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?

Read the full article on ZDNet