized audit opinions with deep learning Bert model was employed. Finally, both the extracted audit numerical characteristics and the financial numerical indicators were used as the training data of the LightGBM model. What is worth paying attention to is that the imbalanced distribution of sample labels is also one of the focuses of financial fraud research. To solve this problem, data enhancement and Focal Loss feature learning functions were used in data processing and model training respectively. The experimental results show that compared with the conventional financial fraud detection model, the performance of the proposed model is improved greatly, with Area Under the Curve (AUC) and Accuracy reaching 81.42% and 78.15%, respectively.

• Asia pacific journal of information systems
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• 제28권4호
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• pp.308-319
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• 2018

This paper aims at providing valuable insights on Financial Fraud Detection on a mobile money transactional activity. We have predicted and classified the transaction as normal or fraud with a small sample and massive data set using Azure and Spark ML, which are traditional systems and Big Data respectively. Experimenting with sample dataset in Azure, we found that the Decision Forest model is the most accurate to proceed in terms of the recall value. For the massive data set using Spark ML, it is found that the Random Forest classifier algorithm of the classification model proves to be the best algorithm. It is presented that the Spark cluster gets much faster to build and evaluate models as adding more servers to the cluster with the same accuracy, which proves that the large scale data set can be predictable using Big Data platform. Finally, we reached a recall score with 0.73, which implies a satisfying prediction quality in predicting fraudulent transactions.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.1-10
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• 2024

In the era of big data, the growth of e-commerce transactions brings forth both opportunities and risks, including the threat of data theft and fraud. To address these challenges, an automated real-time fraud detection system leveraging machine learning was developed. Four algorithms (Decision Tree, Naïve Bayes, XGBoost, and Neural Network) underwent comparison using a dataset from a clothing website that encompassed both legitimate and fraudulent transactions. The dataset exhibited an imbalance, with 9.3% representing fraud and 90.07% legitimate transactions. Performance evaluation metrics, including Recall, Precision, F1 Score, and AUC ROC, were employed to assess the effectiveness of each algorithm. XGBoost emerged as the top-performing model, achieving an impressive accuracy score of 95.85%. The proposed system proves to be a robust defense mechanism against fraudulent activities in e-commerce, thereby enhancing security and instilling trust in online transactions.

• 한국정보시스템학회지:정보시스템연구
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• 제18권4호
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• pp.41-57
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• 2009

Although financial information is a great influence upon determining of the group which use them, detection of management fraud and earning manipulation is a difficult task using normal audit procedures and corporate credit evaluation processes, due to the shortage of knowledge concerning the characteristics of management fraud, and the limitation of time and cost. These limitations suggest the need of systemic process for !he effective risk of earning manipulation for credit evaluators, external auditors, financial analysts, and regulators. Moot researches on management fraud have examined how various characteristics of the company's management features affect the occurrence of corporate fraud. This study examines financial characteristics of companies engaged in fraudulent financial reporting and suggests a model and system for detecting GAAP violations to improve reliability of accounting information and transparency of their management. Since the detection of management fraud has limited proven theory, this study used the detecting method of outlier(upper, and lower bound) financial ratio, as a real-field application. The strength of outlier detecting method is its use of easiness and understandability. In the suggested model, 14 variables of the 7 useful variable categories among the 76 financial ratio variables are examined through the distribution analysis as possible indicators of fraudulent financial statements accounts. The developed model from these variables show a 80.82% of hit ratio for the holdout sample. This model was developed as a financial outlier detecting system for a financial institution. External auditors, financial analysts, regulators, and other users of financial statements might use this model to pre-screen potential earnings manipulators in the credit evaluation system. Especially, this model will be helpful for the loan evaluators of financial institutes to decide more objective and effective credit ratings and to improve the quality of financial statements.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.200-206
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• 2021

Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models.

• 지능정보연구
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• 제21권1호
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• pp.161-177
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• 2015

