A credit policy is the documented set of rules governing how a company extends, limits, monitors, and collects customer credit, covering application requirements, scoring, limits, terms, holds, dunning cadence, and write-off authority.
A credit policy is the documented set of rules that governs how a company extends credit to customers, monitors that credit over time, and collects what is owed. It defines who qualifies for credit, how much they get, on what terms, what happens when they pay late, and when accounts are escalated, written off, or placed with an agency. A credit policy is not a philosophy or a habit. It is a written document, approved by finance leadership, that the credit and collections team can point to when making a call.
The reason it matters comes down to three things: consistency, defensibility, and alignment. Consistency means two analysts looking at the same customer reach the same decision. Defensibility means that in an audit, a legal dispute, or a customer escalation, the company can show that the decision followed a documented standard. Alignment means sales and finance argue about the policy, not about individual deals, which makes the friction productive instead of personal.
A complete credit policy typically contains the following sections:
Most policies also include an exception process, because reality demands one. The key is that exceptions are logged, attributed, and reviewed, not granted invisibly.
The single biggest determinant of whether a credit policy works is whether it treats different customers differently. A strategic account doing 5 million euros a year in revenue cannot receive the same dunning sequence as a small first-time buyer who placed a 500 euro order. Likewise, a high-risk customer in a struggling sector should not get the same payment terms as a blue-chip with a 20-year payment history.
Good credit policies define three to five tiers based on a combination of payment history, financial strength, strategic importance, and external risk signals. Each tier carries its own credit limit logic, payment terms, hold sensitivity, and dunning cadence. Sales knows what they are selling into. Finance knows what they are protecting. Customers experience treatment that fits their relationship, not a one-size-fits-all sequence that frustrates the good payers and barely contains the bad ones.
Most credit policies do not fail because they were written badly. They fail because of one of these patterns:
Each of these failures shares a root cause: the policy is treated as a static artefact rather than a live operating system.
A credit policy should carry a defined review cadence. Annual review is the standard for the document itself: limits, tier thresholds, escalation matrices, and write-off authorities all get revisited against the latest data. Quarterly review should cover exception trends, asking which rules are being overridden, by whom, and why. If 30% of new accounts are landing in an exception tier, the tier definitions are probably wrong.
Major business events (entering a new geography, acquiring a customer base, a downturn in a key sector) should trigger an off-cycle review. The policy is a living document, and the credit team is responsible for keeping it accurate.
AI-native credit operations change credit policy execution in three ways. First, dynamic credit tiers: instead of static tier assignments set at onboarding, agentic systems re-score customers continuously using real-time signals (payment behaviour, dispute frequency, public financial signals, sector trends). A customer can move tiers between order entries.
Second, automated policy enforcement: holds, term changes, and dunning escalations fire based on policy rules without manual intervention. The credit analyst spends time on exceptions and strategic accounts, not on routinely applying rules that the system can apply itself.
Third, exception analytics that feed policy refinement: every override, manual hold release, and out-of-policy decision is logged with attribution. Quarterly the credit team sees patterns: which rules are being bent, by whom, for which customer segments, and what the downstream impact on DSO and bad debt has been. The policy stops being a document that ages in a drawer and becomes a system that learns from its own exceptions.
A credit policy is the documented set of rules that governs how a company extends credit to customers, monitors that credit, and collects what is owed. It typically covers credit application requirements, scoring, limit setting, payment terms, hold triggers, dunning cadence, escalation, dispute handling, and write-off authority.
Three reasons: consistency (two analysts reach the same decision on the same customer), defensibility (in audit or legal contexts, the company can show decisions followed a documented standard), and alignment (sales and finance argue about the policy itself rather than about individual deals, making the friction productive).
Credit application requirements, a scoring and tiering model, credit limit setting rules, payment terms by tier, credit hold triggers, dunning cadence by tier, an escalation matrix, dispute handling, write-off authorization, and agency placement criteria. Most policies also include a documented exception process.
Annual review of the full document is standard, covering limits, tier thresholds, escalation matrices, and write-off authority. Quarterly review of exception trends catches policy drift early. Major business events (new geography, acquisition, sector downturn) should trigger an off-cycle review.
The policy goes stale (benchmarks no longer apply), sales overrides are undocumented, there is no segmentation by customer tier, there is no review cadence, and there is no link between policy KPIs like DSO and bad debt and actual enforcement. Each failure treats the policy as a static artefact rather than a live operating system.
AI-native systems re-score customers continuously using real-time signals so tiers become dynamic, automate enforcement of holds and dunning so analysts focus on exceptions, and log every override with attribution so quarterly reviews can see which rules are being bent and how that affects DSO and bad debt. The policy stops being a static document and becomes a system that learns from its own exceptions.
