Let’s look at a specific example, taken from a previous article I wrote on buyer fraud, or bust-out fraud
as it’s commonly known in our industry, and Euler Hermes’ sophisticated machine learning tool.
Bust-out fraud occurs when a buyer places an order for a product, takes delivery, then “disappears” before the seller can collect payment. In many cases, the buyer company never existed in the first place. Sellers are left holding an unpaid invoice, having shipped goods often worth thousands of euros.
Detecting bust-out fraud is part of our day-to-day business at Euler Hermes: we guarantee the payment of our clients’ buyers’ invoices. Part of this involves identifying a suspicious buyer before a client ships a product to them.
We developed an internal, AI-driven product to track large amounts of incoming data relating to buyers – for example credit limit requests, financial data, rejected claims. The product flags up buyers that fall outside the norm: for example, multiple requests by clients to cover the same buyer, or financial statements that look too good to be true.
These buyers are identified by the tool as potentially suspicious. The key is in the word “potential”. Because the tool cannot itself predict buyer fraud with 100% accuracy: for that, we need people with their years of collective experience and expertise.