文件名称：Fraud Detection Machine Learning
文件大小：9.56MB
文件格式：PDF
更新时间：2021-03-18 16:28:55
Fraud Detection ML
基于非监督学习的欺诈识别 摘要： Fraud is a threat that most online service providers must address in the development of their systems to ensure an efficient security policy and the integrity of their revenue. Amadeus, a Global Distribution System providing a trans- action platform for flight booking by travel agents, is tar- geted by fraud attempts that could lead to revenue losses and indemnifications. The objective of this thesis is to detect fraud attempts by applying machine learning algorithms to bookings repre- sented by Passenger Name Record history. Due to the lack of labelled data, the current study presents a benchmark of unsupervised algorithms and aggregation methods. It also describes anomaly detection techniques which can be applied to self-organizing maps and hierarchical clustering. Considering the important amount of transactions per second processed by Amadeus back-ends, we eventually highlight potential bottlenecks and alternatives.