Use case summary
Corefy gives you a live view of every transaction across all connected providers, with unified statuses and real-time analytics. Rules flag suspicious activity as it happens, alerts notify your team, and Firewall can act on flagged patterns automatically. You see and respond to anomalies while they're unfolding, instead of discovering them after the losses land.
- Payment Ops
Why fraud shows up in your data before it shows up in your reports
Fraud rarely arrives as a single obvious transaction. It shows up as a pattern — a sudden run of small test payments on stolen cards, a spike from one geography, repeated attempts against different cards from the same source. Those patterns are visible in transaction data as they form. The problem is that most teams only see the data after the fact, in reports assembled once the cycle closes — by which point the pattern has already cost what it was going to cost.
A multi-provider setup makes the blind spot worse. When each provider's activity sits in its own dashboard, no one has a live, combined view of what's happening across the whole stack, so a pattern spread across providers — the same fraudster probing each one — may never resolve into something anyone notices. Meanwhile, the manual alternative, someone periodically eyeballing separate dashboards, doesn't scale and doesn't run at 3 am. The result is that suspicious activity is usually confirmed in hindsight, when the useful moment to act on it has passed.
How to watch every transaction as it happens
Once your providers are connected to Corefy, all their activity flows into one live system, so monitoring becomes watching one real-time picture.
- 1
See all providers in one live view
The dashboard shows transactions across every connected provider as they happen, with unified statuses, so your whole payment flow is visible on one screen in real time.
- 2
Flag suspicious activity with rules
Firewall's attribute-based rule engine evaluates every payment in real time, so patterns you define as risky — velocity spikes, mismatched geographies, repeated failures from one source — are caught as they occur rather than found later.
- 3
Get alerted as anomalies emerge
Rules trigger notifications the moment activity crosses your thresholds, so your team learns about a developing pattern while there's still time to act on it.
- 4
Act automatically on what you catch
Monitoring and prevention are the same engine: a flagged pattern can move straight to an automatic response — block the source, challenge with 3DS, add to a blocklist — so detection and action aren't separate steps with a delay between them.
- 5
Watch the metrics, not just the transactions
Real-time analytics track approval rates, decline reasons, and volumes across providers, so a developing problem shows up as a moving number on a dashboard, not a surprise in next month's report.
- 6
Investigate with full context
Any flagged transaction opens to its complete log — the full request and response with the provider — so confirming whether something is genuinely suspicious doesn't mean logging into a provider portal to dig.
What you get
Patterns hiding across separate provider dashboards resolve into one visible view.
Anomalies caught while they unfold
Real-time rules and alerts surface developing patterns in time to act instead of confirming them once the damage is done.
One combined view across providers
A fraudster probing several providers at once resolves into a single visible pattern rather than hiding in separate dashboards.
Detection wired to response
Because monitoring runs on the same engine as prevention, catching a pattern and stopping it happen together, without a manual handoff.
Coverage that doesn't sleep
Automated rules watch every transaction around the clock, so protection doesn't depend on someone happening to look at the right dashboard.
Instability spotted on live dashboards before it grew
Corefy's monitoring surfaced a problem for an ISO/MSP client before it became costly: watching their live dashboards, the account team noticed unstable conversion in one processing currency — an unusually high rate of failed transactions. Investigating in real time, they traced it to a single provider suffering recurring downtime and resolved it by adding a second provider and cascading between them. The point of the story is the timing: the anomaly was caught as a live pattern on the dashboard, not weeks later in a report.