• Title, Summary, Keyword: 이상 거래 탐지

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A Study on Improvement of Effectiveness Using Anomaly Analysis rule modification in Electronic Finance Trading (전자금융거래의 이상징후 탐지 규칙 개선을 통한 효과성 향상에 관한 연구)

  • Choi, Eui-soon;Lee, Kyung-ho
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.25 no.3
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    • pp.615-625
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    • 2015
  • This paper proposes new methods and examples for improving fraud detection rules based on banking customer's transaction behaviors focused on anomaly detection method. This study investigates real example that FDS(Fraud Detection System) regards fraudulent transaction as legitimate transaction and figures out fraudulent types and transaction patterns. To understanding the cases that FDS regard legitimate transaction as fraudulent transaction, it investigates all transactions that requied additional authentications or outbound call. We infered additional facts to refine detection rules in progress of outbound calling and applied to existing detection rules to improve. The main results of this study is the following: (a) Type I error is decreased (b) Type II errors are also decreased. The major contribution of this paper is the improvement of effectiveness in detecting fraudulent transaction using transaction behaviors and providing a continuous method that elevate fraud detection rules.

A Performance Comparison Study of Fraud Detection Techniques (이상거래 탐지 기법의 성능 비교 연구)

  • Kim, Minseok;Park, Sanghyun
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.738-741
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    • 2017
  • 금융 산업, IT 기술의 발전과 이를 융합한 핀테크 사업의 활성화에 따라 전자금융거래의 규모가 지속적으로 증가하고 있다. 이에 따라 다양한 사기 결제나 부정 결제의 위험도 증가하고 있다. 그래서 이러한 위험을 사전에 예방하기 위해 데이터 마이닝 기법을 이용한 이상거래 탐지 연구가 활발히 진행되고 있다. 본 연구에서는 데이터 마이닝을 이용한 이상거래 탐지 연구 동향을 살펴보고, 세부 응용 영역별(신용카드, 보험, 기타금융)로 최적의 성능을 보이는 기법을 비교 분석하였다. 이러한 연구의 결과는 이상거래 탐지 시스템에 대한 최신 연구 동향을 이해하고, 다양한 전자금융거래에 적용할 수 있는 범용(General-purpose) 이상거래 탐지 기술 연구에 큰 도움이 될 것으로 기대된다.

온라인 게임 결제 데이터 분석 기반의 이상거래 탐지 모델

  • Woo, Jiyoung;Kim, Hana;Kwak, Byung Il;Kim, Huy Kang
    • Review of KIISC
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    • v.26 no.3
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    • pp.38-44
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    • 2016
  • 소액결제에 대한 규제 완화로 이와 관련한 사기가 급증하고 있으며, 특히 소액결제가 대부분을 차지하는 온라인게임 산업은 관련 사기로 인한 피해가 증가하고 있다. 온라인 게임의 소액결제 사기는 단순히 금액에 대한 피해뿐만이 아니라 회사 브랜드에도 영향을 미치며, 나아가 고객 이탈로 이어질 수 있다. 소액결제 사기를 방지하기 위해 게임 산업에서도 이상거래 탐지 시스템이 요구되고 있다. 본 연구는 게임 사용자의 결제 패턴을 분석하여 이상거래를 탐지할 수 있는 머신러닝 기반의 이상거래 탐지 모델을 제시하며, 제안하는 모델을 글로벌 온라인 게임에 적용한 사례를 소개한다.

Fraud Detection System in Mobile Payment Service Using Data Mining (모바일 결제 환경에서의 데이터마이닝을 이용한 이상거래 탐지 시스템)

  • Han, Hee Chan;Kim, Hana;Kim, Huy Kang
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.26 no.6
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    • pp.1527-1537
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    • 2016
  • As increasing of smartphone penetration over the world, various mobile payment services have been emerged and fraud transactions have drastically increased. Although many financial companies have deployed security solutions to detect fraud transactions in on/off-line environment, mobile payment services still lack fraud detection solutions and researches. The mobile payment is mainly comprised of micro-payments and payment environment is different from other payments, so mobile-specialized fraud detection is needed. In this paper, we propose a FDS (Fraud Detection System) based on data mining for mobile payment services. The method of this paper is applied to the real data provided by a PG (Payment Gateway) company in Korea. The proposed FDS consists of two phases; (1) the first phase is focused on classifying transactions at high speed (2) the second is designed to detect abnormal transactions with high accuracy. We could detect 13 transactions per second with 93% accuracy rate.

Detecting Abnormalities in Fraud Detection System through the Analysis of Insider Security Threats (내부자 보안위협 분석을 통한 전자금융 이상거래 탐지 및 대응방안 연구)

  • Lee, Jae-Yong;Kim, In-Seok
    • The Journal of Society for e-Business Studies
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    • v.23 no.4
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    • pp.153-169
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    • 2018
  • Previous e-financial anomalies analysis and detection technology collects large amounts of electronic financial transaction logs generated from electronic financial business systems into big-data-based storage space. And it detects abnormal transactions in real time using detection rules that analyze transaction pattern profiling of existing customers and various accident transactions. However, deep analysis such as attempts to access e-finance by insiders of financial institutions with large scale of damages and social ripple effects and stealing important information from e-financial users through bypass of internal control environments is not conducted. This paper analyzes the management status of e-financial security programs of financial companies and draws the possibility that they are allies in security control of insiders who exploit vulnerability in management. In order to efficiently respond to this problem, it will present a comprehensive e-financial security management environment linked to insider threat monitoring as well as the existing e-financial transaction detection system.

