Credit Card Batch Processing Optimization

on Oct28

Written by

James Davis

Senior Technical Writer
at United Thinkers

Author of the Paylosophy blog, a veteran writer, and a stock analyst with extensive knowledge and experience in the financial services industry that allows me to cover the latest payment industry news, developments, and insights. Read more

Reviewed by

Katherine Pensatori

Reviewed by Katherine Pensatori

Product Specialist at United Thinkers

Product specialist with more than 10 years of experience in the Payment Processing Industry. I help payment facilitators and PSPs solve their various payment processing issues. Read more

The purpose of this article is to familiarize payment card industry players with the concept of credit card batch processing optimization. Particularly, the article addresses several techniques intended for increase of approval rates in recurring billing and bill payment scenarios, where batch transaction processing is widely implemented. The two approaches to credit card batch processing optimization to be addressed are account number aggregation and account number optimization.

Account number aggregation concept

One of the techniques to increase approval rates of credit cards processed in batch is transaction aggregation (or account number aggregation).

The concept is as follows. When a batch of transactions comes in, and it has multiple transactions with the same account number in it, these transactions are aggregated into a single transaction (by account number).
Such aggregation is performed for the following reasons:

Reduction of transaction fees paid by the merchant through reduction of the total transaction count (since there are per-transaction fees to be paid)
Consolidation of charges for the cardholder, since only one transaction is shown on cardholder’s statement. (Sometimes cardholder is using the same card for multiple accounts, and he or she might get falsely alerted seeing multiple transactions from the same merchant on a bank statement)

Example

A customer signing up for a health club creates an account for himself, an account for his wife and an account for his mother. He uses the same card on all three accounts and gets hit with three charges. Account number aggregation allows for consolidation of such charges into a single one.

The disadvantage of the technique is that, in a sense, account number aggregation can minimize the chance of collecting on certain accounts. For this reason the first technique is mostly used on files containing original attempts to collect the money and not reattempted declines.

Example

There are three agreements ($25 each), which are aggregated together into a single transaction of $75. In many cases there is higher likelihood of collecting $25 as opposed to $75. On $75 transaction the merchant may get a decline due to “insufficient funds” on the customer’s card, while a $25 transaction could have gotten approved.

Account number optimization concept

Because of the disadvantages of the previous approach, another credit card batch processing optimization technique (sometimes called transaction optimization or account number optimization) is used in order to ensure higher likelihood of collecting.

The concept is as follows. When a batch of previously declined transactions comes in, and it has multiple transactions with the same account number in it, the transaction with the smallest amount is reattempted first. If the reattempt is successful, all remaining transactions with the same account number can be submitted; while if the smallest-amount transaction doesn’t come through, there is no point in resubmitting other ones.

The advantages of this approach are as follows

There is a higher probability of collecting
The merchant doesn’t have to pay per-transaction processing fees for pointless reattempts

For these reasons this technique is more often used on files, containing reattempts (transactions that have previously been declined), i.e. in decline recycling.

Let us consider an example, illustrating the transaction optimization principle.

Example

A batch file with decline reattempts contains three transactions with a same account number: one for $10, one for $25 and one for $50. Since we know that the transactions were previously declined, there is a probability that these transactions will get declined again. While all transactions could be aggregated into a single one, doing so will further reduce the chance of collecting. The original file could be resubmitted as it is, but it’s not the most efficient way either. The recommended approach is to try submitting the transaction with the smallest amount of $10 first. Then, once it succeeds, the remaining transactions can be resubmitted. However, if the first one ($10) fails, there is no need to try resubmitting $25 or $50.

One of the challenges for companies that rely on recurring billing is that the first billing transaction often happens at least 30 days after the customer’s initial subscription to the service. By that time, many customers tend to forget what the service was. Consequently, it is critical for the descriptor of a recurring payment transaction that appears on the bank statement, to be clear for the customer. Our next article will provide detailed explanation of credit card and ACH transaction descriptors known as dynamic descriptors or soft descriptors.