By Use Cases
By Industry
3DiVi Inc., founded in 2011, is one of the leading developers of AI and machine learning (ML) technologies for computer vision.
Let's talk business
Just fill in the form and we will contact you shortly
On-premise license request
Just fill in the form and we will contact you shortly
Your request has been successfully sent. We'll get in touch shortly.
THANK YOU!
Let's talk business
Just fill in the form and we will contact you shortly
Not All Users Are Equal: Why Risk Profiling is the Key to Secure Digital Identity Verification
One of the biggest blind spots we often see in digital services is treating every user as if they’re the same.
This misconception is especially risky in banking and fintech products where cybersecurity directly impacts user trust, loss prevention, and brand reputation.
Fraud, identity theft, and suspicious transactions don’t respond to a one-size-fits-all approach. They need a flexible, risk-aware model of user identification.
This misconception is especially risky in banking and fintech products where cybersecurity directly impacts user trust, loss prevention, and brand reputation.
Fraud, identity theft, and suspicious transactions don’t respond to a one-size-fits-all approach. They need a flexible, risk-aware model of user identification.
Why Treating All Users the Same is a Security Risk
Picture this: three people log into your system.
If you give them all the exact same identity checks, you’ll either annoy your best customers with unnecessary hurdles or leave the door wide open for fraudsters.
- Client #1 — brand new, no history
- Client #2 — a CEO handling major payments
- Client #3 — a loyal user with a spotless record
If you give them all the exact same identity checks, you’ll either annoy your best customers with unnecessary hurdles or leave the door wide open for fraudsters.
3 Steps to Build a Risk-Based Identity Model
NIST SP 800-63 Digital Identity Guidelines highlight that users need to be segmented by risk, transaction type, and access level. Simply put, different users carry different risks—and that means trust and control must be tailored accordingly.
Here’s how to do it in three steps:
1. Segment your users by risk.
2. Assign each group the right level of Identity Assurance (IAL), Authentication Assurance (AAL), and Federation Assurance (FAL) if needed.
3. Then dial your security measures accordingly — from simple passwords and SMS to biometric scans and hardware tokens.
An example of customer segmentation by typical risks is given below:
Here’s how to do it in three steps:
1. Segment your users by risk.
2. Assign each group the right level of Identity Assurance (IAL), Authentication Assurance (AAL), and Federation Assurance (FAL) if needed.
3. Then dial your security measures accordingly — from simple passwords and SMS to biometric scans and hardware tokens.
An example of customer segmentation by typical risks is given below:
The Benefits of Adaptive Authentication
- Stronger protection for high-risk users
- A smoother, hassle-free experience for trusted customers
- Clear, regulator-friendly justification for every security step
- Lower antifraud costs and fewer false alarms
- A solid base for next-level adaptive biometrics and behavioral security
Your Users Aren’t Equal — Your Security Shouldn’t Be Either
Risk-based client segmentation strikes the perfect balance between security and convenience. If you’re in fintech, microfinance, online lending, or any financial platform, it’s time to rethink your authentication strategies. Our users aren’t all the same — so your identity checks shouldn’t be either.
Curious about how to build this adaptive, risk-based digital identity model in your business? We’re always ready to share practical insights, real cases, and step-by-step recommendations. Let’s connect!
Curious about how to build this adaptive, risk-based digital identity model in your business? We’re always ready to share practical insights, real cases, and step-by-step recommendations. Let’s connect!
