The phrase 'AI in credit' has become a marketing catch-all that covers everything from simple rule engines to large language models drafting underwriting summaries. For CFOs and credit professionals trying to evaluate tools and manage regulatory risk, the noise is unhelpful. What matters is a clear-eyed understanding of where AI genuinely assists human credit decision-making, and where using it without robust human oversight creates legal, regulatory, and financial exposure.
Where AI adds genuine value in credit workflows
Document summarisation is the clearest win. A credit file for a complex applicant, such as a company with multiple directors, a multi-year trade history, several bureau reports, PPSR searches, and ASIC records, can run to dozens of pages. An AI model can synthesise the key risk indicators from that file into a structured summary in seconds, saving a credit officer 30–60 minutes of reading time per application.
Risk flag comparison is another high-value application. When a new Equifax report arrives, AI can automatically compare it against the previous report and highlight what has changed: a new payment default registered, a court judgment added, a directorship at another company that has entered administration. Identifying change, rather than asking the credit officer to manually compare two 20-page PDFs, is a task AI handles reliably.
Draft underwriting commentary is a third application: given the bureau data, payment history, and application documents, an AI model can draft a first-pass review note that the credit officer edits, corrects, and signs off. The human produces a better output faster, but the human is still producing the output.
Where AI must not replace human judgment
Credit limit approval is not an AI decision; it is a human decision supported by AI. The distinction matters enormously under Australian consumer and commercial credit law. ASIC's guidance on algorithmic lending is clear: automated decisions that significantly affect a person's financial position require robust explainability, human oversight mechanisms, and clear accountability for the outcome. A credit manager who cannot explain why a limit was set cannot hide behind 'the AI decided'.
Paid bureau searches must also remain human-triggered. AI tools should be able to recommend that a PPSR search or Equifax refresh is warranted, but the actual decision to spend money on a bureau query, and to pull new data about a third party, requires a human to confirm. This is not just a cost governance point; it is a consent and privacy law point. Running bureau checks without a valid, specific consent authority is a privacy breach, and AI automation cannot substitute for proper consent tracking.
Risk scoring is a related danger zone. An AI model can generate a risk score or a creditworthiness estimate. But if that score is used as the primary basis for a credit decision, without a human reviewing the underlying data, you have created an algorithmic lending system that carries substantial regulatory risk. The score is input to a human decision, not the decision itself.
The responsible AI framework for credit teams
A practical framework has three components. First, AI output is always labelled as draft or advisory; it is visible to the credit officer, but it cannot trigger a decision without human confirmation. The platform records that the AI output was viewed, when, and that a human reviewed it before the decision was logged.
Second, AI cannot initiate actions with external consequences; it cannot submit a bureau query, trigger a DocuSign envelope, or update a credit limit. These actions require a human to initiate them, even if the AI has prepared all the supporting information.
Third, the AI's reasoning is preserved in the evidence pack alongside the human decision. This is critical for auditability: if a decision is later challenged, the record shows what the AI suggested, what the credit officer reviewed, and what the final human decision was.
Key takeaway
AI in trade credit is most valuable as a productivity multiplier for experienced credit professionals, not as a replacement for their judgment. The credit teams that get the most from AI tools are those that use them to handle the mechanical parts of credit analysis (summarising, comparing, flagging) while preserving human ownership of the consequential parts (approving, declining, setting terms). Getting that balance right is both a product design choice and a regulatory necessity.
