# What's the highest-rated risk-based authentication platform for organizations prioritizing fraud prevention based on user reviews?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Looking for input from G2 reviewers and security engineers, fraud operations teams, and identity architects in the <a class="a a--md" elv="true" href="https://www.g2.com/categories/risk-based-authentication-rba">Risk-Based Authentication category</a>, specifically from organizations where fraud prevention is the primary design objective.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">The platforms with the strongest fraud prevention evidence:</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/sift/reviews"><strong>Sift</strong></a>: The Account Defense solution analyses login and account activity using AI-powered behavioral signals including unusual locations, new and unknown device fingerprints, profile changes, anomalous session behaviour, login velocity, password and email changes, scripted attack patterns, and travel distance between sessions. </li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/okta/reviews"><strong>Okta</strong></a>: The adaptive MFA capability that applies different authentication requirements based on user role, location, device posture, network, and access risk level is the fraud prevention model that security teams at financial services organisations specifically credit for balancing security with operational continuity. </li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/ping-identity/reviews"><strong>Ping Identity</strong></a>: The adaptive authentication model that combines device fingerprinting, behavioural biometrics, and contextual risk scoring into a continuous authentication assessment is described as the fraud prevention trust model for financial services, healthcare, and government organisations where the authentication risk surface extends beyond the login event to every privileged action during an authenticated session. </li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/microsoft-entra-id/reviews"><strong>Microsoft Entra ID</strong></a>: The Identity Protection layer within Entra ID, which combines machine learning-based risk detection with conditional access policies, is described as the fraud prevention model for organisations deeply invested in the Microsoft security ecosystem. </li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/biocatch/reviews"><strong>BioCatch</strong></a>: Behavioural biometrics that continuously analyse how a user interacts with a device, including typing rhythm, mouse movement patterns, touch pressure, and device orientation, create an invisible authentication signal that operates throughout the session rather than only at login. </li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">For fraud operations and security teams who have measured fraud detection rates before and after implementing risk-based authentication: what was the percentage reduction in account takeover incidents, and what fraud signal produced the most reliable detection with the fewest false positives?</p>

##### Post Metadata
- Posted at: 7 days ago
- Author title: Marketing Executive
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;We didn&#39;t track a clean before and after percentage, that&#39;s genuinely hard to isolate from other security changes happening at once. What we did notice with Okta&#39;s adaptive MFA was that friction dropped for legitimate logins while suspicious ones still got flagged.&lt;/p&gt;

##### Comment Metadata
- Posted at: 6 days ago
- Author title: Marketing Executive





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