A Real-Time Fraud Detection Algorithm Based on Usage Amount Forecast

• 作者: Haizhen Jiao Kaile Xiao Kun Niu Nanjie Deng Zhipeng Gao
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 社会科学
• 关键词: REAL-TIME FRAUD Detection USAGE AMOUNT FORECAST TELECOM industry
• 年，卷（期）: 2016,（1）
• 页码范围: 25-26
• 页数: 2页
• 分类号: C5

摘要

Real-time Fraud Detection has always been a challenging task, especially in financial, insurance, and telecom industries. There are mainly three methods, which are rule set, outlier detection and classification to solve the problem. But those methods have some drawbacks respectively. To overcome these limitations, we propose a new algorithm UAF (Usage Amount Forecast).Firstly, Manhattan distance is used to measure the similarity between fraudulent instances and normal ones. Secondly, UAF gives real-time score which detects the fraud early and reduces as much economic loss as possible. Experiments on various real-world datasets demonstrate the high potential of UAF for processing real-time data and predicting fraudulent users.