Machine learning vs payment fraud: Transparency and humans in the loop to minimize customer insults
What are customer insults, and what does machine learning have to do with it?
False positives, or customer insults? Which one sounds better? It’s a trick question — it’s the same thing, framed differently. In binary classification, a false positive is an error in data reporting in which a test result improperly indicates the presence of a condition (the result is positive), when in reality it is not present.
Even in our data-driven era, however, that definition could sound a bit dry or confusing for some. Hence, “customer insult” was born. In fraud prevention, the term customer insult is used to describe a false positive: A transaction, which is not fraudulent but is nevertheless marked as suspicious.
That happens often and is more of a problem than you may think. A recent survey conducted by Sift on 1,000 consumers in the US found that 36% of respondents have had a transaction falsely declined due to suspected fraud. Another study by PwC found that even one negative customer experience can have a significant impact on retailers.
So, what can be done?