Payments are so ubiquitous that we often forget that each one is a unique combination of data points that are catapulted across providers, players, systems and ecosystems with one outcome in mind—to be approved. But behind the simplicity of this equation are millions of optimizations that need to take place to ensure success.
When optimized correctly a payment creates, captures and compounds in value. When done wrong it’s not just a lost payment, it’s lost revenue and lost customers for your business. Checkout.com has partnered with Oxford Economics to actively track and measure the cost and losses of payment performance since 2019.
In 2022 as much as $50.7 billion in lost revenue occurred due to false declines and that number is growing. Worse yet, the contributing factors for those declines were often variables beyond a merchant’s sphere of influence and sat firmly with issuers, schemes and their payment providers.
So as a business that likes revenue how do you combat the false decline problem if you don’t have the control, data or influence to inspire change? How do you determine how big this performance problem is for your business? The first step is unboxing payments.
Unboxing the payment black box
The payment sphere involves multiple players like issuers, schemes, and payment service providers, each with unique frameworks, rules, preferences, and technologies impacting processing. These details affect performance, contributing to the $50.7 billion issue.
For instance, SCA exemptions have varied routes and types, chosen by players through approval or decline codes. Complexity grows with evolving preferences and technologies. New instruments like Network Tokens and 3D Secure 2.3.1 evolve at varying paces based on regions and roadmaps. Legacy systems, often patched, harbor bugs and inefficiencies. All payments navigate these layers.
Amidst this complexity, modern PSPs employ AI but often lack transparency in optimization results. This prompts questions about approval in such an intricate environment. The truth is that while AI aids, visibility into optimization success, including decline resolution, remains lacking for many customers.