Fraud Prevention & Risk-Based Onboarding Overview
Integrate FrankieOne’s fraud signals and risk-based decisioning into your onboarding flow with OneSDK and our Onboarding API.
Introduction
The biggest challenge businesses face today is balancing strict compliance requirements with a smooth user experience during onboarding. This often leads to high friction for low-risk users and not enough scrutiny for high-risk profiles. Our solution adapts the verification process to each user’s unique risk profile, ensuring low-risk users enjoy a fast, frictionless journey while high-risk applicants undergo additional, necessary identity checks.
How Risk-based Onboarding Works
Our Risk-based Onboarding model assigns each user a real-time risk score, categorizing them as low, medium, or high risk. This allows you to apply the right level of verification at every step, blocking fraud early without adding friction for genuine customers.
Initial Fraud Signal Assessment
As a customer submits their details, our solution assesses fraud risk in the background using real-time intelligence from device, phone, and email signals. This happens instantly without disrupting the user’s experience.
Determine Risk Score
Based on the initial signals, a risk score is calculated to determine if the user is Low, Medium/High, or Very High Risk.
Apply Dynamic Verification The user’s journey adapts based on their risk level.
Low-Risk users proceed with minimal checks and can be onboarded quickly.
Medium/High-Risk users are prompted for additional verification steps, such as KYC checks, ID validation, or biometrics.
Very High-Risk applications can be rejected immediately, stopping bad actors before they create an account.
Key Features and Signals
Our solution leverages best-in-class providers to assess risk in real-time, using a powerful combination of device intelligence and behavioral biometrics to filter out bad actors.
Identifies devices through cookies and device fingerprinting to flag suspicious sessions. Signals include:
Emulator, Proxy, and VPN detection
Browser fingerprinting and Device ID
Remote desktop detection
Detects fraudsters through their intrinsic behaviors and patterns during the onboarding process. Signals include:
Typing and mouse signals
Use of copy, paste, and autofill functions
Hesitation, distraction, and context switching
Validates user-provided information against trusted data sources.
Email Risk Assessment: Checks email age, domain risk, and associated account behavior.
Phone Risk Assessment: Determines if a phone is VOIP or a landline and analyzes its behavior.
IP Location Intelligence: Detects proxy use and analyzes risk based on IP country, city, and timezone.
Integration essentials
Use our Onboarding API to perform email and phone checks, and retrieve combined risk scores.
Capture device and behavioral biometrics data with our OneSDK for web and mobile platforms.
Access fraud insights and manage results through our user-friendly portal.