What's Behind the Numbers?
The growth of the online identity verification market has introduced a number of automated solutions that rely on optical character recognition (OCR) and basic machine learning to determine if an ID is authentic or the consumer’s digital identity is legitimate.
Without the right technology behind them, automated solutions can present a number of shortcomings:
- Poor verification accuracy (i.e., too many false positives)
- Return too many “suspect” decisions (aka “maybes”)
- More maybes lead to more manual reviews
- More manual reviews lead to more frustration and abandonment
Some automated solutions can cause more legitimate customers to be erroneously denied, triggering greater frustration, customer dissatisfaction and online abandonment. This costs you time having to perform manual reviews.
