Deal or deceit: detecting cheating in distribution channels.

Distribution channel is a system that partners move products from manufacturer to end users. To increase sales, it is quite common for manufacturers to adjust the product prices to partners according to the product volume per deal. However, the price adjustment is like a double-edged sword. It also spurs some partners to form a cheating alliance, where a cheating seller applies for a falsified big deal with a low price and then re-sells the products to the cheating buyers. Since these cheating behaviors are harmful to a healthy ecosystem of distribution channel, we need the automatic method to guide the tedious audit process. Thus, in this study we propose the method to rank all partners by the degree of cheating, either as seller or buyer. It is mainly motivated by the observation that the sales volumes of a cheating seller and its corresponding cheating buyer are often negatively correlated with each other. Specifically, the proposed framework consists of three parts: 1) an asymmetric correlation measure which is needed to distinguish cheating sellers from cheating buyers; 2) a systematic approach which is needed to remove false positive pairs, i.e., two partners whose sale correlation is purely coincident; 3) finally, a probabilistic model to measure the degree of cheating behaviors for each partner. Based on the 4-year channel data of an IT company we empirically show how the proposed method outperforms the other baseline ones. It is worth mentioning that with the proposed unsupervised method more than half of the partners in the resultant top-30 ranking list are true cheating partners.