Conversion approval determines when a campaign event becomes a creator payment obligation. Manual review can protect a new integration but becomes arbitrary when reviewers use unwritten standards or miss deadlines. Automatic approval can pay promptly but magnifies a bad event definition. A risk-based policy automates stable cases, samples them for quality, and routes defined exceptions to accountable human review.
Know what approval confirms
Approval should confirm the event met the displayed campaign definition and is payable under current funding and policy. It is not a general judgment that the referred customer will remain forever or that every creator claim was perfect. If retention is part of eligibility, specify the retained milestone and waiting period instead of using reviewer intuition.
List the evidence available at decision time: event fields, customer eligibility flags, attribution record, duplicate checks, refund status, and campaign balance. A reviewer should not use hidden personal characteristics or information unavailable to the creator's appeal process.
Use manual review for calibration
Manual review is appropriate when the conversion schema is new, the product's eligibility logic recently changed, or one event creates a large obligation. Set a target cohort, such as the first twenty valid events, rather than leaving every campaign manual indefinitely. Train reviewers with example cases and a reason taxonomy.
Measure review time and disagreement. If two reviewers classify the same event differently, the policy or data is not ready for automation. Resolve the definition and update examples before increasing volume. Do not hide calibration delays from creators.
Define automation eligibility
Auto-approve only when deterministic controls pass: authenticated request, supported schema, recognized partnership, unique event, eligible customer, event inside the window, required milestone, and sufficient budget. Add partnership history or risk signals only when their role is documented and regularly checked for false positives.
Keep a kill switch and segment-level fallback. A new product release might break only one event version; return that version to review without stopping stable campaigns. Record the policy and code version applied to each event.
Build the review matrix
| Case | Initial path | Service level |
|---|---|---|
| Stable event, established partnership | Auto-approve with audit sample | Immediate state |
| First events from a partnership | Manual calibration | Published review target |
| Schema or product change | Temporary manual review | Until test cohort passes |
| Risk anomaly | Specialist hold | Short, stated deadline |
| Invalid or duplicate event | Deterministic rejection | Immediate reason |
The matrix should be visible at the level needed for creators to understand timing and reasons without exposing sensitive abuse thresholds.
Set reason codes and deadlines
Use distinct reasons for duplicate, existing customer, missing activation, expired window, unsupported geography, insufficient evidence, refund, integration error, and confirmed prohibited traffic. Free-form notes can add context but should not replace consistent categories used for reporting and appeals.
Set a maximum pending period. Alert the owner before it expires and escalate rather than silently extending it. If policy allows automatic approval after the deadline, ensure budget remains reserved. If not, explain what interim action protects the creator from indefinite delay.
Audit automated decisions
Randomly sample approved events and targeted risk segments. Compare with blinded manual review and downstream customer quality. Audit rejected events too; a system can appear precise when only positive decisions are checked. Track reviewer disagreement and whether the audit evidence was available at the original decision time.
Return a segment to manual review when false approvals, false rejections, reversals, or delivery errors cross the precommitted limit. Diagnose the cause before tightening every campaign. Overcorrection can delay valid payments without addressing the broken integration.
Preserve a fair appeal path
A creator should see the event, decision date, amount, reason category, and appeal deadline. Keep customer identity protected. The appeal reviewer should have authority to correct the state and trigger the associated payout rather than merely sending a support response.
Analyze successful appeals by policy version. Repeated corrections indicate a definition, data, or reviewer-training problem. Notify other affected creators and repair comparable decisions when feasible.
Measure the policy
Monitor approval share, pending age, time to decision, reason mix, reversals, appeals, corrected decisions, audit disagreement, retained quality, and payment time. Segment by campaign, event version, and review path. Include review labor in campaign economics.
Automation is ready when a stable definition and integration produce consistent decisions, creators receive timely explanations and payouts, and audits remain inside the accepted error range. It is not ready merely because volume has made manual review inconvenient.
Graduate from manual review with evidence
Manual approval is useful during integration testing, a new creator relationship, or a campaign with expensive or reversible outcomes. It becomes harmful when reviews have no service level, reasons are inconsistent, or creators must chase every payment. Define who reviews, which evidence they can access, how long a decision may remain pending, and what happens when the deadline passes.
Move toward automation by identifying low-risk cases, not by switching the entire campaign at once. Auto-approve events that pass deterministic eligibility checks from established creators, while sampling a percentage for audit. Keep first-time creators, unusual velocity, high-value events, or recently changed integrations in review. Publish the rule categories in campaign terms without revealing sensitive abuse thresholds.
Monitor approval rate, median review time, reversal rate, appeal rate, reviewer disagreement, and creator-specific outliers. Compare automatic decisions with a blinded manual sample. If false approvals or false rejections cross the precommitted boundary, return that segment to review and investigate. Automation should make a stable policy faster; it should not conceal an undefined policy. Every decision still needs a durable event, rule version, reason, and correction path.
Reserve budget for pending decisions
Approval logic and budget accounting must agree. When an eligible event enters manual review, reserve its potential payout so later automatic approvals cannot consume the balance first. Show builders funded, available, reserved, approved, and paid amounts separately. Show creators that the event is pending without implying that money has already transferred.
Define how reservations expire. A review deadline should escalate the event, not quietly release its budget and leave the creator with an unfunded approval. If evidence cannot be obtained, apply the published decision rule and record who resolved the reservation. Test concurrent events near the campaign cap, including a manual event arriving just before several automatic events. The database should prevent total reserved and approved obligations from exceeding available funding. Reconcile reservations after rejection, reversal, campaign pause, and webhook recovery so stale holds do not trap unused builder funds.
Primary sources for Manual Approval vs Auto-Approval: A Risk-Based Policy
The sources for Manual Approval vs Auto-Approval: A Risk-Based Policy were reviewed on July 15, 2026. Check the publisher for revisions and confirm which requirements apply to the campaign, audience, platform, and jurisdiction.