Intent Intelligence
Intent intelligence for fraud, risk and trust & safety teams
Intent intelligence for fraud, risk and trust & safety teams
Darwinium scores intent at every step of the customer journey, from the first pre-authentication visit through account creation, login and account changes, not just at a single checkpoint. Every step is judged against what normal looks like, so fraud and abuse teams catch risk earlier, act before a risky step completes, and see exactly why each decision was made.
Why do fraud controls miss risk that passes the checkpoint?
Most fraud stacks are a series of checkpoints, and each one decides once. A checkpoint sees a request or a session, with no memory of the steps that led there and no model of what should come next.
Attackers spread activity across steps and sessions to stay under per-event thresholds: fifty promo redemptions across new accounts over three days look fine at every individual moment. And in scams, the person passing every identity check is often the victim, being coached by a fraudster.
The costliest fraud now happens after identity has been confirmed.
How does Darwinium score risk across the whole journey?
Journey Transition Probability (JTP) learns the shape of your traffic (which step usually follows which, how fast and how often) and scores every new step against it. It produces four signals:
- Per-step probability: Is this transition expected? Catches skipped steps and going straight to checkout with no browse.
- Path probability: How likely is this whole sequence? Catches paths no legitimate actor takes and coordinated abuse.
- First-passage time: How long did it take to get here? Catches programmatic speed and hesitation in the wrong places.
- Transition-rate change: Has this customer's normal shifted? Catches new attack patterns and campaigns before any labeled incident.
Each decision draws on device, behavior, identity and journey signals in one event. Humans, bots and AI agents are scored by the same model, which matters because only about one in four agentic transactions self-declares.
How does it reduce manual review and analyst workload?
JTP starts from a global baseline on day one and hardens as your own traffic accumulates, with no manual tuning, no analyst overhead and no labeled training data required.
Every flag produces explainable output an analyst can write a rule against. Actions are calibrated to the risk at each step: permit, verify, challenge or prevent.
Friction lands only where it's warranted and teams can start in monitor mode and switch to enforcement with a single rule change.
How does Darwinium catch fraud before money moves?
Intent intelligence that arrives after a payment has settled is forensics. Darwinium makes the decision in-line, in milliseconds, before the request reaches your application. Because it scores every step from the first visit onward, risk shows up in the journey before the high-value action:
- Account takeover: post-login behavior deviates from the real user's historical path.
- Scams and coercion: a long, benign session suddenly shifts to scam-like patterns.
- Promo and returns abuse: improbable paths to discount and refund endpoints; coupon stacking and wardrobing surface as low path probability.
- Credential stuffing: near-zero time to the login endpoint, with no normal pre-authentication journey.
What should fraud teams ask an intent intelligence vendor?
"Intent" is now common in fraud and bot-management positioning. Three questions separate real journey scoring from relabeled checkpoint scoring:
- Do you score the step or the session? Ask how the product would find the exact transition where a coached customer's path turned.
- Does it work on day one? Or does it need months of labeled data before it produces a usable signal?
- Can an analyst see why something was flagged and write a rule on it? Explainability is what turns a signal into an operation.
- Darwinium customers see 50% less fraud and 40% greater operational efficiency
- Only about one in four agentic transactions self-declares
- Transactions involving agents are rejected 9 times as often as other purchases
- 97% of organizations report an increase in AI-driven attacks, yet only 36% believe they have effective end-to-end fraud coverage across the customer journey
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