Design a Risk-Tiered Approval Workflow

Your company ships three kinds of ML-driven changes: (a) tweaks to the weights in a homepage content-ranking formula, (b) a new fraud-scoring model that flags transactions for manual review (never auto-declines), and (c) a new automated underwriting model that can auto-approve or auto-deny small business loans without human review. Currently all three go through the same process: a Slack message to the ML lead, who approves within a day.

1. Explain what is wrong with using one uniform process for all three, citing a concrete failure mode for both "too little rigor" and "too much rigor" as applied to the wrong tier.
2. Design a risk-tiered approval workflow: for each of the three changes, specify what evidence must be reviewed, whether the gate is automated or requires a human, and how many/which humans.
3. Where does the automated retraining pipeline for the fraud model fit into your workflow if it retrains weekly and typically produces small, incremental improvements?