Detection System of Promotion Abuse Using Similarity and Risk Scoring Methods
Abstract
Offering promotion coupons is one of the most popular strategies of online marketing to attract new customers and increase customer loyalty. However, this strategy opens chances for fraud risk as the coupons are being redeemed multiple times using fake accounts. This risk becomes a burden to marketing costs and leads to failure to accomplish the intended strategic value. Therefore, this research focuses on building an automatic detection system of online promotion abuse based on its risk level. The proposed system also must work on live stream and bulk data. Therefore, in live stream data, it could alert the administrator before the transaction finished or the next process started. After conducting an exploratory factor analysis of the 24 attributes collected from four tables of data transaction, there were seven attributes indicating promotion abuse. These attributes were the user IP address, shipping address, mobile number, member email, order email, payment ID, and product name. Then, supervised machine learning of similarity algorithms was used to build models and find the hidden correlation of attributes to indicate the promotion abuse. The result from comparing five similarity methods showed that based on the workflow and performance, the most suitable methods for this case were exact match and Levenshtein edit base. The automatic risk scoring feature of the proposed system used seven attributes of online transactions as their most prominent promotion abuse parameter based on its hidden correlation. From the system performance testing, the result values of precision, recall, and F-measure are 95%, 93%, and 0.94, respectively. These results indicate that the system performance is satisfactory.
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