More talks in the program:
16:00 - 17:00
This session reviews building a Machine Learning pipeline for detecting anomalies of sales point transactions. It is a tricky job for a company like Superonline, that has over 3000 sales points all over the country, distributed across metropolitan and rural areas. An aim is to detect anomalies in transactions because these can lead us to detect fraudulent transactions. The problem splits into two branches, the first is to detect outlying sales points; the second to detect outlying days of a sales point. Detecting the latter is not as complex as finding outlier sales points. Every sales point has a medial number of transactions and a trend. Building a trend line out of past data and calculating a point estimation using the prediction interval method of statistics help us draw boundaries for each day. When a sales point goes beyond these boundaries we raise a flag. For detecting outlier sales points, we need to group them by their type and location and then calculate quartiles and IQR in these groups. When we compare every sales point to these metrics, we see whether they are outlier or not.