Reduce Fraud Without
Sacrificing Customer Experience

The $1.9 trillion e-commerce market is ultra competitive, with sellers focusing on streamlining the entire process—from product discovery to cart checkout. The size of the market makes it very lucrative for fraudsters, who focus on committing fraud at every step in the process.

Combining the powers of human intuition and machine learning, Simility surgically and proactively roots out the bad guys and helps you identify returning good customers so you can focus on growing your e-commerce business. Simility provides an all-in-one solution that can tackle the diverse problems e-commerce companies face, from fake account creation and coupon abuse to delivery fraud and payment fraud.

e-Commerce Companies that Benefit from Simility’s Solutions


What Fraud Problems Can Simility Tackle?


Fraudsters create fake accounts at scale. More importantly, they do so by creating sleeper accounts and know how to cover their tracks.


Fraudsters often abuse marketing programs meant to incentivize new user sign-ups via referrals and promotions.


Fraudsters use stolen cards to buy from legitimate sellers. Online retailers are left holding the bag for chargebacks.


Fraudsters place multiple “Cash on Delivery” orders to fake destinations, leaving the e-commerce vendor with no inventory for legitimate sales.

Why Simility?

Detection Combines Machine Learning and Manual Rules

  • Adaptive machine-learning models are customized for your use case and evolve with the changing nature of fraud.
  • Your analysts can create, edit, and test new rules in minutes instead of weeks with our easy-to-use rules UI.
  • Rules can be auto-tuned based on rejected transaction information.
  • In-session monitoring detects suspect activity in mobile app or browser.

Device Recon Uncovers Similar Fraudster Devices

  • Identify unique devices, mobile or desktop, by their device fingerprints; helps identify buyer/seller collusion.
  • Use device risk scores–computed using proprietary algorithms–to determine fraud probability for any device, even one that has never been seen.
  • Uncover buyer seller collusion by identifying whether two different device fingerprints–highlighted by clustering algorithms–are likely to belong to the same fraudster.

Data Visualization-Enabled Human Analysis

  • Visual graph analysis allows analysts to link sellers, buyers, and transactions via entities like emails and IP addresses, saving weeks of manually working through spreadsheets.
  • Personalized case management, flexible UI, and activity dashboards highlight relevant information for fraud analysts, reducing manual review time by up to 80%.

Data Scientist-As-A-Service

  • Our data scientists provide you the option to extend your team, on an as-needed basis.
  • Data scientists help with model building, tuning, governance, and validation. Fraud trends change over time, and this option keeps your models performing at optimum levels.

Quick Start

  • Get started within hours by pasting a line of JavaScript and/or integrating our mobile SDKs.
  • Use out-of-the-box predefined models that are ready to combat the most common types of marketplace and classified fraud problems.

Case Study: Event Tickets

Scalpers, chargebacks, and manual reviews were hindering the growth of a leading online ticketing company. Simility paired device fingerprinting with machine learning for fraud prevention, enabling the ticketing company to reduce customer friction, increase fraud catch, and accelerate revenue. Learn more…

Related Resources

Comprehensive Fraud Prevention: Empowering Merchants to Offer Seamless Customer Experiences
Onboarding New Customers Among a Host of Stolen & Synthetic Identities
How to Choose a Great Fraud Solution
Solution Guide
Increasing Loan Acceptance Rates While Automatically Detecting Fraudulent Applications
Case Study
Simility Enables Republic Wireless to Automate Approvals and Reduce Chargebacks

Shall We Get Started?

Get your personal demo of Simility today.