AI Critical to Making Business Payments Faster and Safer

“Real-time payments processing is becoming a reality in B2B,” Manish Jaiswal, chief product and technology officer at Corcentric, told PYMNTS.

But as payments get faster, enterprises large and small will need to confront inefficiencies in accounts receivables and payables processes.

Most companies, no matter the business vertical, have issues processing payments — where the challenges are tied to data quality or actual execution of the transaction itself, he said.

Fraud remains an ever-present concern, he added, especially as data breaches and social engineering scams become more sophisticated, targeting an enterprise’s employees into unwittingly sending payments to fraudsters’ accounts.

“People can be the most vulnerable link in the ‘security chain’ of payment processing,” Jaiswal remarked. One of the most important functions of payments — making sure that the person you’re paying is who they say they are — is becoming harder to satisfy than ever.

Merchants and financial institutions can address those challenges by embracing artificial intelligence (AI) to streamline operations and beef up anti-fraud efforts. AI, in combination with machine learning, can analyze large data sets in real time, giving firms the information they need to safely execute payments or stop them.

As Jaiswal said, “there are several areas where AI can and will help make real-time processing a reality.”

Drilling down into the ways in which advanced technologies can improve back-office functions, Jaiswal pointed to AP activities and noted that manual processes are still “prominently used” at many firms.

Improving the Back-End Flows

“There are still a majority of companies that get paper invoices” from suppliers and vendors, he said. Companies receiving those paper invoices typically have a third-party provider or in-house staff that key in the information from those invoices — and human error is always a risk. AI has evolved to the point where the technology can be used to read paper-based or scanned invoices; examine the data against “master data” regarding supplier details, pricing and other details; and “push” approval into the system to help get payments out the door.

“On the back end, if you improve these processes, this improves your cycle time, end to end,” he told PYMNTS. “Your payment execution could be flawless if you do all this work the right way.”

On the AR side of the equation, he said that AI can help improve cash flow predictions. Companies can have millions of dollars in receivables outstanding for weeks and months, and they never quite know when they’re going to get paid.

“With the help of AI, you can analyze the behavior of a given [B2B] customer and predict cash flow,” he said, and even the invoices and payments that may ultimately be disputed.

Real-time processing can have benefits beyond B2B payments themselves, noted Jaiswal. As banks compete against FinTechs, they can speed up and improve their underwriting and extend credit to enterprise (and individual) customers.

Looking ahead, he said that big data and machine learning are transforming entire business verticals, the retail industry among them.

“We’re seeing a transformation where retailers are bringing all the data together across supply chain management and inventory management and point-of-sale systems,” he said.

The positive ripple effects are accruing as these companies optimize inventory flow and reduce stock-outs. Retailers can even collect and analyze external data tied to the weather to anticipate whether and when supply chain flows may be disrupted. There’s also the opportunity to improve “reverse logistics” and anticipate if customers might return items based on prior transaction histories and end-user customer behaviors.

“This drives efficiencies across the board,” Jaiswal said, “and we’ll see AI become very prominent in the years ahead.”