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eStar helps Aussie businesses combat online fraud with new SaaS tool

Retailers gain real time access to risk profiles based on more than 12 years of retail transaction data

MELBOURNE, Australia, 19 October 2015 – eStar, Australasia’s largest specialist eCommerce solutions company, has launched a standalone SaaS-based tool to protect retailers from fraud by automating risk analysis of online orders.

Risk Management Profiler (RMProfiler) aims to reduce the ratio of fraud experienced by local retailers to between 0.2% - 0.05%, against a global average of 1%. Configurable to suit individual merchant risk profiles, RMProfiler uses patterns and analysis gained from eStar’s 12 years of analysing eCommerce fraud data to achieve this, from a client base including some of the largest retailers in New Zealand and Australia.

Many retailers rely on a simple blacklisting of credit cards which have been proven fraudulent, which fails to detect dishonest behaviour by customers using their own cards. So called ‘friendly fraud’ scenarios are responsible for over 60% of all claims, for example when a customer claims a refund from their credit card company by disputing they received the goods, claim they never ordered them, or that there was a problem with the product.

“Online fraud is much closer to home than retailers might think, and can be any purchase value made by any purchase method, and much of it originates from legitimate but dishonest buyers”, said Andrew Buxton, CEO of eStar.

“Combating online fraud requires retailers to do more than just use a reputable payment gateway and follow normal security practices with credit cards. RMProfiler makes accurate fraud prevention more cost effectively available to all Australian retailers, reducing the financial and reputational burden of fraud.

”Reducing this burden has been one of the greatest benefits to businesses already using RMProfiler. “Risk profiling has helped us avoid fraudulent transactions that would have been quite costly to our business,” said Debbie Gibson, owner of One Rugby and Oz Sports Direct.

RMProfiler applies heuristic and algorithmic analysis to instantly compare orders against risk metrics:

  • Known fraudulent addresses.
  • Data consistency analysis.
  • Order velocity and value, and customer behaviour.
  • Payment data validation and comparison.
  • Address validation.
  • Related orders and fuzzy pattern matching.

Order information is submitted to the RMProfiler service either via a secure RESTful HTTP or SOAP API’s, and can be entered via an order entry screen for low volume use. This information is analysed and a decision based on fraud score can be applied through business rules or user interface.

www.rmprofiler.com

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