의사결정나무를 이용한 이상금융거래 탐지 정규화 방법에 관한 연구

Abstract

ABSTRACT Ever sophisticated e-finance fraud techniques have led to an in creasing number of reported phishing incidents. Financial authorities, in response, have recommended that we enhance exis ting 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 th rough 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 compa red the results of our decision tree-normalized detection rules with the results of a sequential detection and confirmed the ef ficiency of our methods.Keywords: Fraud Detection System, Banking System, e-finance accident, Dec ision Tree, Normalization