Consumer Fraud Detection via P-feature Conversion
Shanyan Lai, Junfang Wu, Zhiwei Ma, Chunyang Ye, Hui Zhou · 2021
The rapid development of tourism economy has brought new challenges such as the prevention of consumer fraud for smart city applications. Traditional fraud detection approaches such as telecommunication fraud and credit card fraud detection need a data set containing both the normal behavior and abnormal behavior. Therefore, they are incompetent to address such challenges in the tourism market where the data set is open and very few records of fraud behaviors are available. To address this issue, we propose a P-feature conversion algorithm to construct third-party features to expose the outliers. These features reveal the internal characteristics of different businesses and their useful internal connections. Then, we build a fraud detection model for tourism based on the Local Outlier Factor anomaly detection algorithm. Experimental results show that our model can effectively identify fraudulent merchants in the tourism market.