Machine Learning based Approach to Financial Fraud Detection

Process in Mobile Payment System

Dahee Choi and Kyungho Lee⁺

CIST, Korea University, Seoul, Korea

{shoodol00, kevinlee}@korea.ac.kr

Abstract

Mobile payment fraud is the unauthorized use of mobile transaction through identity theft or credit card stealing to fraudulently obtain money. Mobile payment fraud is the fast growing issue through the emergence of smartphone and online transition services. In the real world, highly accurate process in mobile payment fraud detection is needed since financial fraud causes financial loss. Therefore, our approach proposed the overall process of detecting mobile payment fraud based on machine learning, supervised and unsupervised method to detect fraud and process large amounts of financial data. Moreover, our approach performed sampling process and feature selection process for fast processing with large volumes of transaction data and to achieve high accuracy in mobile payment detection. F-measure and ROC curve are used to validate our proposed model.

Keywords: Machine Learning, Mobile Payment Fraud, Financial Fraud Detection, Semi-Supervised

Method, Feature Selection

+: Corresponding author: Kyungho Lee
Center for Information Security Technologies, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, Korea,
Tel: +81-2180-3937

IT Convergence Practice (INPRA), Vol. 5, No. 4, pp. 12-24, December 2017 [pdf]