An insurer has a 40,000-claim backlog and wants AI to clear it. How do you break the problem down?
Think first
Segment the backlog. Not every claim needs the same treatment, and some should never be automated.
Strong answer
First understand the backlog: claim types, value distribution, age, and why each is stuck (missing documents, awaiting assessor, fraud review, customer response). Segment it, because a strategy that works for low-value, document-complete motor claims is wrong for disputed injury claims. Typically a large share are blocked on missing information, so the first AI slice is document intake and completeness checking: extract fields, flag what is missing, draft the customer request for an adjuster to approve. Second slice: summarise complete claims for assessors so each decision is faster. Keep decisions (approve, deny, settle amount) with humans, especially where regulation requires it. Metrics: claims cleared per adjuster-day, cycle time, rework rate, and complaint rate. Run a shadow mode on a few hundred historical claims to measure extraction accuracy before touching live work.
Weak-answer indicators
- Proposes auto-approving claims
- Treats the backlog as one homogeneous problem
- No plan for measuring extraction accuracy
Likely follow-up questions
- How would you detect if the AI is introducing bias in which claims get fast-tracked?
- The regulator asks how a claim was handled. What can you show them?