# Boosting Fraud Detection in Mobile Payment with Prior Knowledge
## 1 Introduction  
Mobile e-commerce has developed rapidly in recent years, and the number of mobile transactions in China reached 101 billion accounting for 347 trillion yuan in 2019, increasing by 67.57% and 25.13%, respectively [1]. The marketing costs also increase synchronously in mobile ecommerce. Taking Taobao 11.11 and UnionPay 6.2 promotions as representatives, large enterprises spend billions of dollars each year in mobile marketing. Additionally, financial fraud has become more and more common in the context of mobile payments.  
Large numbers of mobile robots emerge with manipulated smartphones and even professional devices [2; 3]. Robots simulate operational behaviors of normal customers to grab the marketing resources with high-speed scripts, which greatly affect the market fairness and seriously weaken the actual effect of commercial promotion. Some of the professional devices used by the underground industry are shown in Figure 1.  
Traditional machine learning models face some problems in fraud detection of mobile payment. On one hand, the machine learning models are facing a shortage of qualified training data as fraud transaction samples are inadequate. On the other hand, although massive human knowledge is accumulated in daily enterprise system operation, most machine learning models do not allow for the direct incorporation of prior knowledge. It becomes a difficult problem in the contexts of technology and engineering for mobile payment applications to distinguish robotic automation from normal user operations.  
In this paper, we propose an extension to boosting algorithms that combine human knowledge with training data in fraud detection of mobile payment transactions. We collect raw data of payment transactions from mobile terminals with a widely deployed APP under user authorization, extract different features from the raw data, label 31,500 payment transactions in 14 days as datasets, and then train and test the extended model with the datasets. Based on our test, the extended model shows better performance than standard boosting model, and the experiment acquires an accuracy rate of 98.7% and a recall rate of 94.8% in prediction.  
We performed further analysis on the differences in dominant features between fraudulent transactions and normal transactions. We discovered some common behavior patterns of robotic automation, including less spatial motion detected by device sensors, higher IP group-clustering ratio, higher device jailbroken rate, more irregular device names and fewer IP address changes. The quantitative analysis result is helpful for APP developers and service providers to understand and prevent fraud transactions from robotic automation.  
The rest of the paper is organized as follows: We enumerate related works in Section 2, elaborate on data collection and feature extraction in Section 3, explain machine learning models in Section 4, and then describe the training process and the experimental results of our proposed model in Section 5. We further investigate behavior patterns of robots in Section 6, and finally discuss several open issues in Section 7 and conclude in Section 8.