How a reinsurance analyst turns throughput into scope
$168,790top of the range in New York · middle $81,370 / yr
AI augments this role
Reinsurance Analysts in the United States earn a median of $81,370 a year. Pay starts near $55,530. Pay reaches $168,790 at the top of the range in New York, the best-paying state for this work among those with at least 500 people in the job.
Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025 (Insurance Underwriters, SOC 13-2053). Last checked 9 September 2026.
Entry level
$55,530
Top of the range · New York
$168,790
Education
Bachelor's degree in Math or Finance
Wages — U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025 (Insurance Underwriters). Top of the range is the highest state-level figure among states with at least 500 people in the job. AI-impact rating is PayCrunch's editorial assessment. Updated September 2026.
🆕 New & Trending AI Tools for Reinsurance AnalystReviewed September 2026
We track new AI-tool launches every week and refresh this list — here’s what’s gaining traction for Reinsurance Analyst work right now.
NumericNEWPaid / see site
AI-driven month-end close, reconciliation, and reporting.
How a Reinsurance Analyst uses it: automate reconciliations and close the books faster
HebbiaNEWEnterprise / see site
AI that reads and analyzes large financial documents and filings.
How a Reinsurance Analyst uses it: pull answers out of contracts, filings, and reports in minutes
NotebookLMNEWFree / $7.99 mo
Google tool that answers questions grounded only in the documents you give it — with citations.
How a Reinsurance Analyst uses it: load your own manuals, policies, or PDFs and ask questions that stay accurate to the source
MindBridgeEnterprise / see site
AI that scans transactions for anomalies, errors, and fraud risk.
How a Reinsurance Analyst uses it: flag risky or unusual entries across the whole ledger, not just a sample
Vic.aiEnterprise / see site
Autonomous accounts-payable and invoice processing.
How a Reinsurance Analyst uses it: let AI code and process invoices with minimal manual entry
RampFree core / paid
Finance platform with AI that automates expenses and spend controls.
How a Reinsurance Analyst uses it: auto-categorize spend and catch policy issues in real time
Power BI Copilot$10+ mo
Microsoft analytics with AI that builds dashboards and explains trends.
How a Reinsurance Analyst uses it: ask questions of financial data and get charts and forecasts back
ChatGPTFree / $20 mo
The most-used AI assistant — writing, analysis, research, and images from a plain-language chat.
How a Reinsurance Analyst uses it: draft emails and documents, summarize long files, and get instant answers to on-the-job questions
ClaudeFree / $20 mo
AI assistant known for careful writing, long-document analysis, and coding.
How a Reinsurance Analyst uses it: analyze big reports or spreadsheets and turn messy notes into clean, finished writing
The submission lands before the New York desk is fully staffed, and a second copy is already with a broker in another time zone. A reinsurance analyst opens the treaty file first. Last year's slip is clipped to this year's numbers. The cedent wants to renew a share of a whole book, and the analyst's job is to say whether the book still looks like the one the desk priced twelve months ago. Later the same morning a facultative referral arrives: one risk, one insured, one placement that will not roll forward on a standing contract. Both files need a written view. Only one of them is a relationship that expects to come back next year.
People outside the market use underwriter and analyst as if they were the same chair. On a working desk they are not the same chair. The analyst builds the loss view, compares the submission with the prior term, and drafts the note an underwriter can defend. The underwriter decides what the desk will offer. A strong analyst makes that decision faster and harder to regret. A weak note, however neat the spreadsheet, sends the file back to the start.
Treaty season on one screen, a single risk on the other
Treaty work is portfolio work. A primary insurer, the cedent, wants another company to take a defined share of many policies at once. The analyst reads the bordereau, or the summary that stands in for it, and looks for what changed: a new class of business, a shift in limits, a loss year that now dominates the history, a geographic mix that no longer matches the story in the cover note. The draft terms matter as much as the loss history. Attachment, limit, how the premium is ceded, and whether a reinstatement is on the page all change the price. The analyst does not have to lecture a room on contract law. The analyst does have to notice when this year's draft is quieter, or harsher, than last year's.
