Comprehensive fraud detection requires dealing with an increasing variety, volume, and velocity of data. Simility provides fraud & risk professionals with powerful tools to collect any data, create powerful models assisted by advanced machine learning, and a rich workbench to analyze results, without writing a single line of code. Extreme flexibility and powerful analysis at your fingertips.
Powerful analysis starts with data, a lot of it, and we realize that your unique business has unique data. Simility enables enhanced investigation amidst data coming in from an increasing number of sources in a variety of formats.
Our beacons, APIs, and SDKs generate valuable data directly from your site and mobile app. Collecting and transforming custom data feeds from virtually any source is easy and straightforward with Simility’s intuitive interfaces. Simility makes it quick and easy for your analysts to ingest, transform, and analyze any form of data.
Dynamic ontology structure to determine relation between data points
Data is modeled in a flexible graph
of objects and relationships
Combining machine learning with human intelligence creates a powerful blend to continuously adapt and outperform fraudsters as your business evolves. Manual rules codify analyst insights and are fed into the machine-learning models. Our machine-learning engine begins with experience from an array of business verticals and adapts to your unique business needs.
With every intuition added, your fraud prevention adapts and evolves, refining and improving the detection of anomalous patterns.
Our ability to accurately detect fraud while identifying good users is powered by our smart machine-learning models that evolve based on every decision and rule, continuously enhancing the detection of anomalous patterns. This means your model improves its defenses while you sleep. Simility’s machine-learning engine detects patterns of fraud before they are perceptible to human analysis or become a problem, keeping you ahead of the fraudsters.
Data is only useful if it is presented in a clear and actionable manner to decision-makers. Simility’s Workbench empowers analysts to encode their intuition, visualize patterns, and make decisions on entire networks in one click.
Machine learning, manual rules, behavior analysis, and device fingerprinting data are displayed in a single analytics view so your analysts don’t have to toggle between data analytics interfaces to see the whole picture. Use sophisticated data visualization with slicing and dicing features to identify patterns and relationships and screen for fraud. Simility simplifies the fraud-screening process.
Cookie-cutter, no-touch fraud detection techniques work only for simple businesses. When your enterprise is more sophisticated, you need a solution that’s fine-tuned to your unique requirements—but is still simple to deploy.
Simility was designed to be personalized with ease and agility, without the customer writing a single line of code. Our expert engineers, scientists, and data analysts ingest your data, developing cutting-edge fraud and risk models. Within days, you’ll have a customized fraud-prevention solution that’s specific to your business needs.
Collects and analyzes data from every possible source and detect patterns to identify deep rooted fraud.
Simility plugs into your application, site or mobile app to capture data directly and enriches it with custom data feeds.
Review the numerous attributes like device fingerprinting, proxy filtering, behavioral analytics, which are connected to a transaction.
Simility enriches this data from external data sources and social networks and analyzes through its ontology classification and dynamic data linking how the transaction and users connects in ways.
Our sophisticated and patented machine learning models, which are further customized for each customers, analyze this information to make automated approve or reject decisions or if required can flag a small fraction for analyst review.
Get answers not just a score. Simility provides visual link analysis, trend analysis, queries, reports, and scores, to help analysts make manual decisions and provide feedback to enrich the machine learning models.