정보기술의 빠른 진화, 빅데이터의 등장, 분석기법의 고도화 등으로 인해 다량의 데이터로부터 의미있는 정보를 추출하는 데이터마이닝을 다양한 영역에 활용하고자 하는 시도들이 활발히 진행되고 있다. 그 중의 한 분야가 농산물 유통영역인데, 농산물에 대한 지속적인 수요 증가와 전자경매의 활성화 등으로 수도권 농산물 도매시장에서만도 연간 수천만건 이상의 거래가 이루어 진다. 그러나 급속한 거래량 증가와 더불어 과거로부터 관행적으로 이루어지고 있는 부정거래도 함께 증가하고 있는데 거래참가자들 사이의 결탁에 의해 발생하는 농산물 도매시장의 부정거래는 점차 지능화되는 추세이며, 이들을 감지하고 적발하기가 매우 어려운 실정이다. 이로 인해 농산물 유통환경의 공정거래 질서는 침해되고 시장에 대한 신뢰는 훼손되곤 한다. 따라서 거래투명성을 제고하고 유통비리를 구조적으로 개선하기 위한 과학적이고 자동화된 부정탐지시스템의 필요성이 어느 때보다도 절실히 요구되는 상황이다. 본 연구에서는 데이터마이닝의 의사결정나무를 이용하여 실제 발생하지 않은 거래를 실물 없이 거래한 것처럼 조작하여 대금을 정산하는 행위인 허위거래를 탐지하는 모형을 제시하였다. 이를 위해 실제 농산물 도매시장의 데이터를 수집하였고, 데이터의 정제 및 표준화 등의 선행작업을 수행하였다. 또한 변수 간의 상관관계 및 분포도 분석 등을 통해 데이터의 특성을 파악한 후 예측모형을 구축하여 허위거래와 정상거래를 분류하는 패턴을 도출하였으며, 최종적으로 시험용 데이터를 이용하여 모형을 평가하는 단계를 거쳐 결과의 적합성을 확인하였다. 향후 데이터마이닝을 이용한 부정탐지 모형을 허위거래뿐만 아니라 낙찰부정, 경매조작 등과 같이 다양화되는 부정거래에 적용하게 되면 보다 지대한 효과를 거둘 수 있으리라 사료된다.

• 디지털융복합연구
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• 제20권2호
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• pp.241-250
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• 2022

COVID-19 여파로 인한 비대면 서비스와 가정 재정 불안정성의 증가로 이륜차 보험사기 발생이 예상되고 있다. 이와 함께 보험사기 수법도 갈수록 교묘해지고 있다. 하지만 비대면 배달 수요와 연관된 이륜차 교통사고와 보험사기 적발 모형 관련 연구는 매우 미흡한 실정이다. 이에 본 연구는 보험사기의 표본 편중문제를 해결하기 위해 균형 랜덤포레스트 알고리즘을 이용하고 보험사기 조사 전문가의 정성적인 판단 기준을 반영한 변수를 모델에 포함하여 적용성을 향상시키며 적발력 높은 이륜차 보험사기 모형을 개발하고자 한다. 보험사기 적발 모형 개발 결과, 기존의 비균형 랜덤 포레스트 모형에 비해 균형 랜덤 포레스트가 보험 사기혐의자를 분류하는 데 있어 통계적으로 우수한 점을 확인할 수 있었다. 특히, 총 26개의 변수를 토대로 탐색적 변수 조합을 적용한 모형의 예측 성능이 가장 높았지만 일부 변수만을 사용한 확인적 모형의 예측 성능도 크게 떨어지지 않은 와중에, 정성적인 보험사기 전문가가 선정한 변수만을 사용한 확인적 모형은 예측력이 떨어지는 것을 확인하였다. 또한, 총 26개의 변수 중 운전자 성별, 연령, 운전자 피보험자 일치 여부, 미수선 청구금액, 대인보험금 등이 중요한 변수로 확인되어 이를 활용해 이륜차 보험사기 혐의자 선별을 위한 적극적인 대처가 필요해 보인다.

• 정보보호학회논문지
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• 제29권1호
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• pp.149-164
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• 2019

Digital Transformation과 4차 산업혁명 등 변화의 시대에 급변하는 기술 변화에 맞게 전자금융서비스는 안전하게 제공하여야 한다. 그러나 전기통신금융사기(보이스피싱) 사고는 현재진행형 이어서 사고의 지속적 증가, 지능화 및 고도화 현상을 대응하려 법률 제 개정 및 정책 제도 개선등 사고 근절을 위해 다양한 노력을 기울이고 있다. 더불어 금융회사는 이상금융거래탐지 시스템 개선 및 고도화를 통한 전기통신금융사기 사고 방지에 노력하고 있으나, 그 대응 결과는 그리 밝지 않다. 이러한 노력에도 불구하고 전기통신금융사기 사고는 관련 대책에 맞서 변화하며 진화를 거듭하고 있다. 본 연구에서는 보이스피싱에 의한 금융거래 사고발생 방지를 위해 시나리오 기반의 Rule 모델과 인공지능 알고리즘을 통해 모델링 된 지능형 이상금융거래 시스템을 설계하고 금융기관의 전자금융거래 시스템 에 실제 설치 운용해 본 결과를 바탕으로 인공지능형 이상금융거래 탐지시스템의 구현 모델과 분석 탐지 결과를 차단 대응 할 수 있는 고도화 된 대응 모델을 제안하고자 한다.