Underwriter Interview Questions

I start by reviewing the submission information for completeness. Missing or inconsistent data is itself a signal. Then I assess the risk against our appetite: does this class of business, geography, and limit structure fall within what we write? If it does, I work through the exposure: what are the key drivers of loss for this type of risk, what is the historical loss experience, and how does this account compare to our book? I use our rating models as a starting point but I treat them as tools, not conclusions. For anything materially different from our standard risk profile I will ask additional questions or request a survey. I then price based on expected loss, loading for expenses and profit margin, and benchmarking against the market rate. If the broker has submitted a target premium that differs significantly from my assessment, I will either negotiate or decline with a clear explanation.

I treat underwriting discipline and book growth as compatible when the pricing is right and the risk quality meets appetite. The conflict arises when brokers push for a premium below what the risk warrants, and the commercial pressure is to accept anyway to maintain the relationship or hit volume targets. My approach is to be transparent about my pricing rationale, explain clearly why a given risk is priced as it is, and look for a middle ground through risk improvement or coverage adjustment where that is possible. What I will not do is write a risk at a rate that I know will not cover expected loss over time. Short-term relationship damage from a firm decline is recoverable. Underwriting a poor book is not. I also track how declined risks perform so I have data to support my decisions.

I start from my own loss cost estimate and work upward, not from the market rate downward. If the market is pricing below what I believe the risk warrants, I need to understand why before I follow suit: are competitors seeing something I am not, or are they under-pricing for market share? I will use market intelligence and loss data to test my assumptions. If my analysis still shows the market rate is inadequate, I will offer terms at my minimum acceptable rate and accept that some business will go elsewhere. Chasing market price in a soft market is how underwriters build books that haemorrhage at the next major loss event. I would rather write less business at adequate rates than more business that is mispriced.

I use both internal and external data. Internally, I look at loss ratios by class, territory, and account size to understand where our book is performing and where it is deteriorating. Externally, I use industry loss data, third-party risk scoring tools, and sector reports to benchmark my pricing and spot emerging risks. For larger commercial accounts I also use exposure data: property values, revenue figures, headcount, and operational details that feed into my rating model. I treat models as structured guidance rather than answers, particularly for complex or novel risks where the data is sparse. The analytical tools help me be consistent across a high volume of decisions, but the judgment on edge cases still has to come from underwriting experience.

A major broker submitted a large property risk in a flood-prone area with a loss history that showed three significant events in five years. The broker argued the client had invested in flood mitigation and the premium should reflect the improvements. I reviewed the survey report and found that the mitigation measures reduced but did not eliminate the exposure, and that the post-mitigation risk was still priced below what our models required. The broker pushed hard, citing the relationship and the size of the premium. I offered terms at a rate that reflected the residual exposure with a clause requiring annual survey updates. The broker placed it elsewhere. That account suffered a further flood loss eighteen months later. The discipline to decline was the right call.

I wrote a professional indemnity account for a technology consultancy at a rate I believed was adequate based on their stated revenue and claims history. A year into the policy they had a significant claim that revealed the actual scope of their work was considerably broader than the submission had described. The claim was covered, but the rate was not appropriate for the actual risk. From that experience I strengthened my approach to professional indemnity submissions: I now require a sample of recent contracts and client descriptions rather than relying on the broad activity classification on the submission form. Better questions at the point of underwriting prevent expensive surprises at the point of claims.

I received a submission for a mid-size construction liability risk with a loss run that was missing two years of data. The broker explained the client had changed brokers during that period and the records were difficult to obtain. Rather than waiting indefinitely or declining outright, I asked for the client's own incident reports for the missing period and a senior management reference on their safety culture. Those two sources gave me enough directional information to assess the risk with a higher confidence load built into the pricing to account for the data gap. I wrote the account at a rate that reflected the uncertainty, and when the missing information arrived later it confirmed my assessment was appropriately conservative.

Burning cost is the ratio of incurred losses to earned premium over a historical period, adjusted for trend and development. It gives you a pure loss cost before expenses and profit loading. I use it primarily for accounts with credible loss history, typically three to five years of data with enough premium volume to make the ratios statistically meaningful. I adjust for trend by indexing historical losses to current cost levels, for development by using loss development factors to project incurred losses to ultimate, and for exposure changes such as revenue growth or operational shifts. Where the history is sparse or the account is too small to be statistically credible, I rely more heavily on manual rating and market benchmarks.

Deductibles serve two purposes: they reduce moral hazard by keeping the insured financially involved in small losses, and they reduce premium for risks where the insured has a high frequency of attritional losses they can absorb. I recommend a deductible level that is meaningful relative to the insured's financial position but not so high that a routine loss creates a cash flow problem. For limits, I start with the insured's maximum probable loss in a realistic adverse scenario, cross-reference with any contractual requirements they face, and then assess whether the premium for that limit is proportionate to the actual probability of reaching it. I use reinsurance or co-insurance to manage my net exposure where the gross limit is large.

I treat emerging risks as requiring more frequent and more humble reassessment than established classes. For cyber, the loss environment changes faster than historical data can reflect, so I weight recent large-loss events and industry threat intelligence more heavily than multi-year averages. I also review policy wording carefully for silent cyber exposure in liability and property lines. For climate risk, I use catastrophe modelling outputs as a starting point but overlay my own judgment on how modelled loss projections translate to specific geographies in my book. Both risk types require accepting that models carry more uncertainty than usual, which should translate into higher confidence loads in pricing.