12 · Protect

A verdict on every charge, explained.

Adaptive machine learning scores every transaction in milliseconds, weighing hundreds of signals before the authorisation ever reaches the issuer.

You set the risk appetite; the system holds the line. And every approve, decline or review arrives with the reasons behind it, not just the answer.

What it does

Each transaction gets a risk score in milliseconds, inline with authorisation, so legitimate payments never wait on a check.

Models learn from fraud patterns seen across the whole Prosperos network, so an attack on one merchant hardens the defences of every other.

Write your own rules on top of the models, by amount, geography, velocity, BIN or any signal, and change them without a deploy.

Every verdict comes with the signals and rules that drove it, so your team can review the call instead of guessing at it.

Borderline transactions route to a manual review queue with full context, so analysts decide the close ones rather than the model alone.

Captures the evidence behind each approved payment and assembles dispute responses, cutting the cost of fraud that does slip through.

How it works

01
Score in the flow
Anti-fraud sits inline on the payment rails, so every transaction is scored automatically the moment it arrives, with no separate integration to call.
02
Set your appetite
Tune the risk threshold and add your own rules in the dashboard, deciding what to approve outright, what to decline and what to send for review.
03
Act on the verdict
Read the reasons behind each decision, work the review queue, and refine your rules as patterns shift, with the models adapting alongside you.

By the numbers

Real-time

Scoring

Every transaction

Coverage

Adaptive ML

Model type

Explained

Decisions

Works with

Ready to let fraud burn out?

Start in test mode today, or talk to an engineer who knows your traffic.