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Sept 2016/Issue 1094

Simility Fraud Detection

 As transactions became omnichannel and fraudsters became more adept, no single technology provider could meet Google’s risk management needs. Fraud fighting platforms had to be built in-house that were flexible and able to quickly adapt to meet new challenges. Former fraud fighting executives at Google formed Simility in May 2014 to further that concept. Simility can provide clients with machine learning and manual rules-based technology capable of building and deploying risk management models in hours after spotting new threats.

dashboards

They can do that, in part, because clients receive dashboards that provide a graphic interface to examine data points and relevant patterns of suspicious activity, not tables of information. The Simility platform can also absorb client data in any format, including unstructured feeds. It can write a natural language rule in real time.

Simility’s first customers were ecommerce marketplaces. Soon, payment gateways used by those clients asked for help with fraud fighting models.  More recently, card issuers and merchant acquirers have become clients. Simility has clients in the U.S., Africa, Asia, and Latin America.

Simility tests its service against any potential client’s existing fraud fighting tools in side-by-side comparisons that measure three main metrics — cost and ease of operations, fraud fighting success, and reduced instances of false positives for fraud. Simility says it has never lost on a side-by-side performance comparison.

One feature Simility excels at is providing to ecommerce merchants risk management on transactions involving online payments where goods are shipped to a store location for pickup. 

Simility’s technology is sensitive to the sharing economy, where fake sellers and fake buyers launder money and cash-out stolen prepaid cards. For card issuers, its technology is strong at fighting money laundering through prepaid card reloads including opening new accounts, particularly when the application originates on a mobile device.

Simility can also take issuer data from existing purchases and other back- office functions to make predictive models. It says it is significantly less expensive than similar services from top vendors. Its off-the-shelf technology can be customized to create models in a few days.

It charges a per-transaction fee on a total-volume basis. Most clients use it on a software-as-a-service basis, sending data through a mobile SDK or Java script. It can manage an integration in less than a week.

Rahul Pangam is CEO at Simility in Palo Alto, California, (650) 351-7592, rahul@simility.com, www.simility.com.

© 2016 HSN Consultants, Inc. THE NILSON REPORT

 

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