Facultative work is the opposite shape. Someone wants cover for one account that does not fit a treaty, or that the treaty was never meant to hold. The packet may be a factory, a fleet, a hospital, or a construction project with a completion date. The analyst reads the exposure, the loss record on that risk, and the reason the broker says the treaty cannot take it. Then the analyst writes a recommendation the underwriter can accept, trim, or decline. Speed matters, because a facultative broker will move to the next desk. Care matters more, because one badly read submission can become one badly priced certificate.
The week also holds the unglamorous middle. Data arrive late and in the wrong layout. A cedent restates premium after the indication has already gone out. A claim notice on an old year changes the way a renewal should be read. The analyst keeps a record of what was assumed, what was later corrected, and which version the underwriter actually saw. That record is the job as much as the model. When an auditor, a retrocession colleague, or next year's renewal team asks why the price moved, the note has to answer without a scavenger hunt through inboxes.
What leaves your desk with your name on it
A usable work product is short enough to read before a call and specific enough to defend after one. It states the subject, treaty or facultative, the cedent or the insured, the figure you recommend, and the two or three facts that carry the recommendation. It says what you ignored and why. It says what would change your mind: a missing loss run, a limit you have not seen, a class of business the desk has walked away from before. Underwriters trust analysts who mark uncertainty in plain words. They stop trusting analysts who bury a guess inside a smooth paragraph.
Tools vary by firm. Some desks live in vendor models. Some live in workbooks built by the person who had the chair before you. Either way, the analyst is expected to know which cells are inputs and which cells are stories. Peer review is normal. Another analyst, or the underwriter, will challenge the loss pick, the expense assumption, and the comparison year. Your job in that review is to explain, not to win. If the challenge is right, you change the note and you say you changed it. That habit is how people get a larger book.
Client contact grows with trust. Early on, you may only listen on the broker call and fix the note afterward. Later you may walk a cedent through why the indication moved, with the underwriter still owning the offer. Writing for that moment is different from writing for the file. Brokers remember who could explain a change without hiding behind the model. Cedents remember who had read the submission rather than a summary of the summary.
Associate in Reinsurance, and the body that grants it
The credential that names this work is the Associate in Reinsurance designation, usually written ARe. The Institutes grants it. The program is a course of study in how reinsurance is arranged, how treaty placements differ from facultative placements, and how premium and loss are shared between a cedent and a reinsurer. Earning it shows that you finished that study and completed the program's exams. It does not give you authority to bind a risk. Binding authority stays with the underwriter and with the company's own rules. What the designation gives a hiring manager is evidence that you already speak the market's vocabulary and that you were willing to study it on purpose.
People prepare while they work. A common path is an analyst seat, a primary underwriting assistant role, or a broker's analytical team, plus the Institutes' own study material and a colleague who has already finished the program. Some employers pay for the courses because a desk full of people who mix up treaty and facultative is expensive. Some candidates also take the Chartered Property Casualty Underwriter designation from the same organization when they want a wider property and casualty base before they specialize. ARe remains the designation that points straight at reinsurance. Put it on the resume when you have it. If you are midway, say which part you have finished rather than implying the whole credential.
No licence from a state insurance department is the universal ticket into an analyst chair. Company appointments and producer licences matter for people who sell insurance. They are a different track. If a role touches surplus lines, intermediary work, or a state that regulates the firm's activities, the compliance team will say so. Do not invent a licence line on a resume to look more official. The designation, the writing sample, and a book of files you can discuss will do more work than a vague claim about being licensed.
How a reinsurer, a cedent, or a broker hires
Campus hiring still feeds the large reinsurers, primary carriers with a ceded-re desk, and the global brokers. Those programs look for a bachelor's degree in finance, mathematics, economics, or a close field, plus evidence you can write. An internship on an underwriting, claims, or broker analytics team is the cleanest bridge. Experienced hiring looks different. A manager with an open seat wants someone who has already touched submissions, even if the submissions were primary rather than reinsurance. Claims experience is respected when you can connect a paid loss to the way a price should have been built.
The screen is practical. You may be handed a short submission and asked to talk through what you would want to know before anyone quoted it. You may be asked to explain a chart you did not build. You may be asked why a treaty renewal and a facultative placement should not be priced with the same habits. Listen for whether you separate the portfolio from the single risk without being nudged. Listen, too, for whether you can say decline. Desks do not need analysts who love every file. They need analysts who can tell a weak file from a thin file, and who can say which missing page would fix the thin one.
