Claims automation: auto-decision vs assisted-decision

Three ways to see it

1. 1

   Auto-decisioning makes sense in a narrow band of claims. Small monetary value, low fraud risk, clear policy match, complete documentation. A PKR 5,000 motor windscreen replacement with a workshop estimate, photo, and police report, on a policy with full coverage, can be auto-decided. A PKR 5,000,000 fire claim on a warehouse, even with documentation, cannot. The auto-decision band is defined by money on the table, complexity of policy interpretation, and fraud signal density.

2. 2

   Way one to design the boundary: write three policy classes. Class A claims (auto-decidable): under PKR 50,000, documentation complete, no fraud flags, customer in good standing. Class B (assisted): everything else under PKR 500,000, AI prepares the case file, human approves. Class C (human-led, AI-supports): above PKR 500,000 or any fraud flag or any policy interpretation question. Publish the thresholds; customers calm when they know the rules.

3. 3

   Way two: customer experience matters at decision moments. Even an auto-decided approval should feel like a courtesy: an Urdu SMS naming the customer, the policy, the claim, and the payment date. An auto-decided decline must be impossible. No system should auto-decline a Pakistani insurance claim in 2026; a human reviews and explains every decline. The asymmetry costs more on the approval side and saves trust on the decline side.

The why-tree

Try this with Claude

AI-edge prompt to try: 'You are a claims fairness reviewer at a Pakistani non-life insurer. Walk me through the design of an auto-decision threshold policy for motor claims after a flood event. Include consumer disclosure language in Urdu and English.' Use as a working draft.