ML risk scoring
Hundreds of signals condensed into a single, actionable score per transaction.
Fraud Prevention
Block fraud, approve good customers
Real-time machine-learning risk scoring, device fingerprinting, 3-D Secure 2.0 and configurable rules — tuned to stop fraud without adding checkout friction.
Risk analysisApproved
4score
3-D Secure passed
Device known-good
AVS & CVV match
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Overview
Risk decisions in milliseconds, tuned to your business
Fraud tooling that is too aggressive quietly rejects good customers; tooling that is too loose invites losses and chargebacks. The right system finds the line for your specific business and adjusts as patterns change.
Bear Merchant scores every transaction in real time and lets you layer your own rules on top. Suspicious payments can be challenged with authentication or held for review, while legitimate ones sail through.
At a glance
- Real-time ML risk scoring
- Device & behavioral fingerprinting
- 3-D Secure 2.0 step-up
- Fully configurable rules
- Fewer false declines
How It Works
How a transaction is evaluated
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1
Collect signals
Device, behavior, network and historical data are gathered.
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2
Score
The ML model returns a real-time risk score.
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3
Apply rules
Your rules decide to allow, authenticate, review or block.
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4
Act
Approved payments proceed; risky ones are challenged or held.
Features
Layered defense against modern fraud
Signals, authentication and rules working together.
Device fingerprinting
Identify returning devices and detect spoofing and emulation.
3-D Secure 2.0
Challenge risky payments and shift liability on eligible transactions.
Custom rules engine
Allow, review or block by amount, geo, BIN, velocity and more.
Velocity checks
Detect card testing and rapid-fire attempts across your traffic.
Manual review queue
Route borderline cases to a review queue with full context.
Benefits
Protect revenue on both sides of the line
Cut fraud losses
Stop fraudulent authorizations before they settle.
Reduce chargebacks
Fewer fraud approvals means fewer fraud-code disputes.
Fewer false declines
Precise scoring approves more legitimate customers.
Stay in control
Tune thresholds and rules as your risk appetite changes.
Results that compound
0%
Fraud loss rate
0%
Card-testing blocked
0ms
Scoring latency
0/7
Model monitoring
We had tuned our old rules engine so tight that we were declining good customers to keep fraud under control. Moving to risk scoring let us reverse that — fraud went down and approvals went up in the same quarter.
Who It's For
Who relies on fraud prevention
Ecommerce
Stores facing card testing and stolen-card attacks.
Digital goods
High-margin, instantly delivered products favored by fraudsters.
Travel
High-ticket bookings that attract organized fraud.
SaaS & fintech
Products where account takeover and payment fraud overlap.
Marketplaces
Platforms protecting buyers and sellers at once.
Specialized verticals
Businesses that must balance approvals with strict risk limits.
FAQ
Frequently asked questions
Every transaction is scored in real time using machine-learning models that weigh hundreds of signals — device, behavior, network, velocity and history — alongside rules you configure.
Our goal is to minimize false positives. Risk scoring and step-up authentication (like 3-D Secure) let you challenge only suspicious transactions while approving good ones frictionlessly.
3-D Secure 2.0 is an authentication protocol that verifies the cardholder with the issuer. On eligible transactions it also shifts fraud liability away from your business.
Yes. You can create custom rules by amount, geography, BIN, velocity, product and more, and combine them with the ML score to allow, review or block.
Directly. Preventing fraudulent authorizations reduces fraud-related chargebacks, which protects both revenue and your chargeback ratio.
Yes. Fraud signals are processed in a PCI DSS Level 1 environment, and sensitive data is tokenized and encrypted in transit and at rest.
Stop fraud before it costs you
See how real-time scoring and 3-D Secure protect revenue while approving more good customers.