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What is Scalarr?

Scalarr presents an innovative solution for detecting mobile ad fraud by leveraging state-of-the-art Machine Learning technology. To address the most pressing issues in mobile advertising, Scalarr implements a dual-layered strategy featuring next-generation algorithms that boast an outstanding accuracy rate of up to 97% in recognizing various types of in-app fraud. Users can take advantage of Scalarr’s offerings by delving into its capabilities to monitor, assess, and prevent mobile app install ad fraud through its unsupervised machine learning functionalities before any damage occurs. The platform effectively employs both unsupervised and semi-supervised machine learning methods to autonomously identify and comprehend fraud patterns within extensive datasets. By analyzing numerous clicks, installs, and post-install event variables, Scalarr greatly reduces the occurrence of both false positives and false negatives in its detection framework. With a sophisticated model designed to prioritize accuracy and thoroughness, Scalarr emerges as a powerful tool that delivers actionable insights at the individual conversion level, enabling advertisers to make well-informed decisions about their ad campaigns. This holistic methodology not only fortifies the integrity of mobile advertising efforts but also empowers marketers to optimize their strategies for better performance and outcomes. Ultimately, Scalarr’s commitment to innovation ensures a safer advertising environment for all stakeholders involved.

What is Confiant?

At Confiant, we prioritize transparency in our data, maintain current research, and offer a highly customizable anti-Malvertising platform, empowering you to choose your level of involvement and control. You can utilize the most advanced ad quality and security solutions, complete with the essential data and actionable threat intelligence to enhance your digital safety. Additionally, our platform features sophisticated ad tech integrations, ensuring you enjoy unmatched visibility across all demand sources for publishers and ad platforms, as well as mobile app publishers through our SDK. Since our inception in 2013, we have dedicated ourselves to safeguarding online users and organizations of every size. Clients such as Microsoft, Insider, and IBM place their trust in Confiant, backed by our team's collective experience of over a century in online security. Moreover, we have developed the industry's first and only Malvertising Attack Matrix, which details the tactics, techniques, and procedures currently employed in malvertising, allowing you to remain proactive. Our platform processes trillions of ad transactions and identifies millions of malicious incidents each month, continually improving to ensure optimal protection for our users. We are committed to evolving our solutions to meet the ever-changing landscape of digital threats.

Media

Media

Integrations Supported

Adjust
AppsFlyer
Branch
Kochava
Singular
Tenjin
mParticle

Integrations Supported

Amazon API Gateway
Amazon Ads
Google Ad Manager
Insticator
MediaMath
Prebid
PubMatic
Raptive
Vuukle

API Availability

API Availability

Has API

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

Contact for quote.
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Online Training

Training Options

Documentation Hub
Online Training

Company Facts

Organization Name

Scalarr

Date Founded

2016

Company Location

United States

Company Website

scalarr.io

Company Facts

Organization Name

Confiant

Date Founded

2013

Company Location

United States

Company Website

www.confiant.com

Categories and Features

Ad Fraud Detection

Not specified

Click Fraud

Not specified

Categories and Features

Ad Fraud Detection

Not specified

Click Fraud

Account Alerts
Activity Monitoring
IP Address Monitoring
IP Blocking
Risk Assessment

Display Advertising

Not specified

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