How to Build an AI Agent That Optimizes Supplier Payments

Most companies leave money on the table with every invoice they pay. The math is simple but the execution is hard.

Supplier payment terms usually look like "2/10 net 30." Pay within 10 days, get 2% off. Otherwise pay the full amount in 30 days. That 2% sounds small. It's not. Annualized, it's a 36% return on capital. If your cost of borrowing is 8%, taking that discount is free money.

Yet only 15% of invoices get paid within the discount window. Even companies with 96% on-time payment rates miss almost all their early payment discounts.

Why? Because knowing when to pay requires knowing what you're paying for, how much cash you have, what else is due, and whether the discount math actually works for this specific invoice. That's a lot of context. Humans don't scale.

This is exactly the kind of problem agents are good at. Not chat support. Not fraud alerts. Actual decision-making that compounds into real money.

Here's how to build one.

The Core Loop

The agent needs to do four things:

1. Understand every transaction flowing through your accounts

2. Match invoices to suppliers and extract terms

3. Model your cash position forward

4. Decide: pay now (capture discount) or pay later (preserve liquidity)

Steps 2-4 are straightforward engineering. Step 1 is where most projects die.

Why Transaction Understanding is the Hard Part

Your ERP says you paid "WIRE 04823 ACH AMZN MKTPLCE". Is that inventory? Software? Marketing spend? The same supplier might show up five different ways depending on which account paid them and how the bank formatted it.

An agent can't optimize payment timing if it doesn't know what payments are for. You need clean, categorized, normalized transaction data. Merchant identified. Category assigned. Recurrence patterns detected.

This is what Ntropy's API does. You send raw transaction data, you get back structured enrichment: merchant name, logo, website, category (both general and accounting-specific), and recurrence information if the payment is periodic.

The enrichment runs on 100M+ entities and uses LLMs for edge cases. You can process up to 4,000 transactions synchronously or batch up to 25,000 asynchronously.

Here's what a request looks like:

from ntropy_sdk import SDK

sdk = SDK(api_key="your-api-key")

transactions = [
    {
        "id": "tx_001",
        "description": "WIRE 04823 ACH AMZN MKTPLCE",
        "amount": -4250.00,
        "date": "2026-02-01",
        "entry_type": "outgoing",
        "account_holder_id": "acct_main"
    }
]

enriched = sdk.transactions.create_batch(transactions)

The response tells you this is Amazon, it's categorized as inventory or marketplace fees (depending on your history), and whether it's a recurring payment. Now your agent has something to work with.

Modeling Cash Position

Once transactions are enriched, you can build a forward-looking cash model. The agent needs to know:

Current position: Cash on hand across all accounts.

Committed outflows: Invoices due, payroll, debt service. The recurrence detection from Ntropy helps here. If you pay Datadog $12,000 every month on the 15th, the agent should know that without being told.

Expected inflows: Receivables with estimated collection dates.

Discount opportunities: Invoices where early payment yields meaningful return.

The model doesn't need to be perfect. It needs to be good enough to answer: "If I pay this invoice today, will I have problems in 14 days?"

The Decision Logic

Here's where it gets interesting. The agent evaluates each payable against a simple framework:

For each invoice with early payment terms:

    discount_apr = (discount_pct / (1 - discount_pct)) * (365 / (net_days - discount_days))

    if discount_apr > cost_of_capital:
        if cash_forecast[discount_deadline] > safety_buffer:
            → Pay early, capture discount
        else:
            → Flag for review (good discount but tight cash)
    else:
        → Pay at net terms (discount not worth it)

A 2/10 net 30 term has a 36.7% APR. If your cost of capital is 10%, you should almost always take it. A 1/10 net 60 term has a 7.3% APR. Probably not worth accelerating unless you're swimming in cash.

The agent runs this logic continuously. When an invoice hits the optimal payment date, it triggers the payment or queues it for approval.

What You Actually Need to Build This

Data pipeline: Bank feeds or ERP export flowing into your system. Most companies already have this.

Ntropy integration: API calls to enrich transactions as they arrive. A few hundred lines of code.

Cash model: A simple projection engine. Can be a spreadsheet at first, then graduate to something real.

Decision engine: The logic above, plus rules for approval thresholds and exceptions.

Payment execution: Integration with your bank or AP system to actually move money.

The whole thing can be built in a two weeks by a small team. The hard part isn't the code. It's getting clean data. That's why starting with enrichment matters.

ROI Summary

Let's make it concrete. Assuming 40% of invoices offer early payment terms averaging 2%, and automation improves capture rates by 30-40 percentage points:

A Fortune 500 company with $2B in supplier spend leaving $12M on the table every year. That's not a rounding error. That's a business unit.

Implementation cost: Engineering time plus Ntropy API costs (usage-based, pennies per transaction). Even at enterprise scale with millions of transactions, enrichment costs are a fraction of the savings.

Payback period: Weeks, not months.

And that's just direct discount capture. It doesn't include working capital optimization from timing payments precisely, reduced payment errors, or the operational leverage from having transaction data you can actually use.

The Bigger Picture

Here's a number worth sitting with: $77 trillion.

That's how much of the $80 trillion commercial payments market still runs on checks, ACH, and wire transfers. Not because those methods are good, but because the data infrastructure to do anything smarter doesn't exist for most companies.

AP automation has been around for decades. What's different now is that agents can handle the judgment calls, not just the mechanical processing.

Should we take this discount given our cash position next week? Is this vendor important enough to prioritize? Does this payment pattern look anomalous? Should this payment route through card for the rebate or ACH to preserve the relationship?

These questions require understanding context. Context requires clean data. Clean data requires enrichment.

That's the stack. Enrichment at the bottom, agent reasoning at the top, real money saved in the middle.