Bring a writing sample that is actually yours. A one-page note on a public company's insurance program, or a memo from an internship with confidential figures removed, beats a generic cover letter about passion for risk. Mention the classes of business you have seen, and do not stretch. If you have only read property treaties, say so. A casualty desk would rather teach you its forms than discover in month three that the resume was hopeful. References from someone who has read your notes are worth more than references from someone who liked your attendance.
From the first model to a book you can name
The early title is analyst or pricing analyst. The work is data, first drafts, and renewal comparisons under close review. The next title is often senior analyst or assistant underwriter. You still may not bind, but the underwriter stops rewriting your notes from scratch. After that, people split. Some become treaty underwriters on a named book. Some stay in facultative and become the person a broker calls for a class of risk. Some move to the broker side and build the submissions they used to tear apart. A smaller group moves toward retrocession, capital, or reserving, where the reinsurance view meets the company's own balance sheet.
What accelerates the path is a book you can describe without a slide. Which renewals you priced, which facultative risks you recommended the desk decline, which assumption you got wrong and how you found out. Managers promote people who can tell that story calmly. They hesitate over people who can only name software. A lateral year in claims or in broking can help if the story afterward is sharper. It does not help if you treat the lateral move as a way to avoid learning price.
Published pay, and the one series name that covers it
The wages in this section are Occupational Employment and Wage Statistics, May 2025, for Insurance Underwriters, a wider title than a reinsurance analyst alone. Entry pay is $55,530. The national median is $81,370. The gap from entry to that median is $25,840. Read the entry figure as a first full-time seat that still lives inside someone else's review. Read the median as pay for someone who already produces indications a desk will send.
The high end of the published range in New York is $168,790. That figure is the top of the range published for New York. New York's median is a different statistic, $97,450. From the national median up to the New York high end, the gap is $87,420. Massachusetts shows the highest median, $106,640, which sits $25,270 above the national median. Connecticut's median is $100,690. Colorado's median is $100,640. Washington's median is $99,400. Five strong medians do not make five identical jobs. A Boston treaty desk and a Denver primary underwriting role can share a wage table and still hire for different files.
Puerto Rico's median, $46,990, is the low end among the published medians. The gap between the Massachusetts median and the Puerto Rico median is $59,650. Geography moves the middle by a sum large enough to matter in a relocation. It still does not turn a state median into New York's high end. When you cite a place, say median or high end out loud. Mixing those labels is how offer talks go fuzzy.
Naming the figure that matches the chair
If the offer sits near $55,530 and the work is a first analyst year, the $25,840 distance to the national median is a story about the next several years, not a demand for the first paycheck. Ask what changes the review: a full renewal season you ran, a facultative book you drafted, the ARe designation finished rather than started. Put those markers in the conversation so the path to $81,370 is concrete. If you already do that work with light supervision, open on the national median and say why your files match it.
Location changes the anchor. A seat in Massachusetts can be compared with $106,640, and you can note that this median is $25,270 above the national figure. A New York seat has two numbers, and they are not substitutes. The median, $97,450, is the middle. The high end of the published range, $168,790, belongs in a talk about a senior pricing lead whose name is on large quotes. Using the high end to bump a first offer makes the rest of your case easier to dismiss. The $87,420 gap from the national median to that high end is a career span.
Bonus and profit-share plans show up on some desks. Ask what the plan pays on, who decides, and whether a bad loss year can take it to nothing. Then return to base pay and the published figure that fits the chair. Entry, national median, a state median, or the New York high end: pick one comparison and stay with it. A reinsurance analyst is paid to keep categories straight. The salary conversation is a good place to show you already do.
The top of Reinsurance Analyst pay — and how to get there with AI
$168,790what Reinsurance Analyst pay reaches in New York
Highest state-level top-of-range annual wage for Insurance Underwriters, among states with at least 500 people in the job. U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025.
And the role it leads to — Financial Risk Specialists — reaches $220,260 in New York.
$55,530entry$81,370middle$168,790top end
What separates a reinsurance analyst at the top of this range from one in the middle is the size of the risk they may bind without asking, and authority is granted to people who can show both a clean decision record and the capacity to handle more.
