I work for a company that ships material (a couple of thousand shipments per day) around the world. In order to ship anything a customer has to declare the weight of a shipment and declare the contents To circumvent customs they under declare material for example T-Shirts might be the declared contents but the actual contents might be a Hugo boss t-shirts which are worth more. Normally they are caught by random inspections and black listed (I have this data set and it grows daily) if it is a re-occurring shipper but this is costly to honest users of the service and impacts the time taken for a shipment to arrive.
Repeated offenders are blocked manually based on addresses but can and do sign up again with a different account varying the address enough so as not to be recognized. For example 1 fake street could be applied again to fkae street 1. A human can see the difference but we dont have the staff to go through it. I think machine learning could be the way forward
I was wondering if anyone would have any ideas how to classify these shipments. Research papers, ideas, brain storming are all welcome. What i would like to do is use the caught shipments to try an identify customers who have tweaked the address slightly so as to direct customs appropriately and make our black listing more effective
Thanks for your time