Effective Normalization Method for Fraud Detection Using a Decision Tree (의사결정나무를 이용한 이상금융거래 탐지 정규화 방법에 관한 연구)

  • Park, Jae Hoon;Kim, Huy Kang;Kim, Eunjin
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.25 no.1
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    • pp.133-146
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    • 2015
  • Ever sophisticated e-finance fraud techniques have led to an increasing number of reported phishing incidents. Financial authorities, in response, have recommended that we enhance existing Fraud Detection Systems (FDS) of banks and other financial institutions. FDSs are systems designed to prevent e-finance accidents through real-time access and validity checks on client transactions. The effectiveness of an FDS depends largely on how fast it can analyze and detect abnormalities in large amounts of customer transaction data. In this study we detect fraudulent transaction patterns and establish detection rules through e-finance accident data analyses. Abnormalities are flagged by comparing individual client transaction patterns with client profiles, using the ruleset. We propose an effective flagging method that uses decision trees to normalize detection rules. In demonstration, we extracted customer usage patterns, customer profile informations and detection rules from the e-finance accident data of an actual domestic(Korean) bank. We then compared the results of our decision tree-normalized detection rules with the results of a sequential detection and confirmed the efficiency of our methods.

A Survey of Fraud Detection Research based on Transaction Analysis and Data Mining Technique (결제로그 분석 및 데이터 마이닝을 이용한 이상거래 탐지 연구 조사)

  • Jeong, Seong Hoon;Kim, Hana;Shin, Youngsang;Lee, Taejin;Kim, Huy Kang
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.25 no.6
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    • pp.1525-1540
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    • 2015
  • Due to a rapid advancement in the electronic commerce technology, the payment method varies from cash to electronic settlement such as credit card, mobile payment and mobile application card. Therefore, financial fraud is increasing notably for a purpose of personal gain. In response, financial companies are building the FDS (Fraud Detection System) to protect consumers from fraudulent transactions. The one of the goals of FDS is identifying the fraudulent transaction with high accuracy by analyzing transaction data and personal information in real-time. Data mining techniques are providing great aid in financial accounting fraud detection, so it have been applied most extensively to provide primary solutions to the problems. In this paper, we try to provide an overview of the research on data mining based fraud detection. Also, we classify researches under few criteria such as data set, data mining algorithm and viewpoint of research.

A Design of Mobile Fitness Recommendation System Based on Data Sharing Mechanism (실시간 이상거래 탐지 기법에 관한 연구)

  • Jang, Ki-Man;Kim, Kyung-Hwan;Choi, Kwang-Nam;Kim, Chang-Su;Jung, Hoe-Kyung
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.763-765
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    • 2015
  • The study is being conducted to ensure the transparency of research and development have identified the problems of the current system and improve the way out. Such a study about the subject that do not follow either outside the institutional system has a disadvantage compared to an unfulfilled. R & D in order to prevent the misuse and fraud enforcement shall detect abnormal transactions that occur from transactions between research institutions and credit card issuers in real time. In this paper, we propose a detection method for real-time transaction over. It is able to detect and respond fraudulent transactions that may occur in a variety of environments by adding the data obtained by the business rules to derive stopped making detection system.

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Outlier Detection Method for Mobile Banking with User Input Pattern and E-finance Transaction Pattern (사용자 입력 패턴 및 전자 금융 거래 패턴을 이용한 모바일 뱅킹 이상치 탐지 방법)

  • Min, Hee Yeon;Park, Jin Hyung;Lee, Dong Hoon;Kim, In Seok
    • Journal of Internet Computing and Services
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    • v.15 no.1
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    • pp.157-170
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    • 2014
  • As the increase of transaction using mobile banking continues, threat to the mobile financial security is also increasing. Mobile banking service performs the financial transaction using the dedicate application which is made by financial corporation. It provides the same services as the internet banking service. Personal information such as credit card number, which is stored in the mobile banking application can be used to the additional attack caused by a malicious attack or the loss of the mobile devices. Therefore, in this paper, to cope with the mobile financial accident caused by personal information exposure, we suggest outlier detection method which can judge whether the transaction is conducted by the appropriate user or not. This detection method utilizes the user's input patterns and transaction patterns when a user uses the banking service on the mobile devices. User's input and transaction pattern data involves the information which can be used to discern a certain user. Thus, if these data are utilized appropriately, they can be the information to distinguish abnormal transaction from the transaction done by the appropriate user. In this paper, we collect the data of user's input patterns on a smart phone for the experiment. And we use the experiment data which domestic financial corporation uses to detect outlier as the data of transaction pattern. We verify that our proposal can detect the abnormal transaction efficiently, as a result of detection experiment based on the collected input and transaction pattern data.

A Study on Implementation of Fraud Detection System (FDS) Applying BigData Platform (빅데이터 기술을 활용한 이상금융거래 탐지시스템 구축 연구)

  • Kang, Jae-Goo;Lee, Ji-Yean;You, Yen-Yoo
    • Journal of the Korea Convergence Society
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    • v.8 no.4
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    • pp.19-24
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    • 2017
  • The growing number of electronic financial transactions (e-banking) has entailed the rapid increase in security threats such as extortion and falsification of financial transaction data. Against such background, rigid security and countermeasures to hedge against such problems have risen as urgent tasks. Thus, this study aims to implement an improved case model by applying the Fraud Detection System (hereinafter, FDS) in a financial corporation 'A' using big data technique (e.g. the function to collect/store various types of typical/atypical financial transaction event data in real time regarding the external intrusion, outflow of internal data, and fraud financial transactions). As a result, There was reduction effect in terms of previous scenario detection target by minimizing false alarm via advanced scenario analysis. And further suggest the future direction of the enhanced FDS.