The Silent Partner: AI’s Role in Subscription Billing Security

The Silent Partner: AI’s Role in Subscription Billing Security

Credit card-related fraud costs the world a staggering $27.85 billion per year with experts projecting a sharp rise over the next decade. Nowhere is credit card usage more ubiquitous than the e-commerce channel. Whether the customer’s card is used for a one-time purchase, stored with their favorite merchant, associated with a service like Apple Pay, or used to power a subscription-based service, credit cards are the currency of the internet. 

Where recurrent, subscription-based payment models are concerned, 13% of all automatic credit card payments fail regularly. Sometimes it’s because of innocuous reasons. For instance, the customer’s card might have expired. Sometimes the customer may have had to cancel a missing card. In many cases, however, it is simply due to fraud. 

Companies that offer recurring billing methods as a payment option have to protect customers in the case that scenario number three rears its ugly head. It’s in the best interests of smart companies to leverage emerging AI technologies to help safeguard their clients.   

An Aggregating Risk of Fraud

We all know that registering a credit card online comes with a certain element of risk by default even on a normally secure site. What exactly is it about the subscription-based payment model that opens customer credit cards up to an additional level of fraud? The problem is an aggregated risk of fraud. 

With a recurrent billing model, the consumer’s card is charged repeatedly, typically on a month-by-month basis. Each new transaction represents a touchpoint where a potentially bad actor can get their hands on sensitive card data. Recurrent billing employs a clear-cut pattern which a thief could possibly discern more easily. Couple that with the fact that as new technology comes to market it’s not just businesses and the public that stand to benefit. Credit card thieves have access to the technology as well, making conditions prime for fraudulent behavior. 

How Does AI Protect the Consumer (And Your Bottom Line)

Online credit card fraud is an escalating problem. So how does artificial intelligence go about combating such an efficient and deceptive enemy?

It can learn the customer’s behavior 

AI can analyze other transactions made by the same customer, sifting through deep user data to pick up on normal patterns of credit card use. If anything seems off, the AI can suspend the fraudulent transaction before the damage is done. 

It can monitor transactions from start to finish

AI is capable of monitoring a robust data set as part of its daily activities. One of the most valuable data points includes end-to-end transaction monitoring. From the time a request leaves the user’s device, to the time it reaches the card’s issuing bank, AI has eyes on it, scanning for inconsistencies. Think of transaction monitoring as an early warning system. 

It can assist with payment validation 

Payment validation is an important step when it comes to recurrent billing. Every payment must ultimately go through some kind of payment authentication with the issuing bank. AI can help make that process more thorough and accurate, ensuring that only legitimate payments are transacted. 

Safe, Secure Subscriptions

The subscription-based pricing model is here to stay. Every manner of business imaginable has embraced the paradigm. But alongside prolific use comes an increased risk of fraud and theft. It’s up to the companies that offer recurrent billing to protect the integrity of their transactions as best they can. MaxQ Technologies can help.

MaxQ Technologies is an industry leader in the recurrent, subscription-based sales arena. We offer a suite of SaaS products designed to help give you and your customers peace of mind. Our integrated payment processors, such as PayPal, use AI and machine learning to keep subscription payments safe and secure while improving the customer experience.

To find out more about implementing a safe and successful subscription-based model, please contact us today

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