GREY MARKET ORDERS DETECTION
One example method includes detecting grey market orders with a detection model. Data from historical orders, which include confirmed grey market orders, can be clustered based on engineered features of the data. A new order can be assigned to one of the clusters based on similarity and a score for the new order can be generated that reflects the likelihood that the new order is a grey market order. Action can be taken on the new order based on the score. The scores output by the detection model can be reviewed such that user input regarding the scores can be used to retrain the detection model.
1 . A method, comprising:
performing, by a detection model:
receiving a new order for a product;
assigning the new order to a cluster based on features of the new order, wherein the new order is similar to orders associated with the cluster;
generating a score for the new order based in part on the assigned cluster, wherein the score is a likelihood that the new order is a grey market order; and
taking an action on the new order when the new order is a grey market order.
2 . The method of claim 1 , further comprising generating a plurality of clusters including the cluster from historical data that includes data related to legitimate orders and grey market orders.
3 . The method of claim 2 , further comprising generating features from the historical data, inputting the features into a clustering engine, wherein the clustering engine generates the plurality of clusters from the features.
4 . The method of claim 3 , wherein the features include one or more of: discount of list price, buy power, last n quarters peripherals to systems ratio, quote system units, goal peripherals units, goallite peripherals units, goal system units, goallite system units, total number of employees, account type, one account, last n quarters peripherals to employee ratio, and/or account description.
5 . The method of claim 1 , wherein the score is based on a maximum cosine similarity score, a weight of the maximum cosine similarity score, a score describing a ratio for the assigned cluster, and a weight for the ratio of the assigned cluster, wherein the ratio is a ratio of grey market orders in the assigned cluster to total grey market orders in the plurality of clusters.
6 . The method of claim 1 , further comprising receiving user input for the score, wherein the user input is based on a ground truth of the user.
7 . The method of claim 6 , further comprising retraining the detection model with the user input.
8 . The method of claim 1 , wherein the action includes one or more of cancelling the order, changing a price of the order, taking no action, or limiting a quantity of the order.
9 . The method of claim 1 , further comprising identifying specific features in the features of the new order that contributed most to the score.
10 . The method of claim 1 , further comprising adjusting weights of the detection model based on user input and based on unsupervised learning.
11 . A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising:
performing, by a detection model:
receiving a new order for a product;
assigning the new order to a cluster based on features of the new order, wherein the new order is similar to orders associated with the cluster;
generating a score for the new order based in part on the assigned cluster, wherein the score is a likelihood that the new order is a grey market order; and
taking an action on the new order when the new order is a grey market order.
12 . The non-transitory storage medium of claim 11 , further comprising generating a plurality of clusters including the cluster from historical data that includes data related to legitimate orders and grey market orders.
13 . The non-transitory storage medium of claim 12 , further comprising generating features from the historical data, inputting the features into a clustering engine, wherein the clustering engine generates the plurality of clusters from the features.
14 . The non-transitory storage medium of claim 13 , wherein the features include one or more of: discount of list price, buy power, last n quarters peripherals to systems ratio, quote system units, goal peripherals units, goallite peripherals units, goal system units, goallite system units, total number of employees, account type, one account, last n quarters peripherals to employee ratio, and/or account description.
15 . The non-transitory storage medium of claim 14 , wherein the score is based on a maximum cosine similarity score, a weight of the maximum cosine similarity score, a score describing a ratio for the assigned cluster, and a weight for the ratio of the assigned cluster, wherein the ratio is a ratio of grey market orders in the assigned cluster to total grey market orders in the plurality of clusters.
16 . The non-transitory storage medium of claim 11 , further comprising receiving user input for the score, wherein the user input is based on a ground truth of the user.
17 . The non-transitory storage medium of claim 16 , further comprising retraining the detection model with the user input.
18 . The non-transitory storage medium of claim 11 , wherein the action includes one or more of cancelling the order, changing a price of the order, taking no action, or limiting a quantity of the order.
19 . The non-transitory storage medium of claim 11 , further comprising identifying specific features in the features of the new order that contributed most to the score.
20 . The non-transitory storage medium of claim 11 , further comprising adjusting weights of the detection model based on user input and based on unsupervised learning.