Predictive AI in Accounts Receivable: Understanding Machine-Generated Financial Forecasts and Advice

A recent study shows 55% of financial leaders are implementing AI-powered financial forecasting and scenario planning. Moreover, 90% of financial decision-makers now rely on AI for financial decisions. So, it’s no surprise that 79% of CFOs plan to increase their AI investments this year, according to Bain.

As knowledge workers, we’re all getting more comfortable with letting machines handle menial tasks like writing emails, taking notes, and summarizing meetings. But that’s just scratching the surface. Leading finance organizations are moving beyond simple AR automation and using AI to think. Specifically, they’re tapping into the power of predictive AI in accounts receivable, leveraging large language models (LLMs) and machine-generated recommendations to cut hours of analysis and decision-making down to just seconds.

AI can reason from data far faster than humans, and with impressive accuracy. One study found that OpenAI’s Generative AI model, GPT, can solve complex analogy puzzles at roughly human-level accuracy. As the saying goes, “the whole is greater than the sum of its parts.” LLMs and multiple AI agents work together with massive data inputs to figure things out: spotting relationships and drawing conclusions the way we do when we think something through.

When it comes to AI in accounts receivable (AR), this decisioning equates to forecasting cash flow, identifying emerging payment risks before they happen, spotting shifts in buyer behavior trends, and surfacing insights that help finance teams predict and act proactively. This is the mindset finance leaders need to prepare for: AI doesn’t just “do something” but infers, decides, and suggests a solution – sometimes even acting on it.

Ready or not, this is today’s reality. So, let’s explore it all: how AI is evolving, how this plays out in the world of AR, and how to prepare your team with the right data foundation, the right tools, and the right understanding of how AI reasons.

The AI Evolution: From Financial Forecasting to Prescriptive Advice

Until recently, most AI tools ran the numbers and projected where things were headed. Helpful? Yes. But teams still had to figure out why it was happening and how to respond. Over the last few years, AI has rapidly advanced to the point where it does more than forecast. It can interpret what’s happening and why in real-time, connecting the dots, and recommending the next move. It doesn’t just report on trends – it actively guides decisions through prescriptive modeling, statistical probabilities, and emerging patterns. These AI assistants augment human judgement.

Take the Days Sales Outstanding (DSO) performance metric as an example. Traditional AI might flag that DSO is increasing based on historical averages. That’s certainly helpful, but you still need to figure out why. Reasoning bridges this gap by seeing how all the pieces play together:

• Top buyers are scaling back their orders
• Others are switching payment methods to credit cards regardless of your surcharging fees
• Some are slipping beyond their payment terms

When all that contextual intelligence comes into view, the root cause becomes clear. AI concludes you’ve got emerging risk, and it’s time to act. It recommends a proactive outreach campaign. Better still, it pulls that content together for you.

Let Billtrust Autopilot be Your Financial Predictor

Billtrust Autopilot combines Generative AI with Agentic AI to constantly monitor activity across your order-to-cash cycle and guide you with a clear, informed view of your financials. It spots what’s important and suggests what to do next. You don’t have to export a spreadsheet, build a report, or talk to an analyst. The value is measurable and realized almost immediately. That’s because GenAI makes the interaction feel like a natural conversation. You simply ask questions.

Say you ask Autopilot: “Which buyers have recently exceeded 60-day terms and reduced their average order volume over the last 3 months?” Normally, that’s a multi-report, multi-spreadsheet kind of project. The AI can handle this instantly, pulling the right data across invoicing, payments, and buyer profiles and then correlating those variables to deliver a clear, actionable answer. The end result: less manual work and productivity gains of up to 80%.

How does it work? Through a multi-agent AI architecture.

Behind the scenes, Billtrust’s Agentic AI is doing the heavy lifting. This form of accounts receivable automation uses multiple AI agents that collaborate in real-time to reason through your request and surface meaningful insights:

• Planner Agent: Maps out the steps required to answer your question
• SQL Agent: Extracts the necessary AR data from multiple systems
• Code Agent: Analyzes payment patterns and buyer behavior
• Supervisor Agent: Reviews the output and packages it into a clear, human-friendly summary

This is the value of Agentic AI in action – it’s a coordinated system of intelligence. Plus, it has built-in data security and access controls to ensure sensitive information stays protected at every step. These AI agents are good at predicting and prescribing solutions, too. Here’s more on that.

Staying in Front of Collections Management

AI agents can leverage predictive intelligence to maximize on-time payments. AI even identifies when and how to execute procedures that will deliver the best results. Recommendations include what to say, when to say it, and through which channel.

In the future, Billtrust’s Dynamic Credit Lines will offer AI recommendations to reduce credit risk and boost revenue growth. Machine learning models analyze historical payment data patterns to optimize credit limits across all accounts. Learn more

A Quick Review: Agentic and Generative AI

Let’s stop here for a moment. We covered a lot of ground in a short amount of time. Generative AI, Agentic AI… these terms can feel confusing, abstract, or overwhelming. Particularly when most finance organizations are just starting to automate AR processes -- let alone embracing multi-agent systems that reason through complex decisions using AI.

You don’t need to become an AI expert overnight, but you need a baseline understanding of how this all works: how AI reasons, draws connections, and suggests actions. Not just so you can trust what it’s telling you, but so you can apply it confidently within the context of your own business.

If you want a closer look at Agentic AI? Check out this introduction to Agentic AI and what it means for mean for accounts receivable. This article breaks down the evolution of AI, explaining how AI agents differ from basic process automation and why not all agents are the same.

