Credit Management: 5 Requirements to Keep 30%of Revenue from Slipping into Bad Debt

When business is good, it’s great. Orders are flowing, customers are growing, and payments run on autopilot. On the flip side, when it rains, it pours – and the first thing companies do to minimize a cash crunch is delay payments to their suppliers. Some studies have found that as much as 43% of credit-based B2B sales are overdue because of customer financial issues. The result?

Suppliers risk as much as 30% of annual revenue to bad debt.

Today’s economic stresses can change a company’s financial position (and credit health) rapidly. When conditions are volatile, traditional credit management just doesn’t work. Credit managers and accounts receivable leaders need to keep their head on a swivel: continuously monitoring risk in real-time, using payment data to prevent defaults before they hit, and staying on top of their organization’s entire credit portfolio.

It’s a new era of dynamic credit risk management, which AI is perfectly primed for. It’s actually one of the biggest ways financial organizations are using AI. In one recent McKinsey study, 60% of senior credit risk executives said they’re using AI for early credit risk detection, having it analyze massive datasets to spot signs that a borrower may default. Over 40% use it for automated credit decisions, having AI evaluate applications in minutes instead of days and auto-approve low-risk accounts.

60% of finance leaders use AI for early credit risk detection and 40% use it for automated credit decisioning.

AR plays a huge role in credit risk management, but tech falls behind. Accounts receivable is a strong starting point for dynamic credit risk management because it’s often the first place credit risk reveals itself. Long before a missed payment or insolvency, warning signs pop up in customer payment behavior. But nearly half of CFOs say their current AR tech stack isn’t optimized for what dynamic credit risk management requires.

So, what is required? Here’s what dynamic credit risk management looks like in action.

Requirement #1: Early Warning Credit Risk Systems, Based on Behavioral Science

Risk monitoring should be ongoing and based on how customers actually pay. It doesn’t matter if a customer’s credit score looked good on paper a few months ago if today’s payment activity – the most accurate indicator of credit risk – is waving red flags.

AI algorithms help you get ahead of credit risk by understanding customer payment behavior. Let’s say, for example, if a customer has been trending towards late payments, if they’re stretching terms, or if payments become erratic.

Behavioral science isn’t new. Most teams just don’t have the time, AI tools, and real-time data to see the threats developing. And by “most,” we mean almost all. Research shows that only 3% of financial organizations can accurately analyze the payment behaviors of their customers and use that intelligence to mitigate risk.

Alerts for Growth Opportunities Too

But it’s not just about exposure. It’s just as much about opportunity. While certain payment behaviors indicate instability, others indicate healthy cash flow – a great opportunity to safely extend higher credit limits to drive bigger orders and unlock more working capital.

AI is also good at navigating nuanced situations, making sense of the subtleties that can highlight strategic plays. Think about the chronically delinquent customer who always pays late but pays consistently at that later time. They’re not paying on time, but they’re not signaling financial distress. You might consider adjusting their payment terms to reflect reality or factoring their actual payment behavior into your cash flow forecast, so it’s not consistently off.

You don’t need to guess what will happen when your customers are actively showing you. AI is doing that for you, using data-driven strategies to stay ahead of credit risk and seize new opportunities.

Requirement #2: A Multi-Source Approach to Credit Evaluations

It’s rarely the case that one thing tells you everything you need to know. The doctor doesn’t look at just one symptom. The dentist doesn’t look at just one tooth. Yet that’s exactly what traditional credit management can be – an evaluation based on a single snapshot.

Limited data means limited credit insight. Dynamic credit management recognizes that risk is shaped by multiple signals across multiple sources. AI-powered credit management software is shown to improve early risk detection by continuously monitoring across both internal and external sources for credit review. Here’s how this broader approach pinpoints concentrations of exposure.

Internal data sheds insight into:

• How consistently customers pay and whether payments are starting to slow
• Whether customers are stretching payment terms more frequently
• The number and severity of overdue invoices
• Changes in invoice dispute activity and how quickly issues are resolved
• How much available credit a customer is using
• Shifts in purchasing behavior
• How responsive customers are to collections outreach
• Patterns associated with previous write-offs and bad debt

External data illuminates:

• Changes in commercial credit scores and ratings
• Signs of financial stress reported by credit bureaus
• Changes in company financial performance and liquidity
• Industry-specific downturns or market pressures
• Negative news, business disruptions, or leadership changes
• Broader economic conditions that could impact the customer’s ability to pay

The more signals you bring together, the stronger AI’s ability to accurately predict, inform, and help teams make smarter decisions.

