AI agents built for fraud operations — complete deep analysis in minutes, tune monitoring rules, respond to new attacks, and automate dispute filing, with a human approving anything consequential.
Fraud teams are outnumbered. Analysts spend most of the day assembling context — pulling device history, stitching linked accounts together, exporting evidence for a chargeback — rather than deciding anything. Attacks evolve faster than a rule change can clear review, and every hour of that lag is money leaving.
We build agents that work inside your fraud operation rather than beside it: reading the same governed data your analysts read, writing into the same case management system, and continuously strengthening your defenses as patterns shift. Each agent is scoped to one job and hands every consequential decision back to a person, with the evidence attached and the whole exchange logged.
Spot complex fraud patterns earlier, cut investigation time, and give teams clearer signals for faster, more accurate decisions. Four agents, each scoped to one job, each reading the same governed data your analysts already work from.
The agent analyses the device, behavioural, and traffic signals behind an attack, identifies the accounts linked to the same pattern or ring, and drafts the rules needed to stop it. What used to cost an analyst a morning of pivot tables arrives as a reviewable brief with the evidence already attached.
Maps connections across users, devices, addresses, payment instruments, and other shared identifiers to surface fraud rings, multi-accounting, promo abuse, and bust-out fraud. Complex linked behaviour becomes legible in minutes instead of an afternoon spent piecing a network together by hand.
Ring detection runs on the same property graph as our fraud practice — the agent reads a governed model rather than inferring a network from a prompt. Fraud & Financial Crime Intelligence
Describe a rule in plain language — English or French — and the agent compiles it into testable logic, then backtests it against historical traffic before anything reaches production. It also flags noisy or underperforming rules and recommends threshold changes based on observed fire rates, so alert quality improves instead of drifting.
Gathers transaction details, customer history, device data, and delivery proof for each dispute, then assembles a network-ready package per reason code with a consistent narrative tailored to the dispute type. Win and loss outcomes feed back, so later submissions get stronger.
Nothing consequential happens unattended. Rule changes, account actions, and dispute submissions are proposed with their evidence and executed only on human approval — and every step is logged with the prompt, the data read, the action taken, and the approver. In a regulated institution that audit trail is the difference between an agent you can demonstrate and one you can actually put into production.
Let's talk about your toughest infrastructure, data, or security challenge. Discovery calls are free, and we scope every engagement in writing before any work begins.