Finance
Actuaries
Low ExposureMedian wage $130,000
Read full reading on Actuaries →
AI Exposure · 2026 Reading
Actuaries vs Quantitative Analysts AI Exposure
Two maths-heavy finance titles. Both score low on AI-exposure because the work is model-building and credentialed; the split is which industry the modelling is anchored in.
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The AI-exposure read
Actuaries vs Quantitative Analysts: which is more exposed to AI?
Quantitative Analysts carries the steeper AI-exposure profile: 22/100 versus Actuaries at 16/100. Actuaries carry more weight on insurance and pensions modelling, regulatory reserving, and the credentialed work that requires professional examination. Quantitative analysts carry more weight on capital-markets modelling, systematic strategy research, and the maths-heavy analytics behind trading and risk.
Both retain low AI-exposure because of credentialing and accountability; AI tooling is augmenting both sides without substituting the credentialed core. The fundamentals overlap. Both build statistical models and inform high-stakes decisions. So the gap between them is structural, not a difference of skill set. On pay, Actuaries sit at $130,000 median in the United States; Quantitative Analysts at $81,100. In both cases the durable move is the same: shift toward the judgment, accountability, and system-design work that current AI tooling cannot absorb.
Pay gap
$48,900
Actuaries earn more (+60%)
Exposure gap
6 pts
Quantitative Analysts more exposed
Lower-risk pick
Actuaries
Lower automation exposure
Overlap and Divergence
Where Actuaries and Quantitative Analysts meet, and where they part
Shared traits show where lateral moves between the roles are most plausible and where the same durability strategies apply to both. Divergence points are the structural differences that change the migration paths, the durable skill sets, and the next-six-months moves.
What's the same
Shared structural traits
- Both build statistical models and inform high-stakes decisions.
- Both are structurally insulated by mathematical depth and regulatory accountability.
- Both can migrate into senior risk, modelling, or investment leadership.
What's different
Key divergence points
- Actuaries carry more weight on insurance and pensions modelling, regulatory reserving, and the credentialed work that requires professional examination.
- Quantitative analysts carry more weight on capital-markets modelling, systematic strategy research, and the maths-heavy analytics behind trading and risk.
- Both retain low AI-exposure because of credentialing and accountability; AI tooling is augmenting both sides without substituting the credentialed core.
AI Exposure Spectrum
Where each role sits on the compression curve
The bar below maps the three risk zones AI Career Index uses. The marker for each role shows roughly where its routine-production layer falls under current AI tooling.
Actuaries
Quantitative Analysts
High exposure
Moderate
Low exposure
40 to 60% of routine tasks
20 to 40%
Under 20%
Inside the Work
What Actuaries and Quantitative Analysts actually do
The top task statements each role carries in the O*NET occupation database. The verbatim text comes from the US Department of Labor's structured occupational data, so the gap between these two lists is the structural gap between the two jobs as the labour market formally records them.
A typical week as
Actuaries
A typical week for actuaries mixes model-building, scenario analysis, and the conversations with stakeholders who act on the numbers: budget owners, investment committees, or audit partners. Tooling like Akur8, Earnix, ChatGPT now sits in the day-to-day workflow. The low-exposure reading reflects the role's reliance on judgment, licensure, accountability, or physical presence that current AI tooling cannot substitute.
A typical week as
Quantitative Analysts
A typical week for quantitative analysts mixes model-building, scenario analysis, and the conversations with stakeholders who act on the numbers: budget owners, investment committees, or audit partners. Tooling like ChatGPT, Cursor, Claude Code now sits in the day-to-day workflow. The low-exposure reading reflects the role's reliance on judgment, licensure, accountability, or physical presence that current AI tooling cannot substitute.
Finance
Actuaries
- Ascertain premium rates required and cash reserves and liabilities necessary to ensure payment of future benefits.
- Collaborate with programmers, underwriters, accounts, claims experts, and senior management to help companies develop plans for new lines of business or improvements to existing business.
- Analyze statistical information to estimate mortality, accident, sickness, disability, and retirement rates.
- Design, review, and help administer insurance, annuity and pension plans, determining financial soundness and calculating premiums.
- Determine, or help determine, company policy, and explain complex technical matters to company executives, government officials, shareholders, policyholders, or the public.
O*NET-SOC code · 15-2011.00
Banking
Quantitative Analysts
- Apply mathematical or statistical techniques to address practical issues in finance, such as derivative valuation, securities trading, risk management, or financial market regulation.
- Research or develop analytical tools to address issues such as portfolio construction or optimization, performance measurement, attribution, profit and loss measurement, or pricing models.
- Interpret results of financial analysis procedures.
- Develop core analytical capabilities or model libraries, using advanced statistical, quantitative, or econometric techniques.
- Define or recommend model specifications or data collection methods.
