Harvard · Resume

Insurance & Risk · 2026

Harvard Resume for Actuaries

From actuarial analyst to FSA — how P&C, life, and health recruiters read an exam-progress résumé in 8 seconds.

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How do I write a Actuaries resume in the Harvard format?

Actuarial hiring is exam-gated and modeling-driven before anything else. Recruiters at insurers (Prudential, MetLife, Travelers), reinsurers (Munich Re, Swiss Re), and consultancies (Milliman, WTW, Mercer, Oliver Wyman) scan three things first: how many SOA or CAS exams you've passed (and your VEE credits), which side of the business you work — life, P&C, health, or pension — and the technical depth behind your pricing, reserving, or capital work. The Harvard one-page format puts your exam count and a quantified modeling result in the top third, exactly where a hiring manager decides whether to read on.

What recruiters look for

  • Exam progress spelled out: exams passed by name (P, FM, IFM/SRM, STAM/FAM, MAS-I/MAS-II, Exam 5/6) plus ASA/ACAS/FSA/FCAS status and all VEE credits
  • Track named: life, P&C, health, or pension/retirement — recruiters filter on it before reading bullets
  • Modeling tools: SAS, R, Python, SQL, plus actuarial software (Prophet, GGY AXIS, Moody's RiskIntegrity, Tyche, ResQ, Arius)
  • Technical depth: GLMs, loss reserving (chain-ladder, Bornhuetter-Ferguson), pricing, ALM, stochastic modeling, MCEV/EV
  • Regime fluency: IFRS 17, LDTI (ASU 2018-12), Solvency II, RBC, ORSA, statutory vs GAAP reserving
  • Quantified impact: reserve adequacy, loss-ratio improvement, premium volume priced, capital relief, automation time saved

Required sections, in this order

Exam & credential header

  • Put exam count and credential at the very top: 'ASA · 6/7 FSA exams passed' or 'CAS — Exams 5 & 6 passed, FCAS track' on the contact line or first Skills item
  • List exams by their actual code (P, FM, SRM, STAM, MAS-I) with sitting dates — recruiters and ATS screen on momentum, not a vague 'pursuing FSA'
  • Confirm all VEE credits (Economics, Accounting & Finance, Mathematical Statistics) and your degree, GPA if 3.5+
  • No photo, no DOB, no marital status — these flag a non-US-standard résumé to an actuarial recruiter

Experience bullets — lead with the model and the dollars

  • Open with the actuarial function and its scale, not 'responsible for': premium volume priced, reserves booked, lives or policies covered
  • Name the method and tool: 'built a Tweedie GLM in R for auto pricing' beats 'did pricing analysis'
  • Quantify the outcome a reviewer cares about: loss-ratio points, reserve movement, capital relief, combined-ratio impact, runtime cut
  • Cite the regime when relevant: IFRS 17 CSM, LDTI cohort modeling, Solvency II SCR, statutory reserve under VM-20

Skills & technical section

  • Group cleanly: Languages (R, Python, SAS, SQL, VBA) · Actuarial software (Prophet, AXIS, ResQ, Arius) · Methods (GLM, GBM, chain-ladder, stochastic)
  • List the regimes you've actually worked under (IFRS 17, LDTI, Solvency II, RBC) — assumed knowledge for the role
  • Add data/ML depth if real: gradient boosting, Spark, Power BI, Tableau, cloud (AWS/Azure) — differentiates a modern actuary
  • Skip 'strong analytical skills' and 'detail-oriented' — every actuary claims them; prove them in the bullets instead

Sample in Harvard format

Harvard Resume for Actuaries · 2026 Template & Guide
Harvard format · 1 page

Strong vs weak bullets

Before

Worked on pricing for the personal auto line

After

Re-priced a $180M personal auto book by building a Tweedie GLM in R across 12 rating variables; the new plan cut the loss ratio by 4.2 points and was filed and approved in 14 states with zero objections

Names the book size ($180M), the method and tool (Tweedie GLM in R), the variable count (12), the outcome (4.2 loss-ratio points), and the rollout (14 states, zero objections). A pricing manager infers real GLM ownership in seconds.

