Snipeyes FD: stop fraud before the money leaves
Fraud costs a bank or wallet twice: once in stolen funds, and again in customers who stop trusting the service. Regulators also expect firms to show how they detect fraud and why they blocked a particular customer. Rules engines struggle on both counts, because criminals change their methods faster than new rules can be written.
Snipeyes FD (fraud detection) uses AI to score every login, payment and account opening as it happens. Suspicious activity is stopped or checked before funds settle, and ordinary customers carry on as normal.
The fraud it watches for
- Account takeover (ATO), where a criminal gets into a real customer’s account. FD spots unusual devices, behavior and bursts of activity, and stops the session before money moves. Analysts get a timeline of what happened.
- Payment fraud. This includes authorized push payment (APP) scams, where a customer is tricked into sending money, and card-not-present fraud online. FD maps the links between accounts to uncover mule networks (groups of accounts used to move stolen funds).
- Synthetic identities and fake onboarding. During know-your-customer (KYC) checks, ID, selfie and document liveness checks catch invented or stolen identities at the door.
- Bonus abuse and collusion, found by linking accounts, devices and payout channels.
What happens to each event
- Score: FD scores the event in real time. It looks at the device, the customer’s usual behavior, velocity (how quickly actions follow each other), links to other accounts and KYC signals.
- Decide: Depending on the risk, it lets the event through, asks for extra verification, sends it for review or blocks it.
- Explain: Every decision carries reason codes. Each case includes an evidence pack for your operations and dispute teams.
- Learn: Confirmed fraud and confirmed genuine activity feed back into the models, so they adapt as criminals change tactics.
Why rules engines and manual review leak money
| Rules engine | Manual review | Snipeyes FD | |
|---|---|---|---|
| Speed | Fast but static | Slow, often hours | Real-time scoring |
| New patterns | Misses rings it has no rule for | Catches some, late | Account links and behavior reveal new rings |
| False positives | High: good customers blocked | Growing backlog | Risk-based checks keep good customers moving |
| Explainability | Rule hit only | Analyst note | Reason codes on every decision |
| Scale | Struggles at peak times | Needs more staff | Designed for peak traffic |
Explaining decisions to auditors and customers
When a customer disputes a blocked payment, or an auditor asks why an account was frozen, “the model decided” is not an acceptable answer. FD records the reasons behind every decision in terms a person can read. Models are versioned, personal data is kept to a minimum, and a full audit log is kept. Together these support your obligations under SOC 2, PCI DSS and GDPR.
Getting started without risk
FD connects through a REST API, with SDKs for web and mobile apps, webhooks for case management, and connectors for core banking, wallets, insurance platforms and e-commerce checkout. It covers logins, payments, onboarding, payouts and promotions.
We recommend starting in shadow mode. FD scores live traffic without blocking anything, so you can compare its decisions with your current controls, on your own data, before you switch it on.
FD is aimed at digital banks, wallets and insurers, where every wrongly blocked customer is a cost as real as the fraud itself.
See FD on fraud patterns like yours
We replay anonymized account takeover, payment and KYC fraud patterns and show how FD scores them. A shadow-mode trial on your own data can follow.
NDA included • Web and mobile SDKs