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What is Unbotify?

The rise of bot fraud within apps poses a serious risk to businesses, as the mobile environment struggles to contend with a surge of automated bots that can wreak havoc on applications. Numerous app categories are at risk of having their entire user interactions hijacked for fraudulent purposes; for example, gaming apps might experience turmoil in their in-game economies, while e-commerce sites could endure significant financial data breaches. The advancement of bots has resulted in the development of highly refined versions that can imitate real user actions with astonishing accuracy, allowing them to circumvent traditional detection methods. With these sophisticated bots at their disposal, fraudsters can disrupt the user experience and reduce retention rates through tactics such as automated gameplay, account takeovers, credit card fraud, spam, and various other malicious activities. To combat this escalating issue, Unbotify has introduced the first patented machine learning solution specifically engineered to differentiate between real users and bots. Our cutting-edge technology utilizes a deep understanding of human behavioral patterns to accurately detect and eradicate advanced bot fraud that might otherwise go unnoticed, all while achieving a remarkably low false-positive rate. By adopting this groundbreaking solution, businesses can safeguard their app integrity while simultaneously fostering greater user trust and engagement, creating a more secure digital environment for everyone involved.

What is ShieldLabs?

ShieldLabs excels in identifying and preventing fraudulent activities while simultaneously assessing traffic quality through advanced scoring techniques. The company effectively addresses challenges like multi-accounting, account sharing, account takeovers, fake registrations, and advertising fraud. Utilizing a single JavaScript snippet, ShieldLabs captures over 100 unique signals from devices and networks with every visit, enabling thorough cross-verification that uncovers complex masking strategies and achieves an impressive detection accuracy of up to 99%. It can effortlessly recognize returning users, even when they have cleared their cookies, used incognito modes, or connected via different IP addresses, while also tracking linked users across various accounts and devices. Instead of merely applying a binary flag, the system categorizes a range of elements such as VPNs, proxies, Tor, Apple Private Relay, datacenter IPs, anti-detect browsers, browser automation tools, bots, and AI agents as distinct signals. Each visitor, user, device, and IP is assigned a unique risk score, with established patterns addressing prevalent fraud scenarios like multi-accounting, account sharing, account takeovers, and impossible travel incidents, and traffic quality is assessed based on its origin, channel, and campaign. Setting up this solution is a quick process, taking only five minutes and including client and server SDKs, public APIs, and signed webhooks, which makes it an appealing choice for businesses eager to bolster their security quickly. Consequently, organizations can effectively establish strong defenses against fraud while ensuring a smooth and uninterrupted user experience for their customers. Additionally, this comprehensive approach allows businesses to adapt and respond to emerging threats in real time, further enhancing their protective measures.

Media

Media

No images available

Integrations Supported

Beaver Builder

Integrations Supported

Beaver Builder

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Pricing Information

$79/month
Free Version
Free Trial Offered?

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Adjust

Date Founded

2012

Company Location

Germany

Company Website

www.adjust.com/product/unbotify/

Company Facts

Organization Name

ShieldLabs Inc

Date Founded

2025

Company Location

United States

Company Website

shieldlabs.ai/

Categories and Features

Categories and Features

Fraud Detection

Access Security Management
Check Fraud Monitoring
Custom Fraud Parameters
For Banking
For Crypto
For Insurance Industry
For eCommerce
Internal Fraud Monitoring
Investigator Notes
Pattern Recognition
Transaction Approval

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