Supporting Local Payments on a Single Global Platform

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Smart processing

November.13.2014 0 Comments


Smart Processing for Recurring Payments has been in the business of processing for recurring payment merchants for over 10 years.

Over the years we have developed a whole range of data analysis tools, real time payment flow direction systems, after sale analysis methods (and a lot more), that will allow you to manage your recurring business model effectively and successfully.


Main Challenges for Recurring Payment Models

We understand recurring business models very well. And we understand that as a result of a recurring business model, higher levels of fraud and chargebacks are the main threat to the continuity of your business.

That is why we fight them on each of 4 stages of payment process:



Pre-Offline Analysis


As a first step we analyze your current traffic and payment flow.

We take as much data as we can (e.g. transaction card type, brand, card level, issuing bank BIN, affiliate ID’s etc.), find payment flow patterns and come up with a number of recommendations to improve the current ratios. You can take only our recommendations or try to process with us in our platform fully setup for your business model and payment behavior.


Tools we use:

  • Pre offline data analysis
  • Payment behavior pattern analysis
  • Developed a set of recommendations
  • System based simulations to measure effect and fine-tune parameters
  • Dynamic BIN management
  • IP blocking simulations to identify chargeback and fraud origins
  • Affiliate traffic analysis

Resulting in specific configuration of our system based on dynamic parameters, even BEFORE you start processing.


One of our clients, a large dating merchant in the US, had chargeback levels which were very close to the maximum allowed limits. Due to competitive pressure they wanted to increase their affiliate network to attract more traffic. Obviously the risks that a more ambitious affiliate network campaign would have them exceed chargeback limits were real.

We analyzed their current traffic, focusing on dynamic BIN analysis to identify main sources of chargebacks. We simulated their previous 6 month processing data on our system and through dynamic BIN management we were able to reduce their current chargeback levels significantly. Obviously allowing them to use the slack we created to approach market more aggressively.

We reduced their current chargeback levels and allowed them for an increase of 30% more traffic, increasing their market share and conversions, whilst still being below maximum thresholds.


Total estimated sales increase is 35-40% for this merchant.

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