Beware: If you’re still cobbling together data from different systems to get answers or insights, you’re doing it wrong. Your ERP, payment data, credit data, and other sources should be seamlessly connected – allowing AI to automate analysis, reduce manual work, and bring the right information to the surface.

Requirement #3: A Portfolio-Wide View into Creditworthiness

For every risk indicator at the 30,000-foot level, there are dozens at individual account level. Where does risk live specifically, and how can you detect it early enough to do something about it? AI is exceptional at spotting patterns across hundreds or thousands of accounts, so you can understand what’s happening across your entire credit portfolio as well as with every individual buyer.

These insights can be used in multiple ways – to make informed decisions that prevent risk as well as drive new growth. Customers who drop out of autopay programs could indicate financial stress and the need to make reviews ASAP. On the other hand, if autopay enrollment is increasing in a certain segment, there’s likely an opportunity to expand credit relationships and do a more targeted push for that industry. This holistic, data-driven approach can increase cash flow by up to 25%, according to Billtrust’s data.

With new integrations into AI tools, finding these answers is as simple as asking a question. Want to know where risk is bubbling, which customers deserve a closer look, or what trends point to growth opportunities? You can simply ask Anthropic’s Claude, Microsoft’s Copilot, or other large language models using plain English prompts. You can dig into broader market intelligence, too. This is made possible by Model Context Protocol (MCP) connectors, and Billtrust is the first to bring them to the AR automation space, so finance teams can easily access intelligent decision-making.

Explore how MCP connectors help automate accounts receivable operations.

But remember, you must know what data the AI is trained on.

Is it trained on millions of real payment transactions, or a small sample of your buyers? Is the data recent or stale? Is it accurate, coming from a wide variety of internal and external sources where reconciliation isn’t a problem? Is it biased in any way?

This last question is huge. AI bias – biased recommendations from AI due to human biases that skew original training data – is something every company needs to understand to build a responsible AI strategy. Over 80% of finance leaders are concerned about AI misuse, and they’re not wrong for feeling that way.

Learn more about trusting AI in accounts receivable.

Requirement #4: Automated Credit Decisioning

Automated credit decisioning is something 40% of executives at top financial organizations are using AI for today. Right now, most credit reviews are complexities riding on the shoulders of humans. Credit approvals and allocations can take anywhere from a couple of days to over a week because there’s only so much AR team members can do by hand.

AI, on the other hand, moves at lightning speed – reviewing credit applications and auto-approving low-risk accounts in seconds. For existing accounts, it can scan a customer portfolio, flag credit risk, guide in making the right credit allocation adjustments, and explain why it suggested those changes. AI continuously analyzes across internal and external data sources, aggregating mountains of data to help credit teams make optimal decisions.

If you’re not familiar with AI in AR, this might sound questionable or outright concerning. We understand the unease. Here’s how you stay in control.

How to Stay in Control of Automated Credit Decisioning

• You decide exactly what information you want applicants to provide and what criteria matter most when evaluating creditworthiness.
• Trade references are automatically collected, kept in one place, and are based on near-real time data.
• You determine the benchmark that dictates whether a low-risk account is considered creditworthy by AI, moving them through quickly.
• Automated routing is built around your existing process, flagging special cases and exceptions for human reviewers.
• Full visibility into where an application stands keeps you and your credit applicants in the loop and out of the weeds of status-update emails and unnecessary follow-up calls.
• Credit allocation decisions are auditable, with clear transparency into why applications were denied and why credit line adjustments are warranted.

See how 84 Lumber is handling 45% more applications with the same team using Billtrust.

Credit Management Requirement #5: AR and Credit Teams Working Together

Some credit teams don’t think about AR’s role – a grave mistake when you look at the risk reductions and revenue gains that come from connecting credit management activities to the AR lifecycle. The two biggest connections involve collections and invoicing.

Your Next Best Step in Credit Management

You don’t have to jump in today, but you should understand how credit management is evolving and what’s now possible with an AI-powered, data-driven approach. There’s so much more to learn with our complete guide to credit risk management. You’ll walk away with even more credit management best practices to consider, buyer’s guide tips, and a macroeconomic snapshot of the forces shaping credit risk and cash flow threats.

Explore the guide here and let us know when you’re ready to bring in a partner to transform the way you manage credit risk and cash flow performance.