Where Behavioral Identity Verification Changes Everything
Every organization that handles identity-sensitive forms has the same blind spot: they can verify the data, but not the person. Stradum closes that gap.
Financial Services
Your customer opened a checking account, a credit card, and a mortgage. Are you sure it was the same person each time?
The gap
KBA confirms the answers are right. Document verification confirms the ID is real. Device fingerprinting confirms the machine is recognized. None of these tools can confirm that the person at the keyboard for the mortgage application is the same person who opened the checking account six months ago. That question goes unanswered in every financial institution's current stack.
How Stradum helps
Stradum builds a behavioral profile from the first form interaction and compares every subsequent session against it — across products, across devices, across time. A new credit card application, a mortgage pre-qualification, a wealth account opening — each one is scored against the established identity signature. When the behavioral pattern breaks, Stradum surfaces it.
The signals that matter here
- Identity continuity across product lines — one behavioral profile, every touchpoint
- Familiar Deviation — detecting a family member or trusted insider opening a product on someone else's behalf
- Cross-product Shadow Profiles — clustering behavioral mismatches across accounts to detect organized fraud
- Zero friction — no additional steps for legitimate customers
Scenario
A customer opens a checking account in January. Their behavioral signature is established — keystroke rhythm, field navigation pattern, typing speed on specific letter combinations. In March, someone opens a credit card using the same identity. The data matches perfectly. KBA passes. The device is recognized. But the behavioral signature is different — the typing rhythm is wrong, the field hesitation pattern is inconsistent, the mouse movement doesn't match. Stradum scores it as a mismatch. The fraud team investigates. The checking account was legitimate. The credit card application was not.
Background Checks
The background check passed. But did the right person fill it out?
The gap
Background checks verify the data in the form — criminal history, employment records, identity documents. They do not verify that the person filling out the form is the person the check is being run on. Identity lending — where a worker with a disqualifying history uses a clean friend's credentials to pass a background check — is invisible to every background check provider in the market today.
How Stradum helps
Stradum sits on the background check form and builds a behavioral profile for each identity as it moves through the application process. When the same identity applies to multiple platforms, Stradum scores each session against the established signature. A consistent profile confirms identity continuity. A behavioral break flags the session for review.
The signals that matter here
- Cross-platform behavioral continuity — one identity, multiple platforms, one behavioral signature
- Reference source detection — catching applicants who paste data, switch windows to reference documents, or show read-type-check rhythm
- Shadow Profiles — identifying when the same unknown impostor has applied across multiple platforms under different identities
- Day-one detection — no baseline required for bot and automation detection from session one
Scenario
A gig worker with a DUI on their record wants to drive for a rideshare platform. They ask a friend with a clean record to fill out the application on their behalf. The friend's name, date of birth, and contact details are entered. The background check passes. But the behavioral signature — the way the friend types, navigates fields, and hesitates on specific inputs — is logged by Stradum. Three weeks later, the same friend fills out an application for a second platform under the same identity. Stradum recognizes the behavioral pattern. When the actual disqualified worker fills out a third application under yet another borrowed identity, Stradum's Shadow Profile system clusters all three mismatches. One person. Three identities. One behavioral fingerprint.
Insurance
The claim was filed by someone who knew every answer. That's exactly the problem.
The gap
Insurance fraud detection focuses on anomalous data — claim amounts that don't match incident profiles, addresses that don't match policy records, suspicious timing. What it cannot detect is a family member filing a claim on a relative's policy using perfectly correct information, typed naturally, from a trusted household device. Every signal in the existing stack says this is legitimate. It isn't.
How Stradum helps
Stradum's Familiar Deviation signal was built specifically for this scenario. When a claim session shows natural typing behavior — no pasting, no reference checking, no automation — but the motor patterns are inconsistent with the established policyholder signature, Stradum surfaces the discrepancy. The label is intentionally neutral: the insurer decides whether this is fraud, an authorized caretaker, or a legitimate exception.
The signals that matter here
- Familiar Deviation — the only signal that catches a known insider filing on a policyholder's identity
- Behavioral continuity across claim types — auto, home, health, renters — one profile, all products
- Natural entry detection — distinguishing between someone who knows the data from memory versus reading it from a document
- Review resolution — analysts can confirm or reject flagged sessions and the profile updates accordingly
Scenario
An elderly policyholder has filed auto and home insurance claims over several years. Their behavioral profile is well-established — slow, deliberate typing, consistent field navigation, long dwell times on specific keys. Their adult child, who has full access to all the policy information, files a new home insurance claim on their behalf without telling them. The data is perfect. The device is the family computer. KBA passes. But the typing rhythm is faster, the field hesitation patterns are different, and the motor memory signature doesn't match the policyholder's established profile. Stradum flags it as Familiar Deviation. The insurer investigates and discovers the claim amount had been inflated.
Healthcare & Benefits
Someone enrolled your employee's dependents. Was it actually your employee?
The gap
Open enrollment fraud — adding ineligible dependents, enrolling in benefits on behalf of someone else, proxy form completion by HR administrators — costs employers and insurers billions annually. Existing controls focus on eligibility verification: does this dependent exist on paper? They cannot detect whether the person filling out the enrollment form is the actual employee or someone acting on their behalf.
How Stradum helps
Stradum verifies behavioral continuity across every enrollment touchpoint. When the same employee enrolls in medical, dental, vision, and pharmacy benefits, each session is scored against their established behavioral signature. Proxy enrollment — where an HR admin, family member, or third party fills out forms on someone else's behalf — produces a behavioral break that Stradum surfaces without any friction to legitimate enrollees.
The signals that matter here
- Proxy enrollment detection — flagging when enrollment forms are completed by someone other than the employee
- Cross-product continuity — one behavioral profile across medical, dental, vision, pharmacy enrollment
- Familiar Deviation — distinguishing between the employee and a family member who knows all the right answers
- Baseline building across enrollment cycles — year-over-year behavioral comparison for returning employees
Scenario
During open enrollment, an HR administrator at a mid-sized company offers to help employees fill out their benefits forms. For most employees this is a convenience. For three employees, the administrator uses the access to add fictitious dependents to their plans — collecting the benefit payout differential. The employees' behavioral profiles from prior enrollment cycles are on file. The sessions completed by the administrator show a completely different behavioral signature — different typing rhythm, different field navigation, different hesitation patterns. Stradum flags all three sessions. The HR administrator is identified as the common behavioral actor across all three mismatches via Shadow Profile clustering.
The pattern is the same across every vertical.
Every organization handling identity-sensitive forms already has a stack — KBA, document verification, device fingerprinting, population-level ML models. That stack has one blind spot it cannot close on its own: it cannot answer whether the person filling out the form today is the same person who was here before. Stradum answers that question. One JS tag. Zero friction. Works with everything you already have.