Verify the User,
Not Just the Credentials
Distinguish between genuine users, fraudsters, and bots in real-time using passive Behavioral Biometrics. Stop sophisticated frauds without adding friction.
What does behavioral biometrics do?
Sign3 Behavioral Biometrics creates a unique user profile based on thousands of micro-interactions like typing cadence, scrolling pattern, mouse path, etc. We compare each live session against historical profiles and risk patterns to flag bots, RATs, social-engineering influence, and mule behavior while letting trusted users glide through.
Watch this 2-minute video to see how Sign3’s technology instantly distinguishes a genuine customer from a sophisticated bot or a human fraudster in a live session.
Credentials Can Be Stolen.
Behavior Can’t
Traditional security relies on what you know, like passwords, and what you have, such as OTPs, but in the age of sophisticated phishing and high-velocity bot attacks, these barriers are failing. The reality is that fraudsters often possess the correct login credentials, bypassing standard checks with ease. This creates a critical security gap where the only way to stop the attack is to stop verifying the data and start verifying the actual person holding the device.
What Signals We Analyze
Keyboard dynamics
Typing speed, hesitation, dwell/flight times, segmented typing.
Mouse & pointer behavior
Trajectory, micro-corrections, pause patterns, erratic movement.
Touchscreen interaction
Press size/area, pressure, swipe velocity, gesture fluency.
Advanced fingerprints
Bot/script signatures, remote-access tooling hints, emulator/headless tells.
Scrolling & navigation
Rhythm, bounce, revisits, form-field order, copy-paste/autofill usage.
Session behavior
Dead time, focus shifts, windowing, replay artifacts, automation traces.
Device motion & sensors
Orientation shifts, micro-tremors, gyroscope patterns.
THE IMPACT
Our Results are Remarkable
Reduction in ATO:Drastically cut account takeover losses by flagging anomalies before funds leave the wallet.
Faster Investigations:Give analysts a clear “Risk Score” instead of raw data, reducing manual review time from minutes to seconds.
Added Friction:Validate 99% of genuine users in the background without ever showing a CAPTCHA.
Use Cases
Account Takeover (ATO)
Fraudsters may have the correct credentials, but they can’t mimic the genuine user’s muscle memory. We flag sessions where the “human” behind the screen changes.
Detecting Social Engineering
Detects when a genuine user is being coerced or coached into making a transfer by a scammer on the phone.
Bot & Script Prevention
Differentiates between human imperfection and robotic precision to stop credential stuffing and inventory hoarding.
Mule Account Prevention
Identifies accounts being used to “wash” stolen funds by analyzing the intent and familiarity of the user.
Blocking Risky Transactions
Transaction monitoring rules (like velocity limits) are reactive. Behavioral intelligence adds a proactive layer by analyzing the user's confidence and focus during the checkout or transfer moment.