How AI Bank Matching actually works

Three layers of matching

Zablen's bank matching pipeline runs every incoming transaction through three passes: an exact-match layer, a fuzzy confidence-scored layer, and a rule-based fallback for anything the model isn't confident about.

Confidence scoring

Each candidate match gets a confidence score based on amount, date proximity, payee similarity, and historical matching patterns for that vendor. High-confidence matches are applied automatically; everything else is queued for a quick human review.

Catching drift before it matters

We continuously monitor match accuracy over time and flag drift when a vendor's transaction patterns change — a renamed payee, a new payment processor, or a shift in invoice amounts — before it causes a wave of missed matches.