Insurance&Tech_TLB_12.12.19

Simility

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430 Sherman Ave, Suite 212
Palo Alto, CA 94306
About Simility
  • Simility is a cloud based fraud detection and risk management company located in Silicon Valley that specializes in combining two valuable insights. The first being combing the power of algorithms to recognize similar and dissimilar signals with the ability for humans to create meaning out of them. The second is giving front-line fraud fighters tools that empower them to put their domain expertise and knowledge to use without needing to write code. Simility helps companies fight fraud, abuse and other similar areas that are challenges for businesses. Simility analysis millions of transactions a day and flags those recognized as suspicious. Simility's entire platform works right out of the box, but it is also highly customizable. Every aspect of the user interface can be customized so analysts can see the important information first, yet have access to all data available, so they stay efficient. Manual rules and machine learning models can be tuned in minutes.
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Products by Simility
  • Device Recon

    Device Recon analyzes hundreds of mobile and desktop device characteristics and behaviors–including browsers, language, location, operating system, even mobile emulation, and battery level–to fingerprint devices. Fraudsters can mask identifying properties like their username, email, and IP... Read more
  • Augmented Analytics

    Augmented Analytics combines the power of human analysis and machine learning to uncover the fraudulent behavior hidden within your structured and unstructured data. What is fraud analytics? Most fraud models consist of hundreds of manual rules and logic statements that describe behaviors... Read more
  • Workbench

    Simility pulls huge volumes of data from disparate sources, and the most relevant signal is presented to analysts in a single interface so they see the signal without noise. Manual rules, machine learning and Device Recon data are all incorporated into a single fraud profile for each user. The... Read more