Detect suspicious activity, automate investigations, and act before the money leaves — from your first dollar to your billionth.
Trusted by Fortune 500 companies and leading banks, fintechs, and merchants.
01
MouseCat's AI learns about your business from your standard operating procedures (SOPs), historical data, and case notes.
Your team can even give MouseCat written feedback to help it learn concepts it missed. This enables MouseCat to understand your users, products, and risk landscape.
02
MouseCat's AI spots anomalies across transactions, accounts, devices, and counterparties.
It can identify signs of ATOs and isolate users who are being scammed. It can analyze transactions to spot money laundering and third party fraud like stolen bank accounts and credit cards. MouseCat can even catch signs of first-party fraud and abuse like promo/refund abuse, reselling, etc.
03
When MouseCat spots an anomaly or when your team does, MouseCat will investigate that user using the same data your human agents use.
It will review all of the relevant evidence (IDV signals, user details, transaction activity, etc.), test competing explanations, search precedent, and produce a decision and explanation that a person can review or that policy can act on automatically. All decisions are accompanied by a regulator-ready audit log which cites the exact data that was used in the decision. This makes subsequent human reviews faster and makes automated decisions more explainable.
04
MouseCat can route decided cases to human agents for review or automatically take action (like auto-approval, increased hold, SAR drafting, etc.) on high-confidence cases.
When laundering, fraud, or scams occur, MouseCat will review recent cases to improve its model and suggest new rules that you can deploy.
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 / online casinos, and marketplaces. Some of the most sophisticated fraud/compliance teams in the world leverage MouseCat to:
REASONING
The case was flagged for a new device, but the account is three days old and every sign-in since it opened has come from the same device on the same IP — there is no second device here to have taken it over. What the account has done in those three days is take three identical $1,950 ACH deposits on consecutive days, against a linked PayPal whose owner on record is not the owner of the verified bank account, after identity verification cleared only on a third attempt, on a licence issued in a different state to the one on the profile, under an email that copies the bank owner's with digits appended. Four near-identical cases in your own history were all closed as stolen payment instrument.
RISK PROBABILITY
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 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.
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.
Across industries we have seen the following capabilities be particularly useful:
