Skip to content

Business Management · Skills and knowledge for working as an actuary in financial services

Technical Skills and Knowledge Required for Actuaries

Updated 11 October 2026 · Fact-checked

Actuaries need technical skills to measure and manage financial risk. The core set is modelling, statistics, risk assessment, data analysis and financial economics. In CB3 you must name these skills, say how each is used in a business setting, and link them to the problem in the case.

Understand Technical Skills and Knowledge Required

An actuary's job is to put numbers on uncertain future events and help a business act on them. Technical skills are the tools that make this possible. Without them, advice is guesswork.

There are five core areas. Modelling means building a simplified, numerical representation of a real process, such as claims, mortality or investment returns, so you can test what might happen. Statistics lets you fit models to data, estimate parameters and judge how reliable results are. Risk assessment means identifying, measuring and ranking risks by how likely they are and how severe they would be. Data analysis covers collecting, cleaning, checking and interpreting data. Financial economics covers the time value of money, pricing of assets, and the link between risk and return.

These skills work together. Data feeds a model. Statistics sets the model's assumptions. Financial economics values the cash flows the model produces. Risk assessment decides which outcomes matter. A weak link, such as poor data, weakens the whole answer.

Technical skill alone is not enough. A model is a simplification, so you must know its limits and state your assumptions. You must also be able to explain results to people who are not actuaries. That is why this topic sits next to communication and professionalism in the syllabus.

In practice, you also need working knowledge of the business: products, regulation and accounting. Software skills, such as spreadsheets and R, are the usual way these techniques are applied. Keep your knowledge current, because methods and data sources change.

How to solve Technical Skills and Knowledge Required questions

Use this method for any question asking which technical skills are needed, or how they apply to a situation.

  1. 1Read the scenario and identify the business problem, for example pricing a product, setting reserves or assessing capital.
  2. 2List the technical skills relevant to it: modelling, statistics, risk assessment, data analysis, financial economics.
  3. 3For each skill, say what it is used for in this exact scenario. Do not give a generic definition only.
  4. 4Link the skills together: where the data comes from, how it feeds the model, and how results are valued.
  5. 5State the limits: data quality, model simplification, and assumptions that may be wrong.
  6. 6Add the non-technical side briefly: communicating results and acting professionally.
  7. 7Close with a clear recommendation or conclusion that answers the question asked.

Quickest way: Five skills, one use each

When to use it: Use this for MCQs and short written parts when time is tight.

  1. Recall the five skills: modelling, statistics, risk, data, financial economics.
  2. Pick the two or three the question points to.
  3. Write one scenario-specific use for each.
  4. Add one limitation, usually data quality or model assumptions.
  5. Stop once you have enough points for the marks available.

Common mistakes in Technical Skills and Knowledge Required

  • Listing skills with no link to the scenario.

    Students memorise a list and write it out without reading the case.

    Fix: For every skill, write 'in this case it is used to...' with a specific task.

  • Treating modelling and statistics as the same skill.

    Both involve numbers and fitting, so they blur together.

    Fix: Say modelling builds the structure of the process, while statistics estimates parameters and tests how reliable they are.

  • Ignoring data quality.

    Students focus on the clever technique and assume data is correct.

    Fix: Always mention checking, cleaning and validating data, and say results depend on it.

  • Claiming a model gives the true answer.

    Numerical output looks precise.

    Fix: State that a model is a simplification, name key assumptions, and mention sensitivity testing.

  • Leaving out financial economics.

    It sounds like a specialist area, so students think it applies only to investment roles.

    Fix: Remember that valuing any future cash flow needs discounting and a view on risk and return.

  • Writing only technical skills when asked about working as an actuary.

    The topic title says 'technical', so students stop there.

    Fix: Add a short note on communication and professional judgement, as technical results must be explained and used responsibly.

Worked examples

Example 1

An insurer plans to launch a new health product with little past claims data. Describe the technical skills the actuary would use. (6 marks)

Show the solution
  1. Identify the problem: pricing a product with limited data, so uncertainty is high.
  2. Data analysis: gather any available claims data, such as data from similar products or industry sources, and check its quality and relevance.
  3. Statistics: estimate claim frequency and severity, and measure how uncertain those estimates are given the small sample.
  4. Modelling: build a model of premiums, claims and expenses over time, and test different assumptions.
  5. Risk assessment: identify the main risks, such as claims higher than expected, and test scenarios.
  6. Financial economics: value future cash flows, allowing for the time value of money and a return that reflects risk.
  7. Limits: results depend on assumptions, so the actuary should state them and explain the uncertainty to management.

Answer: The actuary uses data analysis, statistics, modelling, risk assessment and financial economics together. Each is applied to pricing, and the limited data means assumptions and uncertainty must be stated clearly.

Example 2

Which one of the following is the best description of the role of statistics in an actuary's work? (A) Building a simplified structure of a business process (B) Estimating parameters from data and judging their reliability (C) Discounting future cash flows at a risk-adjusted rate (D) Ranking risks by likelihood and severity

Show the solution
  1. Option A describes modelling, not statistics.
  2. Option C describes financial economics, which values cash flows.
  3. Option D describes risk assessment.
  4. Option B describes fitting to data and measuring reliability, which is the role of statistics.

Answer: B

Exam tips

  • Always tie each skill to the scenario. Generic lists earn few marks in case-study questions.
  • Mention data quality and model limitations whenever a question involves modelling or data.
  • For MCQs, match the description to the skill by asking what the action does: build, estimate, value, rank or clean.
  • Keep points short and numbered so a marker can find each skill quickly.
  • Add one line on communicating results, as the exam treats technical and non-technical skills as linked.

Practice questions from Skills and knowledge for working as an actuary in financial services

Technical Skills and Knowledge Required in other exams

The same ground in other exams, if you are preparing for more than one or want another angle on it.

Technical Skills and Knowledge Required: frequently asked questions

What technical skills do actuaries need?

The core skills are modelling, statistics, risk assessment, data analysis and financial economics. Actuaries also need business and product knowledge, and practical software skills such as spreadsheets and R.

How do I develop actuarial technical skills?

Practise on real-style problems, use software to build and test models, and work with messy data. Study the Core Principles subjects in order and apply each idea to a business scenario. Keep learning through continuing professional development.

Is this topic tested with calculations?

Usually not. It is tested through multiple-choice questions and written answers where you describe skills and apply them to a case. Clear, scenario-based explanation matters more than formulas.

Do technical skills matter more than communication?

Both matter. Technical work has little value if you cannot explain it to decision makers. Exam answers should show technical skills first and then how results are communicated.