The companies that figure this out won't just have more efficient AP departments. They'll have financial operations that run themselves, with humans handling exceptions instead of routine decisions. They'll be positioned to capture a share of that $77 trillion as it modernizes.

Why Networks Should Care

If you're a Card Network,, this is where it gets interesting.

Of that $77 trillion in commercial payments stuck on legacy rails, almost none flows through card networks. Checks, ACH, wire. Zero interchange. The B2B card opportunity has been talked about for a decade but adoption remains stubbornly low. Why? Friction. Suppliers don't want to pay interchange. Buyers don't have the infrastructure to push virtual cards at scale. The decision of which rail to use for each payment is too complex to make manually.

Agents change this equation.

A payment timing agent doesn't just decide when to pay. It can decide how to pay. Route this invoice through a virtual card because the 1.5% rebate beats the supplier's 1% surcharge. Pay that supplier via ACH because they're strategic and card fees would damage the relationship. Use wire for this cross-border payment because speed matters more than cost.

These are judgment calls that used to require a human. An agent with enriched transaction data can make them thousands of times a day.

The math for card networks: if intelligent agents shift even 5% of that $77 trillion from legacy rails to commercial cards, that's $3.8 trillion in new volume. At an average interchange of 2%, that's $76 billion in annual interchange revenue up for grabs. Not all of it goes to networks, but the number is big enough that building the infrastructure to enable these agents becomes an obvious strategic priority.

So how does a network actually make this happen?

Networks have the pieces. They have an open banking connectivity and transaction data. Track BPS handles B2B payment orchestration. Receivables Manager makes it easier for suppliers to accept virtual cards without the friction. Commercial Connect API-s let  corporations plug into all of this programmatically.

What's missing is the intelligence layer that ties it together.

Here's the play: embed transaction enrichment directly into the commercial payments stack. When a corporation connects via Commercial Connect, they don't just get payment rails. They get enriched transaction data, cash flow visibility powered by open banking, and an agent-ready infrastructure that can make routing decisions automatically.

The network becomes the platform for autonomous AP. Not just pipes for moving money, but the intelligence layer that decides how money should move.

Offer this as a value-add to issuing banks. "Your commercial card program now includes intelligent payment routing that maximizes card utilization while respecting supplier relationships." Banks get stickier corporate clients. Corporates get better economics. Card Network  gets volume.

The alternative is waiting for corporations to build this themselves, using whatever enrichment provider and payment rails they choose. Some will. Most won't, because it's complex and outside their core business. The network that makes it turnkey, wins.

Why does owning the data layer translate to owning the volume? Three reasons.

First, defaults matter. When a customer builds their payment agent on Card Network’s infrastructure, the path of least resistance is Card Network  rails. The agent knows which suppliers accept virtual cards (because Receivables Manager tracks acceptance). It knows the rebate economics (because the network sets them). It can route to card whenever the math works without the corporate having to configure anything. 

Second, data compounds. Every payment routed through the network generates data: which suppliers accepted, what fees they charged, how quickly they processed. Feed that back into the enrichment layer and the agent gets smarter about card routing over time. An agent built on generic enrichment doesn't have this feedback loop. The network-integrated agent learns that Supplier X always accepts cards with no surcharge, so it routes to card by default. That knowledge is proprietary.

Third, bundling economics. Card Networks  can price enrichment at cost (or below) if it drives card volume. A standalone enrichment provider has to make money on enrichment itself. For a corporate choosing where to build, the bundled offering wins on total cost of ownership. The enrichment becomes a wedge for the payment rails. 

This is why the data infrastructure question isn't academic. Whoever provides the intelligence layer gets to set the defaults, accumulate the feedback loops, and bundle the economics. The volume follows the data.

Why Commercial Banks Should Care

Banks have a different problem. They already have the corporate relationships. They hold the accounts, process the payments, and provide the credit facilities. What they don't have is stickiness.

Corporate treasury is a commodity. Cash management, payments, FX. Every major bank offers roughly the same thing. Corporates split their banking across three or four providers and treat them as interchangeable utilities. Margins compress. Relationships feel transactional.

Payment timing agents change the value proposition.

A bank that offers intelligent AP isn't just providing rails. It's providing leverage on working capital. The corporate doesn't just get a place to park cash. They get an extra $2.8M in annual savings from discount capture. They get cash flow visibility that actually works. They get an agent that manages supplier payments so their treasury team can focus on strategy instead of invoice queues.

That's not a commodity. That's a reason to consolidate banking relationships.

The economics work in the bank's favor too. Better cash forecasting means corporates can hold operating cash in interest-bearing accounts longer instead of keeping excess buffers in checking. More deposits, more duration. The bank can offer better rates because the corporate isn't hoarding liquidity out of uncertainty.

Cross-sell opens up. Payment timing agents surface credit opportunities naturally. "You're missing this discount because of a cash timing gap. Here's a 7-day facility at 6% to bridge it and capture the 36% APR discount." That's not a cold pitch. That's the agent identifying a financing need the corporate didn't know they had.

The risk of not doing this is disintermediation. If a Card Network or a fintech treasury platform owns the intelligence layer, the bank becomes a dumb pipe. Payments route through them but the relationship lives elsewhere. The corporate logs into someone else's dashboard to manage their cash. The bank processes ACH files and wonders why margins keep shrinking.

The bank's advantage is data they already have. Transaction history across accounts. Payment patterns. Receivables and payables flows. Most banks don't enrich this data in a way that's useful for agents. It sits in legacy systems, unstructured, unusable. Fix that and you have something the fintechs have to acquire. Leave it broken and you're waiting for someone else to build the intelligence layer on top of you.

To explore Ntropy's transaction enrichment API, check out ntropy.com or dive into the documentation.