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Simility

Name: Simility
Rating: 4.5 (2 reviews)

By Simility

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Highlights : About Simility

Device ID recognizes fraudsters' identities

Machine learning continuously refines detection

Create, edit, and test custom manual rules

Workflows

Feed unstructured data to a Simility fraud model

About Simility

Simility is a cloud-based fraud detection software platform founded by some of Google's fraud fighting teams. Simility analyze millions of transactions per day and flag those recognized as suspicious. Common types of fraud include stolen credit cards, message spamming, account takeover, fake reviews, and promotional bait. Simility's platform allows users to define any undesirable behaviour as fraud, so they can detect more subtle behaviour’s like policy violations and inter-member harassment. Simility's entire platform works right out of the box, but is also highly customizable. Every aspect of the user interface can be customized so that analysts see the important information first, yet have access to all data available, so stay efficient. Manual rules and machine learning models can be tuned in minutes.

Specifications

Business Size

Mid-Market

Deployments

Cloud

Language Support

English

Platforms

Product Details

Benefits

Start fighting fraud immediately.

Send unstructured data through Simility’s iOS and Android SDKs or Javascript snippet.

Analyze fraudulent user behavior

Analyze fraudulent user behavior with Simility's existing models, based on experience in a number of verticals. Allow analysts to customize detection mechanisms with their own hypotheses of how to catch more fraud.

Custom built machine learning

Simility's custom built machine learning improves fraud detection. Every decision made will be automatically fed back into the machine learning algorithms to catch fraud that's near impossible to see with a human eye.

Automate analysts' workflow.

Simility's graphical interfaces allow analysts to see the pattern in any fraudulent behaviour. A manual rule builder allows analysts to easily edit their own rules, so they can automate their own work and plug their intuitions into the machine's fraud model.