In the quiet arithmetic of financial trust, researchers have asked whether the newest forms of machine intelligence can outpace the workhorses already guarding millions of accounts. A hybrid neural model combining convolutional, recurrent, and attention-based layers was tested against the rarest of signals — fraudulent transactions appearing in only one out of every hundred — and found itself nearly equal to, but not decisively beyond, the gradient-boosted methods that practitioners already rely upon. The study is less a declaration of victory than a careful mapping of where complexity earns i