Real-time fraud decisions, from sign-up to payout.
Kairos decides each event in milliseconds and shows your team the reason behind every decision.
- Stripe
- Shopify
- Fingerprint
- Cloudflare
- Fastly
- Lambda@Edge
Catch fraud on day one, before you have labels.
Grayson, Finic's decision model, answers questions you write in plain language, such as "Does the shipping address look like a reshipper?", with calibrated probabilities. It reads descriptions, emails, names and notes, and every answer explains itself.
- No training data needed
- Reads unstructured data
- A reason for every decision
- Does the shipping address look like a reshipper?0.91
- Is the order far larger than this customer's usual?0.77
- Do the item titles match the customer's past orders?0.18
- Is this email address throwaway?0.06
Models that learn your outcomes, tested before they ship.
Logistic regression and gradient-boosted trees learn from your numbers and Grayson's answers. Every variant is trained on older data and tested on newer data, so you see real gains, with an interval, before anything goes live.
- Split by time, the way fraud arrives
- ROC-AUC, PR-AUC and a 95% interval
- Precision and recall at your thresholds
- Grayson alone0.912
- Logistic regression · fields0.871
- XGBoost · fields0.894
- Logistic regression · fields + GraysonWinner0.949
- XGBoost · fields + Grayson0.941
Your team stays in charge of every change.
Workflows have drafts, tests and versions, and changes can land as drafts for review. Every decision keeps its inputs and trace, so you can explain any outcome to a customer, a partner bank or an auditor.
- Drafts, tests and versions
- Every decision traced step by step
- Roles and per-key permissions
Work in Kairos from your AI tools.
Kairos has an MCP server, so Claude Code, Cursor or Codex can read decisions, draft workflows and run experiments. An admin decides what each key may do.
Every action is recorded under the agent's key.
See MCP and AI tools- list_decisionswithdrawal · last 90 days · 412 labeled
- create_workflow_experiment5 variants
- get_workflow_experimentdone · 5 of 5 trained
- apply_workflow_experiment→ draft v6 for review
Logistic regression on the payout's numbers plus Grayson's answers wins: ROC-AUC 0.949 on validation, 0.930 on test. I saved it as a draft, v6, with the results attached for your review.
Across the whole customer journey.
From the first sign-up to the last payout, with one view of each customer, their devices and their history.
Let real customers in. Keep fake accounts out.
Decide every sign-up as it happens. Kairos sees the devices, emails and addresses a new account shares with others, and how the sign-up session went.
Explore account opening- +0:00sign_up
- +1:37verification_started
- +2:14email_verified
- +3:51login
- shared_device0.94
- rapid_attempts0.81
- disposable_email0.62