How AI is changing the way businesses make credit decisions

There's a scene that plays out in finance teams across the construction, manufacturing, and wholesale sectors every single day. A new trade account application comes in. The credit manager opens a browser tab. Then another. Then another. They pull a credit report. They cross-check Companies House. They email for references. They wait. They chase. They scroll through a spreadsheet. Eventually, hours or days later, they make a decision based on fragments of data, much of it months out of date.

This process has barely changed in 20 years. AI is about to change it fundamentally.

The current state of play

According to Gartner, 59% of finance departments are now using AI in some capacity, but the most common applications are knowledge management and accounts payable automation. Credit decisioning, one of the highest-stakes functions in B2B trade, remains largely manual.

This isn't because the technology doesn't exist. It's because the credit function has historically been treated as a risk containment exercise rather than a strategic capability. When the goal is simply to avoid bad debt, the incentive to invest in better tooling is limited. The spreadsheet works well enough. The credit report does its job. The process is slow, but nobody's counting the cost of that slowness.

That's starting to change, and the catalyst isn't technology for its own sake. It's the growing recognition that credit decisions don't just manage risk. They shape growth.

Three shifts AI makes possible

The impact of AI on B2B trade credit isn't about replacing humans with algorithms. It's about transforming what the credit function is capable of. Three shifts stand out.

From periodic reviews to continuous intelligence

Today, most suppliers assess a customer's creditworthiness once, when they apply for a trade account. After that, the account might get reviewed annually, or only when something goes wrong. Between those reviews, the business could be thriving or deteriorating, they could be in the market for new supplies, and the supplier has no way of knowing.

AI changes this by making continuous monitoring economically viable. Instead of a credit team manually reviewing hundreds of accounts on a rolling schedule, machine learning models can track payment behaviour, financial signals, and operational patterns across the entire portfolio simultaneously. When something meaningful changes, a customer's payment times are drifting, their revenue is climbing, a director has been replaced, the system surfaces it. When nothing has changed, it stays quiet.

This isn't theoretical. KPMG's 2025 research on AI in finance found that leading adopters are using AI for exactly this kind of anomaly detection and pattern recognition, and 57% of them report that ROI is exceeding expectations.

The practical result for a credit team: instead of starting each morning wondering which accounts need attention, they start each morning knowing.

From hindsight to foresight

Traditional credit tools are backwards-looking by design. A Companies House filing tells you what a business looked like at the end of its last financial year. A credit score reflects historical data filtered through a model built on past defaults. The entire system is engineered to answer the question: "based on what happened before, is this business likely to pay?"

AI introduces a different question: "based on what's happening right now, where is this business heading?"

This is a meaningful distinction. A company with a mediocre credit score but six months of steadily improving payment behaviour is a fundamentally different prospect from one with the same score and worsening trends. Static data can't tell you that. Machine learning models tracking real-time banking data, payment patterns, and operational signals can.

Forrester's research on AI agents in financial services found that monthly risk reporting can reach 90% automation when AI is properly deployed, transforming what was previously a week-long manual process into same-day delivery. Applied to trade credit, this means a credit team that can see not just where a customer has been, but where they're going, and adjust terms, limits, and approvals accordingly.

From gatekeeper to growth engine

This is the shift that matters most, and the one the industry has been slowest to recognise.

When credit teams have continuous intelligence and forward-looking insight, they don't just make fewer bad decisions. They make more good ones. They can proactively increase limits for customers who've earned them. They can extend better terms to buyers whose trajectory warrants it. They can spot the new applicant whose behavioural signals suggest they'll become a top account within 12 months.

McKinsey's research supports this at the macro level: B2B organisations using AI for sales and commercial decisions are seeing 13 to 15% revenue growth. But the more telling finding is that only 6% of companies qualify as "high performers" where AI contributes meaningfully to profit, not because the technology fails, but because most organisations are still using AI for efficiency rather than growth.

The credit function is a perfect example. Using AI to pull reports faster is efficiency. Using AI to identify which customers to extend more to, which relationships to deepen, and where the next wave of growth is coming from. That's transformation.

What this looks like in practice

Strip away the theory and the daily experience of a credit team changes in three concrete ways.

The morning briefing. Instead of a list of pending applications and an inbox full of reference requests, the credit manager sees a prioritised dashboard. Three new applications, each with a complete profile already assembled, financials, payment behaviour, operational signals, AI-generated risk and opportunity assessment. Two existing accounts flagged for review: one showing early warning signs, the other recommended for a limit increase based on six months of improving performance.

The decision. Instead of spending two hours gathering data to make one credit decision, the credit manager spends five minutes reviewing a recommendation. The AI has already analysed the applicant's banking data, payment history across suppliers, financial trajectory, and market context. It presents a suggested credit limit and terms, with a clear explanation of why. The manager applies their commercial judgement, context the AI doesn't have, like a conversation with the sales team about the buyer's upcoming project pipeline, and approves. Total time: minutes, not days.

The portfolio. Instead of reviewing accounts reactively, when an invoice goes unpaid or a sales rep raises a concern, the credit team manages their portfolio proactively. The AI continuously monitors every account and surfaces the signals that matter: customers trending upward who should be rewarded with better terms, customers trending downward who need attention before they become a problem, and new opportunities that the team would never have spotted manually.

The network effect

There's a compounding dimension to AI in trade credit that makes it different from most AI applications in B2B.

Every credit decision generates data. Every invoice paid or missed, every order placed, every trade interaction adds to the intelligence available to the system. As more suppliers and buyers participate, the quality of every individual assessment improves.

Gartner's prediction that by 2028, 90% of B2B buying will be intermediated by AI agents, pushing over $15 trillion in spend through AI-driven exchanges, points to a future where trade decisions are made within intelligent networks rather than bilateral relationships. A buyer's track record with one supplier informs the assessment another can make. Reliability demonstrated across the network compounds into a commercial reputation that travels with the business.

This is where AI stops being a tool for individual credit teams and becomes infrastructure for the trade economy.

The gap between knowing and doing

That gap, between understanding the opportunity and being ready to capture it, is where the next five years of competitive advantage will be won and lost.

For suppliers in construction, manufacturing, and wholesale, the question isn't whether AI will change how credit decisions are made. The research, the economics, and the technology all point in the same direction. The question is whether they'll be the ones using AI to approve faster, extend smarter, and grow their customer base, or the ones watching competitors do it first.

The credit team isn't disappearing. But the credit team armed with AI, seeing every signal, across every account, in real time, will operate on a completely different level. Not a department that prevents losses. A function that drives growth. And the suppliers who build for that future now will be the ones who define how B2B trade works for the next decade.