Reviewing company records to establish how much cover is already in force on a single risk or a group of closely related risks, examining documents for applicant health, financial standing and property condition, applying ratings and endorsements where a risk is substandard, and declining what is excessive is a decision job wrapped in a great deal of retrieval. Correspondence to field representatives and medical personnel, rate quotations, explanations of underwriting policy, all of it is drafting. Analysts who use a model for the drafting and the document summarising, then put the recovered hours into catastrophe accumulation and treaty structure, end up qualified for authority rather than merely busy.
Your playbook, by where you are now
Just startingMeasure yourself before anyone else does
Log every submission you touch in Microsoft Excel with dates received, referred and decided, and the reason for the decision.
Learn the in-force position properly: pull aggregate exposure on a risk group from the policy system before quoting anything on it.
Standardise your correspondence, requests to field representatives, medical follow-ups, rate quotations, into templates so the wording is consistent and defensible.
Have Claude summarise long submission documents into a fact sheet, then confirm every material figure against the original before it influences a decision.
Search LexisNexis on the financial standing questions rather than accepting a broker's characterisation of a counterparty.
What proves it: A decision log showing your volume, cycle time and referral rate over a full year.
Realistic span: the first two years
A few years inOwn the accumulation question
Take catastrophe exposure seriously: build the view of where losses correlate across the book rather than assessing risks one at a time.
Keep the accumulation model in Microsoft Access or the pricing tools your team already runs, and reconcile it to the policy records every month.
Write the reasoning for every substandard rating and endorsement you apply, since a reviewable rationale is what a referral authority is granted against.
Ask for a larger binding limit on a defined class, and bring the throughput and decision-quality record when you ask.
Learn what the reinsurance treaty actually cedes, so authorising cover on a high risk is a structural decision rather than a reflex.
What proves it: An increased binding authority, granted on a written case you made.
Realistic span: years three through six
ExperiencedPrice the programme, not the file
Move to structuring: retention levels, layer pricing, and which risks belong in which treaty rather than which single risk to decline.
Automate the recurring exposure reporting with Power Automate so the monthly pack stops eating a week, and spot-check every figure it produces.
Explain underwriting policy outward, to field representatives and brokers, until the submissions arriving at your desk have already been filtered.
Build the quantitative depth the risk specialist route expects, in modelling and statistics, before you need it.
Watch where this market sits: New York concentrates the work and pays it accordingly, and the financial risk specialist path opens from exactly this ground.
What proves it: A treaty or programme structure carrying your analysis, with results you can point to.
Realistic span: seven years and up
The next 90 days
For the next ninety days, keep a record of every submission you handle: when it arrived, when you decided, whether you had to refer it, and what the decision was. Add the hours you spent writing correspondence rather than assessing risk. Most analysts discover that drafting letters and chasing missing medical or financial information takes more of the week than examining documents for degree of risk. Fix the biggest slice with templates and a drafting assistant, then bring the before-and-after figures to your manager together with a specific request for a wider authority on one class of business. That is a conversation about scope backed by evidence, which is the only kind that moves.
Wage figures: BLS OEWS, May 2025. The playbook is PayCrunch editorial guidance, not a guarantee of pay or placement.
Every figure is the national median from the U.S. Bureau of Labor Statistics (OEWS) shown on that role’s own page.
Never used AI before? Start here (2 minutes).
Attack the data grind first - it's where analysts lose the most time. Cleaning submission data and bordereaux by hand eats days each renewal. Learn Python (the pandas library) with GitHub Copilot writing the code, or use Copilot in Excel, to turn messy cedant files into model-ready data in minutes. That reclaimed time is what you reinvest in analysis and pricing.
Then go deep on the models that define the job. Learn your shop's catastrophe platform - Moody's RMS or Verisk Touchstone - and use Perplexity to research perils and market conditions with citations, and Claude to explain unfamiliar treaty terms in plain English. Keep confidential cedant and treaty data inside approved systems; use general AI on methodology and anonymized examples only.
The one rule, forever: Catastrophe models and pricing decide how much capital backs a treaty, so validate every assumption and never accept vendor defaults blindly - communicate model uncertainty rather than a single false-precise number. Never paste confidential cedant, treaty, or policyholder data into a consumer AI tool; keep it in approved systems. Actuarial and underwriting judgment governs the number that binds capital, not the model output.
