21 tasks, each one witnessed by the sources that watched the job — and behind every one, a prompt you can use tonight.
Most days mix number work, meetings, and writing. You start by checking data and models in Excel or IBM SPSS Statistics, updating loss curves or reserve estimates. Expect several hours building tables and charts, then turning those into slides in PowerPoint or reports in Word.
Afternoons often have cross-team meetings with underwriting, finance, or product to explain your results and decide pricing or reserving actions. You also prepare documentation for regulators or internal review, and sometimes review policy wording to match your risk models.
Learn Microsoft Excel deeply: pivot tables, formulas, and VBA/macros. Excel is the day-to-day tool for pricing, reserving, and producing tables and charts. Learn PowerPoint and Word to explain findings to non-technical people and regulators.
Next, get familiar with statistical packages like IBM SPSS Statistics or Power BI for visual dashboards. If you aim for modeling or automation, learn a programming language such as C++. Microsoft Visio helps for process diagrams. Employers usually list these systems in job ads.
Actuaries use AI for pattern finding, data cleaning, or building alternative risk models, but you must treat AI outputs as starting points. Always validate AI results against established statistical models, documented assumptions, and known data behavior before using them for pricing or reserves.
Keep full traceability: save inputs, model versions, and validation tests in Word or Excel outputs for audits. Regulators expect documented methods, so don’t use AI as a black box—explain how it changed your numbers.
You need strong probability and statistics, plus practical Excel and basic programming skills (C++ or similar). The job is math-heavy but also needs clear written and verbal explanation — turning technical numbers into simple slides or memos for underwriters and managers.
Attention to detail and patience with documentation matter a lot: regulators and internal audits require precise files, assumptions, and step-by-step calculations. If you dislike writing or explaining numbers, you’ll struggle despite strong math.
Actuaries focus on long-term financial impacts of risk: calculating reserves, pricing policies, and ensuring compliance with professional standards. They use actuarial models and regulatory frameworks and produce formal reserves and pricing documents.
Data scientists often focus on exploratory modeling, predictive performance, or product features without the same regulatory documentation. Underwriters decide who to insure and on what terms using rules and judgement; actuaries set pricing and reserve levels that guide those underwriting decisions.
According to the U.S. Bureau of Labor Statistics (BLS), about 26,670 people worked as actuaries. The median annual wage was $130,000; the lowest tenth earned $78,570 and the top tenth earned $215,100. These are national figures and vary by city, sector, and experience.
Entry pay at small firms can start lower; large insurers, consulting firms, and specialized roles (like pension or reinsurance) often pay above the median. Certification level (ASA, FSA) and years of experience drive most increases.