At my previous firm we needed to price a new term life product for a 35‑year‑old male demographic.

Develop a premium that reflected mortality risk, expense loadings, and regulatory constraints.

I extracted the relevant mortality rates from the latest SOA table, adjusted for select period, applied a discount rate to calculate present value of future benefits, added expense and profit loadings, and validated the result against the company’s pricing guidelines and state regulations.

The final premium was 0.85% of the sum assured, meeting profitability targets while staying compliant, and was approved for launch within the quarter.

Our claims reserving team needed to assess the variability of future claim payments for a large casualty portfolio.

Implement a stochastic model to quantify reserve risk and report a range of possible outcomes.

I selected a bootstrap approach, resampled residuals from the development factors, generated 10,000 simulated loss triangles, calculated ultimate losses for each, and derived the 75th and 95th percentile reserves. I then presented the results with a risk‑adjusted capital recommendation.

The analysis revealed a 12% reserve variability, leading senior management to increase capital allocation by $3 million and adjust pricing for the next underwriting cycle.

During a quarterly board meeting, the CFO asked why our loss ratio had increased despite stable premiums.

Translate the actuarial explanation of emerging claim trends into business‑focused language.

I prepared a concise slide showing claim frequency trends, used analogies (e.g., “weather patterns”) to illustrate volatility, and linked the data to potential pricing adjustments. I then walked the CFO through the visual, answering questions in plain terms.

The CFO grasped the issue quickly, approved a targeted rate review, and the board commended the clear presentation.

While reviewing a new auto insurance pricing model, I noticed the lapse rate assumptions were based on outdated data.

Assess the impact and propose an updated approach before the model went live.

I ran sensitivity tests comparing the old lapse rates to recent experience, quantified a 3% overpricing risk, and presented findings to the pricing committee with a recommendation to incorporate the latest lapse data and adjust the model parameters.

The committee accepted the changes, preventing potential loss of market share and ensuring compliance with internal pricing standards.

A regional office provided a snapshot of 5,000 policies with age, exposure, and claim cost data for the past year.

Determine whether existing premiums reflect the underlying risk profile.

I segmented the data by age bands, calculated loss ratios for each segment, performed a GLM to model claim severity as a function of age, and compared predicted costs to current premiums. I identified underpriced segments (ages 25‑34) and recommended rate adjustments with supporting profitability forecasts.

The analysis led to a 4% premium increase for the identified segment, improving overall loss ratio by 0.6 points without eroding competitiveness.

Our firm was approached by a corporate client interested in a high‑deductible health plan with wellness incentives.

Assess the product’s expected profitability and risk profile before quoting a price.

I gathered historical claim data for comparable products, adjusted for demographic differences, built a frequency‑severity model using a Tweedie distribution, incorporated cost‑of‑care inflation trends, applied regulatory reserve requirements, and performed scenario analysis on utilization rates. I also evaluated the impact of wellness incentives on claim frequency.

The model projected a 7% profit margin under baseline assumptions, with sensitivity analysis showing profitability remains above 4% even under adverse utilization scenarios, supporting a competitive quote to the client.