The plays — exact steps, exact prompts
Do these in order. Each one is copy-paste ready. You do not need to know anything about AI going in.
1
Run catastrophe models and portfolio roll-ups faster
Why this pays: Catastrophe modeling is the core technical skill in reinsurance and the scarcest. The analyst who runs and interprets the models fluently - and can explain the assumptions - is on the path to senior modeler and the top of the band.
Moody's RMSVerisk TouchstoneAnalyze Re
1
Master your shop's cat platform - Moody's RMS (Intelligent Risk Platform) or Verisk Touchstone - to run exposure, generate EP curves, and roll up portfolio losses, and use Analyze Re for fast portfolio and structure analytics.
2
Use AI to sharpen how you interrogate and explain model output.
Copy-paste this prompt
Act as a catastrophe modeling expert. Explain how to interpret an EP curve and the key metrics (AAL, 1-in-100 and 1-in-250 OEP/AEP, TVaR) for a [US hurricane] portfolio, what assumptions most affect the tail, and the sensitivity tests I should run before trusting the numbers. Then list the questions an underwriter will ask me about these results. General methodology only.
Understand the drivers and run sensitivities - never present a single model number as certain; communicate the uncertainty and the assumptions behind it.
3
Document your assumptions and run sensitivity tests (different event sets, demand surge, secondary uncertainty) so your results are defensible to underwriters and clients.
What you'll haveFluent, well-understood cat modeling with tested assumptions - the scarce technical skill that carries an analyst toward $168,790.
2
Automate submission and bordereaux data prep
Why this pays: Every renewal starts with messy cedant data. Automating the cleaning that used to take days frees you for pricing and analysis and eliminates the errors that corrupt a model - the reliability that gets you the important accounts.
Build reusable Python scripts with GitHub Copilot to clean, map, and validate submission data and bordereaux - standardizing formats, geocoding exposures, and flagging gaps - so every file arrives model-ready.
2
Generate the validation harness once and reuse it every renewal.
Copy-paste this prompt
Write Python (pandas) to validate a property exposure bordereau for cat modeling: check for missing or invalid geocodes, TIV outliers, blank construction/occupancy codes, currency inconsistencies, and duplicate locations, then output an exception report listing each issue and the row. Use synthetic data only.
Automates the checks; a clean run still needs your reasonability review, and real cedant data stays in approved systems - never in a consumer tool.
3
Turn recurring data-prep into a documented, parameterized workflow so each renewal is a rerun, not a rebuild - reproducibility scales you across more accounts.
What you'll haveModel-ready data in minutes with fewer errors - reclaimed time for analysis and the reliability that earns the important accounts.
3
Price treaties with AI-assisted models
Why this pays: Pricing sophistication is where reinsurance pay climbs. An analyst who can build transparent experience- and exposure-rating models and explain them stands out for the pricing roles that pay the most.
Learn a modern pricing platform like hyperexponential (hx Renew), or build experience- and exposure-rating models in Python with Copilot, keeping the logic transparent enough to defend to an underwriter.
2
Use AI to structure the pricing analysis and its defense.
Copy-paste this prompt
Act as a reinsurance pricing analyst. Walk me through pricing an [excess-of-loss property treaty]: how to develop and trend the loss experience, blend experience and exposure rating, load for expenses and cost of capital, and reflect the terms (limit, attachment, reinstatements). List the assumptions I must justify to the underwriter. General methodology only, no client data.
A methodology guide, not a price - the actual pricing runs on real data in approved tools, and every assumption is yours to justify.
3
Benchmark your model price against the technical price and the market, and document why they differ - the explanation is what underwriters and brokers pay for.
What you'll haveTransparent, defensible treaty pricing - the sophistication that positions an analyst for the highest-value reinsurance roles.
4
Extract and compare treaty terms with AI
Why this pays: Contract wordings hide the risk - a reinstatement clause or exclusion can swing the economics. Using AI to read and compare wordings fast makes you the analyst who catches what others miss, protecting the deal and your reputation.
ClaudeChatGPTMicrosoft 365 Copilot (Word)
1
Use Claude to summarize a treaty wording in plain English and extract the key commercial terms - limits, attachment, reinstatements, exclusions, hours clauses - into a structured comparison.
2
Compare this year's wording against last year's to catch changes.