Before

Helped set reserves for the workers' comp line

After

Owned quarterly loss reserving for a $420M workers' comp portfolio using chain-ladder and Bornhuetter-Ferguson in ResQ; identified $8M of redundant IBNR that released to earnings after a triangle re-segmentation by injury type

Shows the scale ($420M), the methods (chain-ladder, B-F), the tool (ResQ), and a hard dollar outcome ($8M IBNR released) tied to a real technique (re-segmentation by injury type). Signals reserving judgment, not just running a model.

Before

Did valuation work for the life insurance block

After

Led IFRS 17 transition modeling for a $2.6B traditional life block in Prophet, building the CSM roll-forward and risk-adjustment under the GMM; cut the model runtime 60% by re-engineering the projection grid, hitting every quarterly close on time

Names the regime (IFRS 17 GMM), the scale ($2.6B), the tool (Prophet), the deliverable (CSM roll-forward, risk adjustment), and a process win (60% faster runtime, on-time close). Proves both technical and delivery depth.

Before

Built reports and automated some processes

After

Automated the monthly experience-study pipeline in Python (pandas + SQL), replacing a 3-day manual Excel process with a 20-minute run that feeds mortality and lapse assumptions to the valuation team across 1.4M policies

Quantifies the automation (3 days → 20 minutes), names the stack (Python, pandas, SQL), the actuarial deliverable (experience study feeding mortality/lapse assumptions), and the scale (1.4M policies). Turns 'automation' into measurable analyst leverage.

Mistakes specific to this role

  • Burying exam progress. '4 SOA exams passed, ASA-track' belongs on the contact line — recruiters and ATS filter on exam count first, so don't make them hunt for it.
  • Listing duties instead of dollars and methods. 'Did reserving' is invisible; '$420M WC portfolio, chain-ladder and B-F, $8M IBNR released' is a screen-pass. Always attach scale, method, and outcome.
  • Naming Excel as a headline skill. Excel and VBA are assumed. Reserve the Skills line for R/Python/SAS/SQL, actuarial software (Prophet, AXIS, ResQ), and methods (GLM, stochastic, chain-ladder).
  • Mixing tracks vaguely. A reviewer hiring for P&C pricing needs to see GLMs and loss ratios, not generic 'insurance experience.' Signal your track — life, P&C, health, or pension — in the first third.
  • Going to two pages as a student or analyst. Exam-track and even most credentialed actuaries fit on one page. A two-pager signals you can't prioritize — the opposite of the actuarial brand.

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Frequently asked

Where exactly do I put my exam progress if I'm still sitting for them?
On the contact line under your name, or as the first item in your Skills/Certifications section — never buried in Education. Be precise: 'SOA — Exams P, FM, FAM, ALTAM passed; ASA-track, sitting ILALFMU next sitting' or 'CAS — Exams 5 & 6 passed, ACAS expected 2026.' List VEE credits separately. Exam count is the first screen, so showing named, dated progress beats a vague 'pursuing FSA.'
Do I list life/P&C/health exams differently, and does the track matter on the résumé?
Yes — name your track early because hiring is track-specific. SOA candidates note the FSA specialty fellowship track (e.g., ILA, QFI, retirement, group & health); CAS candidates list Exams 5/6/7/8 toward ACAS/FCAS for P&C. A P&C pricing recruiter wants GLMs and loss ratios; a life valuation team wants IFRS 17 or VM-20. Mirror the track in both your exam list and your bullets.
I'm an actuarial analyst with no fellowship yet — how do I show impact?
Quantify what your models moved or protected: loss-ratio points improved, reserve redundancy released, premium volume priced, capital relief from a reinsurance structure, or runtime and manual hours cut by automation. 'Cut auto loss ratio 4.2 points on a $180M book' and 'released $8M of IBNR' are senior-level signals even from an analyst — they prove your work changed the numbers.
Should I list programming and machine learning, or stick to traditional actuarial skills?
List both if they're real. Modern actuarial teams hire for R, Python, and SQL alongside Prophet or AXIS, and predictive-modeling roles increasingly want GLMs, gradient boosting, and cloud. But depth beats logos: 'built a Tweedie GLM in R that re-priced a $180M book' is worth more than ten tools you opened once. Keep traditional methods (reserving, valuation, ALM) visible too — they're still the core of the role.

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