It's virtually impossible to have both a false positive and false negative rate of 1% for any predictive model. This is inherently a tradeoff. In addition to the terrific parent comment, you can trivially push false positive rate to 0% by never predicting a positive outcome or push false negative rate to 0% by always predicting a positive outcome. Finding the sweet spot somewhere in the middle is what we try to do, and generally for criminal investigations, which is probably what this is closest to, we err on the side of false positives at the cost of more false negatives. We'd rather let the guilty go free than jail or execute the innocent. There is an asymmetric cost to the different types of errors.

But as the parent says, this means you're still making plenty of mistakes. You're just not making the specific kind of mistake you're trying hard not to make, though I'm pretty skeptical of anyone claiming 1% false positives. It's hard to even envision a truly valid way of assessing this. You'd at minimum need an adversarial test set created by an independent third party that has no incentive to be favorable to you, and even then, the landscape is so constantly shifting that an error rate estimated at one time tells you not exactly nothing about a future time, but this isn't exactly estimating the strength of gravity. You can be correct one week and wrong the next.

Not a statistical estimate, but getting shot in the head doesn't even have a 99% kill rate.