Map an Insurance Underwriting Workflow and Pick What AI Automation Should Fix First

The company builds AI that automates parts of insurance underwriting. In this round, a business-side interviewer checks whether you are genuinely interested in the customer's workflow and business problems, not only in the technology. Expect a conversation in which you show what you understand about underwriting, reason about where automation helps, and ask good questions. ### Clarifying Questions - Are the customers insurance carriers, managing general agents, or brokers, and who uses the product day to day? - Which lines of business matter most: personal lines, small commercial, or large and specialty commercial risks? - Is the product meant to assist underwriters, or to make some decisions automatically? ### Part 1 — How underwriting works Walk through what an underwriting team does with a new piece of business, from the moment a submission arrives to the decision to quote or decline, as you understand it. Where do you think an underwriter's time goes? ```hint Follow the documents Trace what arrives with a submission, who touches it, and which systems the information must end up in. ``` #### What This Part Should Cover - The main stages from intake to quote or decline, with the hand-offs between broker and underwriter - The inputs an underwriter works from and the decisions they own - A hypothesis about where the time goes, stated as something to confirm ### Part 2 — Where automation should start Which pain points in that workflow would you automate first? What would you deliberately leave to the underwriter, and how would you tell whether the automation is working? ```hint Rank by volume and verifiability Some steps are frequent and easy to check against a source document; others are rare judgment calls with regulatory weight. Let that difference drive the order. ``` #### What This Part Should Cover - A prioritized list of automation opportunities with the reasoning behind the order - A clear boundary between assistance and decisions that stay with people - Success metrics tied to customer outcomes, plus the main risks ### Part 3 — Your questions What questions would you ask the interviewer, or an underwriter, to check your understanding and find the most valuable problem to work on? ```hint Ask what only a practitioner knows Good questions probe a specific step, a specific failure, or how value is measured, not facts a public website already answers. ``` #### What This Part Should Cover - Questions that test your own hypotheses from Parts 1 and 2 - Questions about how customers measure value and come to trust the product ### What a Strong Answer Covers - A credible end-to-end picture of the workflow, honest about the limits of an outsider's view - Pain points tied to business outcomes such as speed to quote, underwriter capacity, consistency and loss experience - Automation priorities justified by volume, verifiability and risk, with people keeping judgment and accountability - Awareness of regulation, fairness, explainability and data privacy in insurance decisions - Curiosity shown through specific questions and through listening ### Follow-up Questions - An underwriter says they do not trust the extracted data and re-check every field. What do you change? - How would you show that automation improves underwriting results, not only speed? - Which underwriting decisions would you never let a model make on its own, and why? - How would your priorities change for personal auto policies compared with a large commercial property risk?