MouseCat can route decided cases to human agents for review or automatically take action on high-confidence cases.
Auto-approvalSAR draftingRule suggestions
Outcomes
MouseCat in action
MouseCat customers include public and Fortune 500 companies, and businesses across a variety of industries including: banks, credit unions, crypto exchanges, e-commerce sites, i-gaming, and marketplaces. Some of the most sophisticated fraud and compliance teams in the world leverage MouseCat to:
1.Increase net-revenue by catching more risky behavior with fewer false positives
2.Reduce the operational expense of manual reviews
3.Improve customer experience by reducing review time
“We’ve given our risk analyst team a lot more leverage alongside our existing ML models and rules, and seeing close to 90% analyst-agreement rates on correctly identifying fraud and scams – it surprised me how well it’s working.”
Will MegsonVice President of Product, Coinbase
Capabilities
Case CS-2026-0512FRAUD · 0.96
Device fingerprintShared · 3 ring accounts
Payout accountChanged before cash-out
ACH deposits3 × $1,950 · under $2k
Payout owner vs. IDDifferent person
Profile emailLookalike of owner's
Precedent in your history4 cases, all fraud
ESCALATE
Six signals agree, none contradicted — funds still recoverable for 40 minutes.
Evidence cited · 18 steps · 3.1sCOMPLETE
01
Fraud and Scam Prevention
MouseCat detects and investigates fraud and scams in real time across every transaction type, and turns what it learns into better rules and models.
Identify cases of first-party fraud, third-party fraud, ATOs, and scams across a wide variety of transaction and transfer types (credit/debit cards, ACH, wires, Zelle, checks, crypto) in real-time.
Run deep investigations of anomalous activity to build a complete case file on a user and determine a recommended action (add friction, escalate, allow) along with detailed evidence.
Improve detection over time using historical data (like past R10s and chargebacks) and human feedback, and obtain high-quality rules and model features you can use in your own system.
Analyze cases of fraud to find broader trends like a ring of users all operating out of the same location and using a specific type of emulator to conduct fraud at scale. Leverage these trends to improve your controls and reduce losses.
Find synthetic identities by analyzing account information, device, and user behavior so you can proactively close accounts and prevent future fraud and laundering vectors.
MouseCat · Session 7ef54h··8542LIVE
→Decide checking device, geolocation, and behavior against account history
Device fingerprintShared · 3 accounts
VPN / proxyDetected
IP reputationKnown abuse IP
Geo velocity2 countries / 1hr
Behavior vs. baselineUnseen device, odd hour
Card testing6 declines, 1 approval
BLOCK
Account takeover in progress — six signals agree, none contradicted.
02
Account Security
MouseCat monitors every account for the signals that precede fraud — device changes, behavioral anomalies, bots, and account takeovers — and matches new activity against patterns it has already seen.
Find patterns like dormant accounts that transition to active, changes in transaction size or type, etc.
Spot emulators, VPNs, proxies, remote-access software, device fingerprint changes, etc.
Cluster users based on their behavior and find historical cases that are similar to new patterns (e.g. same device fingerprint, payee, or behavioral sequence).
Isolate users engaging in automated activity, such as an AI agent or scripted bot.
Detect and intervene in ATOs before losses occur by leveraging device, geolocation, and behavioral signals to spot the early signs of stolen accounts.
Alert AML-2026-0142REVIEWING
OFAC SDN screeningNo match
PEP screeningNo match
Structuring patternFlagged
Funnel accountFlagged
Adverse mediaReviewed
SAR draftReady
FILE SAR
2 of 6 checks flagged — evidence linked, narrative drafted.
Audit trail · 14 steps · 2.4sCOMPLETE
03
Compliance and AML
MouseCat integrates with your existing compliance stack to screen, investigate, and document AML activity end to end — from sanctions screening through SAR drafting.
Automatically perform sanctions screening against the OFAC SDN list, analyze matches, and tag likely false positives.
Spot signs of money laundering like structuring, layering, funnel accounts, and shell indicators.
Automatically review AML alerts, leveraging key data like transaction history and user profile to determine the appropriate action to take (filing, escalation, dismissal, etc.). All reviews have a complete audit trail attached.
Create detailed SAR drafts that cover the key FFIEC criteria, based on evidence collected in the case record. The SAR is linked to the raw data that substantiates the narrative.
