As generative AI continues to dominate conversations across financial services, its usefulness in B2B credit is increasingly being questioned by practitioners working closest to risk. While machine learning has transformed areas such as fraud detection and document processing, some argue that the core credit decision itself remains poorly suited to opaque AI models.
For Clément Carrier, CEO and co-founder of embedded invoice financing startup Aria, the issue is simple: B2B credit leaves little room for error. Decisions often involve advancing large sums of capital on thin margins, where a single bad call can erase the gains from dozens of successful transactions. In that context, explainability matters as much as speed.
Clément Carrier, CEO and co-founder at Aria
“For all the hype around generative AI in finance, B2B credit scoring is one of the places where it doesn’t live up to expectations. That’s because B2B credit is a high-stakes game with zero room for error,” he says.
Why B2B credit Is different
Carrier draws a clear distinction between consumer and business credit. In B2C lending, individual transactions tend to be smaller, mistakes are less damaging, and lenders often rely on behavioural signals drawn from non-public data. At scale, probabilistic models can make sense, even if individual outcomes are imperfect.
B2B credit operates under a different set of constraints. Ticket sizes are larger, purchasing events are less frequent, and the data environment is richer and more structured. Financial statements, registries, payment histories and contracts are often available and verifiable, raising the bar for decision quality.
“In B2B, the opposite holds. Ticket sizes are large, purchases are less frequent, and there’s a wealth of structured, publicly or contractually available data such as financial statements, registries, payment histories, and signed contracts. That means you can – and should – demand much higher confidence in your judgments.”
That data richness, he argues, makes heavy reliance on opaque AI models harder to justify. In regulated environments, credit decisions must be defensible not only internally, but also to auditors, regulators and customers. Models that cannot be explained or challenged introduce operational and regulatory risk.
“In our world, you can’t rely on opaque, ‘black box’ AI models that are hard to explain or challenge,” Carrier said.
Simple questions, high consequences
Carrier suggests that B2B credit ultimately comes down to a small number of fundamental questions: has the buyer genuinely committed to pay, and is the buyer financially sound over the relevant time horizon
Answering those questions does not require models trained on noisy behavioural data. Instead, it requires structured analysis of verifiable information and clear reasoning that can withstand scrutiny.
“The reality is that B2B credit decisions don’t need exotic AI because the core questions that need to be answered are deceptively simple,” he said.
This preference for transparency also reflects the regulatory context in which B2B lenders operate. Credit models need to be explainable, consistent and auditable – qualities that traditional analytical approaches tend to provide more reliably than generative systems.
“Traditional, transparent approaches work best on this data, and more importantly, they’re far easier to explain to regulators, auditors and customers,” Carrier added.
Where AI actually adds value
That skepticism does not mean AI has no role to play in B2B credit. Carrier points instead to a supporting role, where machine learning improves efficiency and accuracy around the decision rather than replacing it.
At Aria, AI is used to automate labour-intensive processes such as document intake, Optical Character Recognition, financial data extraction and ratio calculation. These tools reduce manual workload and allow analysts to focus on higher-value judgment calls.
“Where AI really earns its keep is in the ‘micro-tasks’ around the decision, not the decision itself,” Carrier said.
AI is used mainly to support oversight rather than decision-making. Machine learning helps scan portfolios, flag irregular activity and highlight cases that merit closer review, particularly around fraud or emerging financial stress.
Human judgment remains central
Carrier is unconvinced of fully automated underwriting in B2B credit. Instead, he points to tools that help analysts work faster and with better information, while keeping responsibility for credit decisions with people rather than models.
“The future of AI in B2B credit is less about fully autonomous underwriting and more about augmented, human-in-the-loop decision-making,” he said.
His view is not that AI should be avoided, but that it should be applied with restraint. When used to improve operational processes, it can support B2B finance. When applied to core credit judgments, it can reduce clarity rather than improve it.
“The bottom line is: use AI only where it clearly improves the plumbing of B2B finance, and be very cautious about handing it the steering wheel for credit decisions.”