Is AI Getting it Right? How to Trust AI Finance Tools

Let’s talk about a question on the mind of every CFO: How can you trust AI-generated recommendations? It isn’t magic, and it’s not always correct – hence the disclaimers you’ll see when using different platforms. That’s not exactly comforting when you’re trying to predict your financial future.

The truth? Poor data hygiene. When underlying systems are cluttered with outdated configurations, fragmented records, or inconsistent data formats, AI in accounts receivable can confidently generate answers that are plain wrong. This is why data hygiene remains one of the biggest barriers to adoption. Clean, structured, reliable data gives AI a solid foundation to reason from but building that foundation is grueling work.

Some estimates show finance teams spend up to 40% of their time simply gathering, cleaning, and verifying data. Does your team have time for that? Probably not. Are they ready to trust AI if the data isn’t right or the volumes aren’t large enough to support accurate AI-driven decisioning? Also, no.

But it’s not just about data...

Controls to Ensure AI Accuracy

Overall, AI can be trusted when it has the right data and the right controls.

Trust should be earned, not automatic. It’s never advisable to just hand over control to AI--even if you’re fully confident in your data hygiene. Financial leaders and AR professionals should use approval processes and control features before an AI engine is trusted to fully execute on its own recommendations. Aided automation should always precede unaided automation. While everyone likes to talk about AI’s ability to act autonomously, autonomy is a journey in trust building rather than a light switch. This is why it's important to get started early. Yes, like now.

Jumping these Hurdles with Billtrust

Billtrust Autopilot draws from nearly 25 years of anonymized transaction data – your data, other customers’ data, and behavior insights from thousands of buyers – all protected for personal privacy but fully intact for analysis and unparalleled insight. It’s the industry’s largest financial data network: rich, clean, deeply contextualized, and constantly refined across $1 trillion invoice payment transactions annually. Combined with more than two decades of AR domain expertise, this creates a market-unique foundation for predictive analytics to not just report on risk but truly reason through it. The end result: Advice you can count on.

We gatekeep your data, keeping it clean, organized, and continually refreshed so you get the clearest view into your AR performance. Want to dig into granular details? It’s there. Want to benchmark your performance against others in your industry? You can do that, too. This trusted foundation, combined with feature controls, security protections, and deep AR expertise, powers next-level AI reasoning.

“I would say that Billtrust is top tier when it comes to AI. I think the AI functionality from Billtrust is exceptional compared to other organizations or partnerships that we've had in the past, or even just partnerships that we've explored. People say that they have AI functionality. That doesn't necessarily mean it's AI functionality to the standard that we're looking for.”

Becki Hamilton, Sr. Manager Cash Applications, Willscot
Hear her story

Using Predictive AI to Manage Accounts Receivable Proactively

AI is ready to do a lot for your AR team if you’re ready to start experimenting with it. Here are four ways you can realistically use AI’s predictive analytics to the advantage of accounts receivable.

1. Spot Payment Drift Before it Snowballs

Payment issues don’t show up overnight. What happens are little shifts: a customer who’s always paid on time starts creeping a few days later, then a few more. Suddenly you’re 60 days past due and wondering how you missed it.

AI doesn’t miss it.

AI tools like Billtrust Autopilot constantly monitor these data points, watch for subtle changes – Days to Pay (DTP) metrics inching up, partial payments creeping in, payment methods shifting – and flag risk early while there’s still time to act. It can even suggest (and in some cases trigger) next steps automatically. Examples include:

• Sending a personalized reminder with AI drafting it all for you
• Automatically adjusting payment terms
• Prioritizing the account for a collector’s review

Instead of your team hunting for answers, AI keeps the process moving and helps your people stay focused on the conversations that need a human touch.

2. Catch Spending Slowdowns Tied to Payment Risk

When buyers start pulling back on orders and falling behind on payments, it’s a major concern. AI connects the dots, flagging when a customer’s average order size is shrinking, frequency of purchases is slowing, and payment timelines are stretching out.

Tools like Billtrust Autopilot surface these early warning signs while the customer relationship is still stable, giving your team time to step in, connect, and adjust before small shifts turn into bigger problems. And with AI insights guiding the outreach – like suggesting customized payment plans – your team can engage in a way that supports the customer relationship, not just the bottom line.

3. Alert when Customers Disengage

It happens to the best of us: a steady customer suddenly goes quiet. Autopay gets turned off. Invoice emails sit unopened. Buyers stop logging into the billing and payment portal. These aren’t random – they’re usually the first signs that something is shifting behind the scenes.

AI tools like Autopilot can pick up on these subtle signals. Instead of waiting for payments to slip, the tool flags the change early and can recommend actions like a proactive check-in call or a soft-touch outreach before things escalate.

4. Drive Targeted, Proactive Actions

Not every situation calls for the same approach, but your team doesn’t have time to dig into every detail. That’s where AI steps in.

A tool like Autopilot can recommend specific, proactive next steps for each customer. For one, it might suggest a gentle outreach from the account manager. For another, it may recommend adjusting credit terms, payment policies, or engaging in deeper review. For low-risk situations, it may even automate the follow-up entirely. Instead of chasing every overdue balance equally, your team focuses on the situations with the greatest financial impact – maximizing collections while minimizing wasted effort.

These use cases are already happening, and the right data and the right technology platform make them possible.

You Trust AI with Emails. Why Not Your Cash Flow Management?

The teams leaning into AI-powered reasoning aren’t just reacting faster – they’re seeing around corners, spotting risks sooner, and turning insights into action before problems hit their bottom line. The result is smarter decisions, stronger customer relationships, and a finance function that’s proudly out in front.

Wondering what predictive AI could do for your AR operation? Our team is happy to help you shape your financial future. Contact us for a solution demo and free consultation.