Rewriting Embedded B2B Finance with AI

Part 2 of 2

Part 1 of our embedded finance blog series highlighted why banks should embrace APIs and blockchain. Part 2 looks at the challenges and opportunities with commercial payments.

Embedded finance has already made its way into the consumer space, where you can simply tap on your phone to pay a bill or complete an e-commerce transaction. In the business-to-business (B2B) world, however, things have not gone as seamless.

Why is that the case? With embedded finance in B2B, companies are dealing with larger transactional amounts, stricter regulatory compliance, a need for stronger data security and integration complexity.

The larger transactions, in particular, are a critical element.

Business invoices can involve hundreds of thousands of dollars. They may have comprehensive extended payment terms and complex compliance rules, which can vary per region.

But what’s happening with transactions on the consumer side — simplicity, instant payments and technologies being embedded into platforms outside the traditional financial arena — does offer real-world examples and insights for B2B finance.

For instance, changing customer expectations and the need for frictionless, secure and personalized customer experiences, are already bleeding into the commercial space. That’s where there is a growing demand and opportunity, particularly with AI.

In fact, more than 8 in 10 CFOs at large companies are either using AI or considering adopting it.

“In the B2B space, it’s often about orchestrating payments between buyers and suppliers. AI can process all the data, files and invoices, and determine not only what is the best way to pay a supplier but also what is the best payment type,” said Todd King, Vice President of B2B Solutions, TSYS. “AI is going to handle all of that and be a game changer.”

Weighing risk vs. reward

AI-embedded B2B payments are projected to reach $16 trillion in transaction value by 2030, driven by seamless, automated financial processes.

North America has carved a sizable position in the market, partly due to the need to improve inefficiencies with administrative tasks as well as opportunities to stymie late payments — half of all United States B2B invoices are overdue. Overdue invoices are converted to cash 20 days late on average.

Embedding payments in enterprise resource planning (ERP) systems and procurement platforms can help remove blockers tied to manual reconciliation, leading to greater efficiency and speed with managing working capital and ensuring suppliers are paid on time.

The Asia-Pacific region is heavily leveraging the technology, and is expected to have the fastest-growing market for B2B embedded finance and payments.

B2B payment solutions in India use AI to automate processes, enhance security and embed financial services into business workflows. These solutions are often built on top of the Unified Payments Interface infrastructure, a real-time digital payment infrastructure the National Payments Corporation of India developed. Digital adoption and a mobile-first ecosystem are among the factors driving this movement.

A growing focus in the Asia-Pacific region is on B2B use cases, leveraging AI to embed financial services and create seamless, automated and faster payment solutions with more accuracy. Some examples are:

• AI-powered fraud detection to monitor transactions and identify suspicious activity.
• AI agents making transactions on behalf of users in a conversational interface for e-commerce platforms.
• AI for Buy Now, Pay Later in B2B marketplaces.

On the other hand, there are challenges with implementing and using embedded payments.

B2B cross-border payments can deal with inconsistent regulations and legacy systems. Upgrading outdated payment systems often involves expensive upfront costs.

These obstacles may weigh heavily on businesses as they balance revenue with growth and modernizing their financial systems.

More than 43% of CFOs say AI integration to manage accounts payable functions is their top pain point.

How financial leaders weigh these factors could have a significant effect on their business, employees and customers.

Supplier and demand

The success of AI-embedded solutions does not squarely fall on the shoulders of banks and fintechs. There is a reliance on suppliers.

Nearly 73% of businesses have not automated supplier payments.

Manual processes mean there is a human factor to consider. Suppliers could be resistant to adopt new technology because of cost concerns, technological complexity and trust.

Implementing AI-driven payment systems may not have a clear return on investment for suppliers, particularly those with small and medium-size businesses.

Plus, suppliers with legacy ERP systems using outdated architectures may not be compatible with API-driven AI solutions. Ensuring compatibility could require custom development, potentially increasing costs up to 60%.

Since suppliers often prioritize certainty and control ahead of payment processes, this can make them wary of handing control to AI-driven, automated systems.

For the trust factor, some suppliers still prefer traditional payment methods like paper checks. The inability to change could outweigh potential benefits.

What’s next for embedded payments?

B2B AI-embedded payments are evolving to provide greater intelligence, autonomous operations and integration into more everyday platforms.

The next phase appears to be led by AI agents. This technology could enhance fraud prevention, manage end-to-end workflows, and offer greater adoption and use of virtual cards.

“When talking about ecosystems and simplifying experiences, embedded finance, embedded banking and embedded payments seem to be everywhere,” said Duncan.