How does AI detection actually work at the model level? I keep seeing confidence scores and probability outputs but almost no explanation of what signal the tool is actually measuring. Is it perplexity, token probability distributions, something else entirely? Trying to understand whether the disagreement between tools reflects different methods or just noise.
The reality is that AI fraud detection does work at the population level. The false positive rate across millions of transactions might be acceptable to the institution while being completely unacceptable to the individual business that gets frozen. What’s missing is a proportional response mechanism. The detection is fast; the resolution isn’t. Pushing for faster appeal processes and documented false positive rates in service agreements is probably the most practical lever available right now.