Published on the 19/10/2015 | Written by Newsdesk
Retailers gain real time access to risk profiles based on more than 12 years of retail transaction data..
Ecommerce enabler eStar has launched a standalone SaaS-based tool to protect retailers from fraud by automating online order risk analysis. The company said its Risk Management Profiler (RMProfiler) has dropped fraud rates to between 0.2% – 0.05% against a global average of 1% among those of its clients using the service.
Configurable to suit individual merchant risk profiles, RMProfiler draws on eStar’s 12 years of analysing eCommerce fraud data from 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, or manual review of orders, but these methods fail to detect dishonest behaviour by customers using their own cards. So called ‘friendly fraud’ scenarios are responsible for over 60 percent 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.
eStar CEO Andrew Buxton said online fraud is closer to home than retailers might think. [It] can be any purchase value made by any purchase method, and much of it originates from legitimate but dishonest buyers.”
Combating online fraud, said Buxton, 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 New Zealand and international retailers, reducing the financial and reputational burden of fraud.”
Developed with Callaghan Innovation funding, RMProfiler’s risk model delivers a profile of Australasian online purchase behaviours more accurate than previously available.
eStar said RMProfiler applies heuristic and algorithmic analysis to instantly compare orders against risk metrics which include known fraudulent addresses, data consistency analysis, order velocity and value, and customer behaviour. It also validates payment data, addresses and checks related orders.