O*NET-SOC code · 13-2099.01
Day-to-Day Work Split
How Actuaries and Quantitative Analysts actually spend the week
Roughly how each role splits its week across the three layers AI Career Index tracks. Routine production is the layer AI tooling compresses fastest. The bigger that slice, the more exposed the role.
Actuaries
20%
45%
35%
Quantitative Analysts
20%
45%
35%
Routine production · What AI absorbs first
Judgement and decisions · Mostly durable
Relational and accountability · AI cannot reach
Role Profiles
Actuaries and Quantitative Analysts side by side
The high-level positioning for each role: category, AI exposure band, and median wage. Use this as the orientation point before reading the structural divergence below.
Also Known As
Alternate titles and US footprint
The alternate titles each role goes by in job listings, plus the size of each role's US labour-market footprint. Useful when the title on a job description does not match either role exactly: the alternate-title list shows which canonical role the listing actually belongs to.
Actuaries
Job listings also call this
Pricing ActuaryReserving ActuaryPension ActuaryCasualty Actuary
US employment
26,670workers
Quantitative Analysts
Job listings also call this
Quantitative Risk AnalystQuantitative Model AnalystQuantitative Equity AnalystQuantitative Research AnalystQuantitative Strategy AnalystFinancial Quantitative Analyst
US employment
132,130workers
Employment figures are from the US Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025 release. BLS reports employment at the SOC-code level: roles sharing a SOC code share an employment count by definition.
The Verdict
Which one should you pick?
Most readers come here for an answer, not just a comparison. Here is the structural read on which role wins on durability, and which one wins on what each is best at.
For durability
Pick Actuaries
Sits further from the routine-production layer AI tooling compresses fastest. The structural exposure is lower, the wage path is more defensible.
For shared skills
Lateral moves are real
Both build statistical models and inform high-stakes decisions.
If you stay in Quantitative Analysts
Migrate up the stack
Quantitative Analysts face more exposure but retain durable migration paths into senior, accountability-bearing seats. The next 6 months of intentional moves matters more than years of tenure.
At-a-Glance Comparison
Actuaries vs Quantitative Analysts on the structural drivers
The dimensions that determine how the two roles diverge under AI: exposure band, US median wage, top durable skill, and the most relevant structural attributes. Each row is sourced from the role's full reading.
At-a-glance comparison
–
Actuaries
Quantitative Analysts
AI exposure score
16 /100
22 /100
Risk band (2026)
Low Exposure
Low Exposure
Real-world AI adoption
5.4%
22%
Routine tasks AI-substitutable
under 20 percent
under 20 percent
Median annual wage (US)
$130,000
$81,100
Entry-level wage (10th pct)
$78,570
$48,460
Senior wage (75th pct)
$170,650
$110,410
Total US employment
26,670
132,130
Top durable skill
Complex deal structuring and negotiation
Credit and risk underwriting judgment on non-standard cases
O*NET-SOC code
15-2011.00
13-2099.01
Reviewed by AI Career Index Research · Methodology v3 · 2026 ·Read the methodology →
AI tool overlap
The AI tools actually doing the work in each role
Where the same tool already appears in both workflows, the substitution path is shared. Where they diverge, the compression vectors do too.
Both roles use
- ChatGPTReserve-memo drafting, regulatory-filing language, and methodology explanation.
Only Actuaries
- Akur8AI-driven insurance pricing and rate-modelling.
- EarnixAI-powered insurance-pricing optimisation.
- Python with CopilotAI-assisted reserving model and stochastic simulation code.
- Milliman AriusAI-augmented loss-reserving and predictive modelling.
- FIS ProphetActuarial modelling platform with AI-driven calculation acceleration.
Only Quantitative Analysts
- CursorAI-native IDE for Python, R, and KDB code.
- Claude CodeAgentic backtesting code, risk-model construction, and trading-algorithm work.
- Numerai SignalsAI-driven equity-signal platform for quant strategies.
- QuantConnectAI-augmented algorithmic-trading research and backtest platform.
- GitHub CopilotInline completion for Python, C++, and KDB quant code.
FAQ
Common questions
Go Deeper
Want the full structural reading?
Each individual role page goes deeper than the comparison view: automation risk, AI tools currently affecting the role, the structural skill map, the 6-month action plan, and the full occupation data block. Read both readings to make a fully informed call between Actuaries and Quantitative Analysts.
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RC
Rafael Costa
Head of Operations · Lisbon, Portugal
"I work in a field that AI is squeezing hard. The report did not pretend otherwise. What it did was show me which part of my role is safe and how to move more of my work into it over the next six months. That honesty is what I needed."
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Hannah Kowalski
Creative Director · Warsaw, Poland
"I had my whole team of twelve take it. The group results flagged two roles that were quietly falling behind and at risk of losing ground to competitors. We fixed both before it showed up in their numbers. At forty-nine dollars a person, it is the best-value spend I have made all year."
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Michael Anderson
Managing Partner · Chicago, USA
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