A finance team should not sample every AI-assisted accounts receivable finding the same way. Review all material, ambiguous, novel, or action-changing exceptions, then sample lower-risk routine findings with a documented rule. The sampling plan should test both source traceability and the quality of the human-facing conclusion without allowing a small clean sample to hide high-risk misses.
The NIST AI Risk Management Framework Playbook recommends documented testing, monitoring, human review, and feedback from operational use. The NIST AI RMF resource frames risk management across governing, mapping, measuring, and managing. For receivables analysis, those ideas translate into a review population built from invoice-level decisions rather than a generic accuracy percentage.
Define the review unit
Use one source-linked finding as the sampling unit. Each unit should include the invoice or source-row identifier, finding type, source evidence, model or ruleset version, generated conclusion, recommended next action, human disposition, and review date.
Do not sample only final reports. A report can look coherent while individual findings lack support. The reviewer must be able to move from the conclusion back to the supplied source row and distinguish observed facts from forecast judgment.
Route findings by risk
| Review tier | Example characteristics | Review coverage | | --- | --- | --- | | Full review | Material amount, new exception type, conflicting sources, missing lineage, or action-changing recommendation | Review every finding before use | | Elevated sample | Repeated exception with moderate judgment or recent error history | Sample more heavily and compare by type | | Routine sample | Stable deterministic check with complete source fields | Use a documented rotating sample | | Holdout test | Known cases with locked expected outcomes | Re-run after changes to detect regression |
The tiers should depend on business impact and uncertainty, not only model confidence. A high-confidence output with missing source evidence still belongs in full review.
Prevent sampling blind spots
Stratify by exception type, amount band, customer concentration, age band, data source, and outcome. Rotate the routine sample so the same easy records are not reviewed repeatedly. Keep every override and unsupported finding in a defect log, then increase coverage for the affected stratum until the issue is understood.
Use the AI AR evaluation-set guide to preserve locked test cases. Use the source-citation coverage framework to measure whether material statements point back to evidence.
Record the sampling decision
For each run, store the eligible population, exclusions, selection method, strata, sample count, reviewer, defect definitions, escalation threshold, and disposition. Do not report a causal or statistical claim unless the design supports it. The practical goal is operational control: detect unsupported outputs before they affect a cash decision.
When a reviewer overrides a finding, use the human-override log to preserve the prior value, replacement, reason, and follow-up. When defect patterns change after deployment, route them to the post-deployment monitoring framework.
Decision rule
Review every high-impact or ambiguous finding. Sample routine findings by a documented rotating rule, stratify the population so rare exceptions remain visible, and expand review whenever defects cluster. A pass means the reviewed finding is source-supported and usable; it does not certify every unreviewed finding.
AR Pulse is designed to keep customer findings traceable to supplied source rows and human judgment reviewable. It does not contact customers or act autonomously on receivables. See the finance AI review record for the broader control structure.