Based on the structuring function of biological neural networks, Scalarr’s Neural Network is one of the most powerful machine learning models available on the market today. Scalarr’s Neural Networks are composed of hundreds of neurons that can recognize hidden fraud patterns and make associations, which are incomprehen-sible to humans and contrast the post-install activity. The ability to process billions of data points simultaneously, and self-train, consistently leads to the dramatically increased accuracy of fraud detection.
Scalarr's Unsupervised Machine Learning algorithm automatically discovers and learns fraud patterns from huge amounts of highly dimensional unstructured input data without any labeling or training, thus making it possible to uncover the most sophisticated types of fraud and abuses. Read More
Scalarr’s Semi-Supervised Algorithm is developed not only to learn from both unlabeled and labeled data, but make use of unlabeled data within a supervised learning framework.
Key Knowledge Database combines the intelligence of the industry’s shared fraud databases together with Scalarr’s own knowledge set, which includes millions of accounts in order to deeply enrich the data and leverage of accuracy in decisions made by Scalarr’s Machine Learning Engine.
By appending valuable data points, Scalarr enhances the fraudster’s profile in real time, getting more visibility into their algorithms and connections between them. Read more
Scalarr has dramatically increased the accuracy
of fraud detection
up to 97%
Scalarr Anti-fraud Engine optimizes the most advanced machine learning techniques and models to analyze data; providing the most accurate and meaningful decisions on the level of each conversion, reducing the cycle of ad campaign optimization and preventing fraud-related losses.
Full Scope of Technologies at Work
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The general principle of “mixes” grounds on the conscious use of several various types of fraud to get over the known protection measures of fraud ...
Fraud is an adaptive crime, so it needs special methods of intelligent data analysis to detect and prevent it