Anomaly Radar
Catch the transaction that shouldn't be there.
Every transaction is scored against the normal pattern for that vendor, category, and amount. Radar surfaces duplicate payments, subscription creep, and fraud-shaped outliers before month-end close, with a stated reason for every flag.
false-positive rate on live customer transaction volume
Anomaly detection has a trust problem: most tools flag so much that finance teams learn to ignore the alerts within a month. We built Radar backwards from that failure mode — it was tuned against real customer transaction data specifically to keep the false-positive rate low enough that a flag is worth opening.
It doesn't look for one thing. It runs several models in parallel — duplicate-payment matching, vendor-behavior drift, category-spend outliers, and a lightweight fraud-pattern classifier — and only surfaces an item when it clears a confidence bar, with the specific reason attached.
Nothing is blocked or held. Radar is read-only by design, same as the rest of Dibein: it tells you what looks wrong, in plain language, and links straight to the source transaction in your bank or ledger so you can act on it there.
Score every transaction
As transactions sync, each is compared against the historical pattern for that specific vendor and category — not a generic company-wide threshold.
Cross-check for duplicates
Payments are matched on amount, payee, and timing window to catch duplicate invoice payments and double-paid subscriptions before they'd otherwise surface at month-end.
Track vendor drift
A vendor whose invoice amount, frequency, or bank details change from their established pattern is flagged, which is the exact shape most vendor-impersonation fraud takes.
Explain, don't just flag
Every alert states which model triggered it and why, in a sentence a non-technical reviewer can act on without opening a black box.
See Radar in action on a sample workspace.
No signup required. This runs on fixed sample data — nothing here touches your own accounts.
Duplicate payment detection
Catches the same invoice paid twice, including near-duplicates with a slightly different reference or a split payment that adds up to the original amount.
Subscription creep tracking
Surfaces recurring charges that increased silently, tools nobody uses anymore, and overlapping subscriptions across departments paying for the same category twice.
Vendor-change alerts
Flags a change to a vendor's bank details or a first-time payment at an unusual amount — the two signals most associated with business email compromise fraud.
Category outliers
Highlights spend that's statistically unusual for its category and time of month, tuned per business rather than against an industry-wide benchmark.
One-click resolution
Mark a flag as expected, false-positive, or resolved directly from the alert, which retrains that vendor's baseline going forward.
Can Radar stop or reverse a payment?
No. Dibein's bank connections are read-only, deliberately. Radar flags for a human to review and act on inside your bank or ledger; it never holds, blocks, or initiates a transaction.
How is the false-positive rate measured?
Against labeled outcomes from live customer accounts, where a finance user marks each alert resolved, expected, or false-positive. The 2% figure is the trailing-90-day false-positive rate across that labeled set as of this quarter.
Does it need a training period before it's useful?
Category and duplicate-payment detection are active from day one. Vendor-behavior drift needs roughly 3 vendor payment cycles to establish a baseline, so it strengthens over the first one to two months.