Fraud agents that cut false positives, not customers

If you run a payments business long enough, you realize there are two fraud problems.

The money fraudsters steal, and the money your own system blocks by mistake.

Most teams already catch the obvious bad traffic.

Where you quietly bleed margin is the good cardholder who gets treated like a thief.

Card declined on a normal purchase.

They try again, declined again.

Now they hate your brand, spam support, and finish the transaction with someone else.

What a fraud agent really is

The simple version.

Your models score risk, your rules set guardrails, and a fraud agent sits on top as a co pilot.

The agent watches the transaction stream, asks the right questions, uses your models, then decides whether to approve, challenge, or block.

It also explains why.

How the system actually works

In practice, a fraud agent for payments behaves like this.

1. It listens to events.

   It taps into your auth stream, chargebacks, disputes, device data, KYC, and historic behavior.

2. It scores each transaction.

   It calls one or more fraud models, plus your existing rules, to get a risk view.

3. It picks the next action.

   Approve if it looks clean, step up authentication if it is suspicious, block or hold if risk is high.

4. It explains what it did.

   For each case it generates a short reason that a human can understand.

5. It learns from outcomes.

   It watches which approvals become chargebacks and which declines turn into complaints, then feeds that back.

High clarity, low detail.

Enough for your team to see how it fits into the stack.

Where most teams go wrong

Most teams do some version of this already, but in a brittle way.

They rely on static rules that never die, even when the product changes.

They push marginal cases into review queues that grow faster than volume.

They accept 5 to 10% false positive rates on card transactions because “that is the industry norm.”

At 50 million card transactions per month, a 5 to 10 percent false positive rate means 2.5 to 5 million legitimate attempts blocked.

Cutting that to around 2 to 3% frees up something like 1 to 4 million extra approvals every month.

The pattern is always the same.

Losses creep up, teams add more rules, and spend their weeks fighting fires instead of improving the system.

What changes when you fix it

When teams move from rules alone to model plus agent, the numbers change.

You see false positives drop from the 5 to 10% band into something closer to 2 to 3%.

On a 50 million transaction portfolio, that is over a million extra approvals per month that used to be blocked.

Real deployments report results like:

• Around 40% fewer false positives, with about 30 percent fewer false negatives.
• One merchant saw an 86% drop in order declines while keeping fraud inside target.

Translate that to margin and it is simple.

More real customers get through, fewer bad transactions slip past, and your Risk Ops team deals with a smaller, higher quality queue.

Why this is harder than it looks

On paper this sounds straightforward.

In production it is not.

You need clean, real time access to payment events across multiple processors, acquirers, and regions.

You need models that understand both global patterns and local quirks, without punishing new markets.

You need the agent to run inside tight latency limits so authorizations are not delayed.

You also need clear explanations for every decision for regulators, partners, and internal audit.

That means proper feature tracking, decision logs, and a way to explain model behavior in normal language.

On top of that, you have to land all of this inside a stack that already has a decade of rules, hard coded exceptions, and one off PSP integrations.

Most teams underestimate that part.

This is why many fraud projects never get past the slide stage.

Where Devbrew fits in

Devbrew comes in at the system level.

We design and ship fraud agents that sit on top of your current stack.

We handle the hard parts around data plumbing, real time orchestration, and explainable decision flows, without forcing a full rebuild.

We focus on two measurable outcomes.

Lower false positives and lower investigation time, expressed in extra approvals, recovered revenue, and hours saved in Risk Ops.

If your false positives are high, you are already paying

If you already know your false positive rate is too high, you are not early.

You are already paying for it in lost approvals and support cost.

I am happy to review your current fraud flow, look at your numbers, and show you where an agent layer would recover the most margin.

If you want to see what this could look like on your existing payments stack, get in touch and we can start from a simple baseline.