Incident Response Plan: Spike in Incorrect Payments for CA Users Aged 18–24

Scenario

Since 00:00 today, the share of incorrect payments for users aged 18–24 in CA has risen from a historical 2.0% to 3.1% across approximately 120,000 transactions. You are the on-call lead on a payment-accuracy team with access to:

• Feature logs and model scores
• Recent code/deployment diffs
• A sampling tool to pull raw cases for audit

Finance is escalating and requests a mitigation within 24 hours.

Assume the system uses a real-time ML risk score to route transactions (approve vs send to review/decline). "Incorrect payment" is measured by a near-real-time audit/labeling pipeline.

Task

Describe, in a structured, time-boxed plan, exactly how you would:

1. Triage and quantify impact (owners, metrics, and acceptance criteria for "all clear").
2. Form testable hypotheses and design the minimum experiments/queries to isolate root cause (data slices, holdouts, backfills).
3. Decide on a safe mitigation (e.g., threshold change, feature hotfix, rule fallback), including rollback triggers and predicted side effects (precision/recall trade-offs).
4. Communicate status to executives and partner teams at specific times with concrete artifacts (dashboards/runbooks).
5. Prevent recurrence (observability, guardrails, pre-deploy checks).

Be explicit about risks you will accept vs avoid and how you would measure success by end of day.