This talk will show how a fraud detection model has been developed based on the data from the browsing sessions of the different users. Tools such as PySpark and Spark ML have been used in this initiative due to a large amount of data.
The model created was able to identify a grouping of characteristics that covered 10% of the total sessions in which 88% were deemed fraudulent. This allows analysts to spend more of their time on higher-risk cases.