Copy-paste this prompt
Act as a reinsurance contract analyst. Compare these two treaty wordings and produce a clear list of every material difference in terms - coverage, exclusions, reinstatement provisions, definitions, and limits - and explain the economic impact of each change. Flag anything unusual or ambiguous for legal review. [paste anonymized/redacted wordings]
A first-pass review, not a legal opinion - every flagged term must be confirmed against the actual contract and escalated to underwriting or legal. Redact confidential identifiers.
3
Bring the flagged terms and their economic impact to the underwriter - being the analyst who surfaces the hidden clause is how you become trusted with the key deals.
What you'll haveFast, thorough wording reviews that catch costly terms - the diligence that protects the deal and builds an analyst's reputation.
5
Research emerging perils and market conditions
Why this pays: Reinsurance rewards the analyst who understands where risk is heading - cyber, climate, secondary perils. Being current and able to quantify emerging exposure makes you a partner in strategy, the profile that earns the best roles.
PerplexityCyberCubeClaude
1
Use Perplexity to track catastrophe activity, rate movements, capacity, and emerging perils with citations, verifying key data at the primary source.
2
For specialty lines, learn the dedicated models and frame the risk.
Copy-paste this prompt
Act as an emerging-risk analyst in reinsurance. Brief me on how the market currently models and prices [cyber catastrophe] risk: the main models used (such as CyberCube), the key loss drivers and accumulation scenarios, the data limitations, and the questions a reinsurer should ask a cedant. Cite where the market view is still uncertain. General overview only.
Emerging-peril models carry large uncertainty - use AI to orient, verify against the actual model documentation, and be explicit about what the data can't yet tell you.
3
Synthesize a short view on an emerging peril for your team - being the person who understands the next big exposure is how an analyst becomes a strategic partner.
What you'll haveA current, quantified command of emerging risk - the strategic profile that earns an analyst the highest-value reinsurance roles.
6
Build broker and executive-ready analytics
Why this pays: The renewal is won on a clear story about risk and price. An analyst who turns model output into compelling analytics and narrative gets their work in front of clients and leadership - the visibility that drives advancement.
Microsoft Power BIClaudeMicrosoft 365 Copilot (PowerPoint)
1
Build clear exposure and structure analytics in Power BI - loss distributions, program options, price-versus-risk comparisons - so the story is visual and decision-ready.
2
Draft the renewal narrative from your own analysis.
Copy-paste this prompt
You are preparing a reinsurance renewal summary for a cedant's leadership. From these results, write a clear one-page narrative: the risk profile, the recommended program structure and why, the pricing rationale, and the key trade-offs. Lead with the recommendation, plain language. [paste anonymized results]
Draft only - verify every number against your models, keep client data in approved systems, and own the recommendation yourself.
3
Use Copilot in PowerPoint to build the deck, then rewrite the recommendation in your own words - the judgment call is what clients and leadership are buying.
What you'll haveCompelling, decision-ready renewal analytics - the client-facing visibility that turns strong analysis into an analyst's advancement.
Your 12-month sequence to the top of the range
How the plays above stack into a path from median pay toward the $168,790 tier.
Month 1
Automate submission and bordereaux cleaning with Python and Copilot (or Copilot in Excel) to reclaim time, and start learning your cat platform in depth.
Months 2-3
Build fluency running and interpreting cat models with tested assumptions, and use AI to speed treaty-wording reviews - escalating flagged terms to underwriting.
Months 3-6
Develop transparent pricing skills (hx Renew or Python) and a Perplexity-based workflow for perils and market conditions.
Months 6-12
Go deep on an emerging peril and build client-ready analytics and narratives - the strategic, visible work that carries pay toward $168,790.
Gear for this job
As an Amazon Associate, PayCrunch earns from qualifying purchases. Links to books and tools are for the job on this page; we only recommend what we’d use in the work.
Same live O’Reilly 3rd already on data-scientist / python-developer / actuarial-consultant. This page’s lead is Learn Python (the pandas library) and the prompt is Write Python (pandas) to validate a property exposure bordereau. Not CompTIA Data+ and not leftover 94 CFP.
Next steps for a Reinsurance Analyst
Some links below are affiliate or partner links. PayCrunch may earn a commission if you enroll or subscribe through them, at no extra cost to you. Wage figures on this page still come from the Bureau of Labor Statistics, not from these programs.
