Score every transaction in under 8 milliseconds
Fraudpulsar catches the card testing, account takeovers, and synthetic identities your static rules engine waves through, scoring risk in real time as each payment lands.
Static rules catch last month's attack. Not today's.
Rules engines were built for audit trails and compliance documentation, not real-time anomaly detection. Every novel attack pattern requires a human to write a new rule. That gap, between the attack and the rule update, is where fraud operates.
Velocity rules, BIN-country checks, and static amount thresholds treat every account the same, regardless of what that account normally does. A card tester probing with $0.50 authorizations sits below every threshold you have set.
- Card velocity rule fires a day after the burst ends
- New attack vectors require manual rule authoring before detection begins
- High false positive rate blocks your best customers at peak spend
Instead of asking "does this transaction match a known-bad pattern?", Fraudpulsar asks "does this transaction match what this specific account normally does?" The comparison is always against the account's own history, never against a global population.
- Deviation from personal baseline scored in real time
- Novel attack patterns visible before a rule is written
- High-spend regular customers score low, not high
A risk score anchored to this account, not to a global threshold.
Three steps from event ingestion to a 0-1 score with contributing factors. Full technical walkthrough
Send your transaction event stream via REST POST or webhook. Account ID, transaction metadata, device context, IP. No card numbers or raw PANs.
Fraudpulsar maintains a rolling behavioral model per account: merchant mix, time-of-day patterns, amount distribution, device consistency, velocity windows.
Each new transaction produces a 0-1 risk score with contributing factors in the response. Read the score. Set your own block, review, and pass thresholds.
Full walkthroughThe attack types payment teams actually lose sleep over.
Fraudpulsar's per-account deviation scoring identifies the six attack categories that generate the most chargeback volume and manual review burden for payment teams.
Rapid low-value authorization attempts deviating from account's normal cadence and amount distribution.
Behavioral shift after credential stuffing: new device, new location, atypical merchant categories within the same session.
First-transaction signatures that diverge from organic new-customer patterns, including device and behavioral profile mismatches.
Installment patterns that deviate from account's repayment history and purchase profile, indicating structured first-payment default intent.
Chargeback filing patterns correlated to behavioral signatures that distinguish genuine disputes from deliberate misuse.
Cross-account patterns at specific merchants that signal coordinated chargeback rings or merchant-level fraud schemes.
One webhook. Score in your response within a day.
REST POST to /v1/score. Webhook async events for post-authorization signals. Every response includes risk_score, confidence, and contributing_factors. No polling, no batch processing.
Add your webhook endpoint and receive your API key from the sandbox dashboard.
POST your transaction event stream to /v1/score. Takes account_id, transaction metadata, device, and IP.
Read risk_score (0-1) and contributing_factors in the response. Set your own block/review/pass thresholds.
Optionally subscribe to async risk updates for post-authorization signals and chargeback feedback loops.
From risk engineers who ran the pilot.
Three teams ran Fraudpulsar against live transaction streams before we opened broader access.
"The card-testing detection was the immediate win. We were seeing a burst of $0.50 and $1.00 authorizations that our velocity rule kept missing because it was tuned for $0.01. Fraudpulsar flagged the entire cohort as a behavioral deviation within the first 90 seconds."
"We cut our manual review queue by roughly a third in the first month. The false positive reduction was the metric our ops team cared about most. Fewer legitimate transactions getting flagged means fewer customer complaints."
"The latency numbers held up in production. We were skeptical about sub-10ms scoring at our transaction volume but the p99 has stayed under 22ms since we flipped to live. That mattered for our authorization flow."
Start scoring transactions in your sandbox.
10,000 test transactions, synthetic fraud scenarios pre-labeled, full API. We will issue your sandbox key within one business day of your request.
Request API access