AI for electronic receivables: Ready, set, match!

Artificial intelligence is changing the game for electronic receivables.

In the last two decades, technology has revolutionized commercial banking products. First came online banking, with 24/7 access via secure web portals. No more calling the bank to execute a wire, view account balances or transfer funds. Mobile banking followed, taking convenience to a new level. Today, banking apps are mainstream, with capabilities from fraud alerts to dual controls to  Remote Deposit Capture  (RDC).

Artificial intelligence is poised to be the next disruptor. For commercial banking customers, AI increases automation and efficiency. For financial institutions, it unleashes a number of exciting opportunities with AI products and services.

Banks that harness AI’s product development potential can:

• Add new revenue streams

• Increase efficiency for customers

• Deepen customer relationships

• Maintain a competitive advantage

Machine learning solutions are already in the market. Integrated Receivables (IR), for example, is rapidly gaining traction with middle market and large corporate customers.

Forty percent of businesses expect to implement an IR solution by 2021.¹ Seventy percent of banks view Integrated Receivables as a high priority.¹

AI capabilities power payment reassociation

IR solves a common challenge for treasury customers who are inundated with electronic receivables that lack corresponding remittance data. It’s a problem common problem as payment methods go digital but related processes lag behind.

NACHA estimates that more than 60 percent of ACH payments, for example, arrive separately from remittance information. These “stranded” receivables force staff to track down email remittances, then manually enter data. It’s busy work that delays posting, lengthens DSO and negatively impacts cash flow.

AI’s sophisticated machine learning capabilities make an IR solution possible. AI has the structure, computing power and self-learning functionality necessary to analyze vast amounts of unstructured data, then reassociate the payment with its remittance—all without human intervention. 

From an innovation perspective, IR clearly demonstrates the power of AI for product expansion. It also shows the market’s growing comfort with AI, as banks and businesses readily adopt the technology.

IR reduces exceptions and boosts straight through processing

The AI layer of an IR solution follows a simple process:

1. Extract: Machine learning algorithms scan and “read” thousands of remittance documents—like emails—and extract pertinent details, such as vendor name, payment amount, invoice number and date.

2. Match: The AI solution compares this data with the treasury customer’s open file of invoices to create a three-way match: payment, remittance and open invoice.

3. Confirm: The customer makes a one-time confirmation that each match is correct; after that, the IR tool’s self-learning capabilities automatically reassociate all future payments for each vendor account.

AI not only speeds this process—faster than any human’s abilities—it continuously learns each company’s unique receivables scenarios. As a result, the IR application becomes smarter with each remittance it touches.

Treasury management customers with IR dramatically reduce exceptions and improve customer service. IR solutions that leverage AI for their electronic receivables pain points can increase straight through processing rates by up to 95 percent.

IR is just one example of smart product development powered by AI. Most of these early solutions come from collaboration with agile fintechs. These partnerships help banks leverage best-of-breed technology and get to market quickly—without taxing internal resources.

Sources & Further Reading

¹Aite Group. (2018, January). The Corporate Need for Integrated Receivables and Banks Journey Into Integrated Receivables.

This content is accurate at the time of publication and may not be updated.