Reinsurance Analyst work is specific enough that a stamped 'check out these courses' block would be noise. BLS files this work as Insurance Underwriters (SOC 13-2053). O*NET Job Zone 4 is typical: a bachelor's degree, so the honest next credential is a professional certificate or bachelor's-level coursework — not a random catalog dump.
The occupation's listed knowledge areas include Sales and Marketing and Administrative; the links search those subjects, not a generic 'career courses' list.
Reinsurance Analysts in this dataset list C++ among the tools in use, so a program that names that stack is a better fit than a survey course.
Coursera search for sales and marketing — a professional certificate or bachelor's-level coursework that lines up with business and finance, not a generic professional-development aisle.
FlexJobs screens remote, hybrid, freelance, and flexible listings so you are not wading through unverified ads. This is a job-board search for Reinsurance Analyst work, not a claim that they list a counted SOC 13-2053 inventory.
Write a Reinsurance Analyst resume, or one aimed at Financial Risk Specialists, instead of a blank template. Resume Now is a resume builder; we are not claiming a counted template set for this SOC.
A Reinsurance Analyst resume that names the actual tasks on this page, or the step-up title Financial Risk Specialists, beats a blank template when you apply.
What Reinsurance Analysts earn by state
These are the Bureau of Labor Statistics’ own figures for Insurance Underwriters, state by state — not a cost-of-living adjustment applied to the national number. Only states employing at least 500 people in the occupation are shown, because a state median drawn from a handful of workers is noise rather than a signal.
Massachusetts
$106,640
highest of them · +31% vs the national median
Puerto Rico
$46,990
lowest of the 34 states and territories that qualify · -42% vs the national median
The same job pays $59,650 more a year at the median in Massachusetts than in Puerto Rico — 127% higher. That gap is what the Bureau measured, before any question of what it costs to live in either place. The top-of-range figure quoted at the head of this page, $168,790, is a different statistic in a different place: it is the 90th-percentile wage in New York. The state that pays the typical worker most and the state where the best-paid go highest are not always the same one.
Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025, SOC 13-2053. 34 states and territories clear the 500-employee reporting floor for this occupation; those below it are left out rather than shown with a wide error band.
Free data. Use any of it.
PayCrunch publishes verified, BLS-sourced salary + AI-playbook data on 1,000+ professions — free, no signup.
No, but it will replace analysts who only clean data by hand. AI preps data and runs models; it cannot judge whether an assumption fits the risk, defend a price at renewal, or own the decision that binds capital. Analysts who use AI to automate the grind and focus on modeling and pricing judgment pull ahead; those who guard manual work fall behind.
Is it safe to use ChatGPT or Claude with treaty and cedant data?
Not with confidential cedant, treaty, or policyholder data - that belongs only in approved systems. Use consumer AI for methodology, plain-English explanations, and redacted or anonymized examples, and keep the real data inside your firm's secured, contractually covered tools.
Can I trust a catastrophe model's output?
Only after you test it. Cat models carry real uncertainty, and vendor defaults rarely fit a specific portfolio. Validate assumptions, run sensitivities, and communicate a range rather than a single number - the model informs the decision, but your judgment and the underwriter's govern the capital.
Which AI skill gives a reinsurance analyst the biggest edge?
Automating data preparation with Python. It reclaims the days lost to messy bordereaux each renewal, eliminates errors that corrupt models, and frees you for the cat modeling and pricing analysis that define top-of-range analysts.
Do I still need to understand cat models and pricing if AI runs them?
Yes - that understanding is exactly what AI lacks. Running a model is easy; knowing whether its assumptions fit the risk, where the tail is fragile, and how to defend the price is the scarce, well-paid skill. AI amplifies an analyst who understands the mechanics and exposes one who doesn't.
Methodology & sources
Salary (median, 10th, top of the range) — U.S. Bureau of Labor Statistics, OEWS.
By state — the Bureau of Labor Statistics’ own state medians, limited to states employing at least 500 people in the occupation. No cost-of-living arithmetic is applied to a wage anywhere on this page.
The plays — PayCrunch's own step-by-step guidance using publicly available AI tools. Tool names/URLs are real and current as of August 2026; prompts written to work as-is. Verify any professional output before relying on it.