Automating the reporting a fixed income analyst dreads
$239,700top of the range in South Dakota · middle $102,740 / yr
AI augments this role
Fixed Income Analysts in the United States earn a median of $102,740 a year. Pay starts near $63,720. Pay reaches $239,700 at the top of the range in South Dakota, 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 (Financial and Investment Analysts, SOC 13-2051). Last checked 9 September 2026.
Entry level
$63,720
Top of the range · South Dakota
$239,700
Education
Bachelor's degree in Finance
Wages — U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics, May 2025 (Financial and Investment Analysts). 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 Fixed Income AnalystReviewed September 2026
We track new AI-tool launches every week and refresh this list — here’s what’s gaining traction for Fixed Income Analyst work right now.
NumericNEWPaid / see site
AI-driven month-end close, reconciliation, and reporting.
How a Fixed Income 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 Fixed Income 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 Fixed Income 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 Fixed Income 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 Fixed Income 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 Fixed Income 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 Fixed Income 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 Fixed Income 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 Fixed Income Analyst uses it: analyze big reports or spreadsheets and turn messy notes into clean, finished writing
The note on the desk is about a rating change and a move in yields, and someone has to explain both before the portfolio meeting. That someone, in this career, is a fixed income analyst. The subject is bonds, interest rates, and credit. You read issuers, you follow the rate environment, and you write analysis that a portfolio manager, a credit committee, or a research desk can use. The decision about what a portfolio holds belongs to those people. Your job is the explanation: clear, sourced, and finished in time for the meeting that will actually use it.
The work sits on a buy-side desk at an asset manager, an insurer, or a pension; on a sell-side research desk; or inside a bank's credit organization. The audience changes. The material is still bonds. A government trader's analyst, a municipal specialist, a corporate credit analyst, and a person covering mortgage-related bonds are different seats inside one craft. What they share is a habit of tying a view to financial statements, to the terms of the bond, and to the rate backdrop, and of saying when the evidence is thin.
What the note has to carry
Credit work starts with the issuer. You read the financial statements, the borrowing terms, and the business that has to produce the cash. You notice a leverage trend, a liquidity squeeze, a customer concentration, or a covenant the company is approaching. You compare that picture with what a rating agency just said and with what the market price of the issuer's bonds already implies. Then you write. A useful note tells the reader what changed, why it matters for credit, and what you still do not know. It leaves the portfolio decision to the person who has the mandate. An analyst who buries the conclusion under adjectives, or who writes a slogan where a balance sheet belongs, will be corrected once and then avoided.
Rates are the other half of the week. The level and the shape of the interest-rate environment change the value of bonds already owned and the terms on which new bonds come to market. You follow central-bank communication, inflation reports, and the moves in benchmark yields, and you explain what those moves mean for the sectors you cover. You are not issuing an instruction to a trading desk. You are telling a portfolio manager or a committee how the rate backdrop touches the credits and the bond types in front of them. If you do not understand the difference between a credit event and a rate move, the note will mix two stories and the meeting will stall.
Sector coverage gives the job its shape. One analyst may live in investment-grade corporates, another in high-yield, another in tax-backed municipal bonds, another in government bonds, another in mortgage-related securities. Early in the career you support a senior analyst's universe: updating models, pulling filings, drafting the first version of a note, and keeping a calendar of earnings and rating reviews. Later the universe is yours. You know the issuers well enough to say what is new. You keep an internal view current when a filing, a rating action, or a change in the issuer's business lands. You can sit in the meeting and defend a paragraph without theater.
The tools are ordinary and demanding. A spreadsheet you can audit, a filing system you can refind, a data terminal the firm already pays for, and a writing style a busy portfolio manager will finish. Models are useful when every input has a source. They are dangerous when a hardcoded number has no owner. The analysts who last document assumptions, date their notes, and separate what the issuer reported from what the analyst inferred. That separation is the ethics of the seat. It is also what lets someone else pick up your coverage when you are out.
The charter, and a registration where the seat requires one
A bachelor's degree in finance, economics, or accounting is the ordinary start. It proves you can read statements and talk about interest rates with a vocabulary the desk already uses. Employers treat it as the door into an associate seat. It is preparation for the job. A licence to give personalized investment advice to the public is a different grant, and many research seats never require you to hold one.
The credential this desk respects most often is the CFA charter, granted by the CFA Institute. The charter proves you completed the institute's program, met its experience requirement, and committed to its ethics code. People prepare while they work, using the institute's curriculum and the filings they are already reading. Say on your resume where you are in the program, with the level named only if you have actually completed it. A charter in progress is a legitimate fact. A charter you merely intend is a wish, and wishes read poorly next to a candidate who has already done the work.
Some seats, particularly at a broker-dealer, require a FINRA registration. FINRA is the Financial Industry Regulatory Authority. The firm sponsors the registration. It is a registration. When a posting lists it, name it that way on your application and complete it on the firm's timetable. The FINRA site is the public home of that registration system. The registration is a regulatory status for people engaged in the firm's securities business. The CFA charter remains the analytical credential hiring managers discuss when they are judging the research. Collect the registration if the seat requires it. Do not describe it as a method for picking bonds. It is the firm's compliance with a registration.
Two different lines on the resume
The CFA charter speaks to the analysis. A FINRA registration, where the seat requires one, speaks to the firm's regulatory status for that role. List each one only when it is true. A posting that asks for both is asking for two facts, and a posting that asks for neither is still judging the writing sample.
Desks that hire, and what they read first
Asset managers, insurance investment offices, public pensions, banks, and independent research firms are the employers. Campus recruiting still feeds some associate classes, especially where a firm has a training program. Experienced hiring happens when a sector loses a person or when a portfolio grows enough to justify coverage it used to share. The first screen is whether you have touched the asset class. A resume aimed at municipal credit should not open with an equity project and hope the reader squints. Lead with bonds: a coursework project is acceptable at the start, an internship on a credit or rates desk is better, and a year of coverage is best.
The work sample matters more than a list of adjectives. A two-page note on a single issuer, with the financials sourced and the conclusion labeled as yours, will outperform a paragraph about being passionate about markets. Remove anything a former employer would consider confidential. If you are a student, use public filings and say so. In an interview you may be asked to walk through that note, to explain a recent rating action in a sector you claim, or to say how a rise in benchmark yields would show up in the bonds you follow. Stay inside explanation. The moment you turn the interview into a list of trades you wish the firm would do, you have left the job you are being hired for.
Hiring managers listen for intellectual honesty. They want to hear what would change your view, which filing you would open first, and where your coverage is thin. They want clean language. Fixed income has enough jargon to hide in, and the people who advance refuse the hiding. If a FINRA registration is required, the firm will tell you and will sponsor it. If the CFA program is expected, say exactly how far you are. References should be someone who has read your writing under a deadline, not only a professor who liked your attendance.
Geography is part of the market. A large share of the seats cluster where asset managers and banks cluster, and a smaller number sit inside insurers and pensions elsewhere. Be willing to name the cities you will actually live in. A remote arrangement exists on some research desks and is uncommon on desks that sit next to a portfolio they support every morning. Ask. Then believe the answer the hiring manager gives you about where the work happens, because a note written far from the meeting is only useful if someone in the meeting still reads it.
From a slice of coverage to a sector
The path runs from associate, supporting a senior analyst's names, to analyst with your own coverage, to a senior or sector-lead role that sets the research agenda for a group. Some people become portfolio managers. That is a different job: the decision, the mandate, and the accountability for results move into your chair. The analyst path can be a full career without that move. A sector lead who writes the notes others rely on, who trains associates, and who can brief a committee cleanly is already at the top of this craft.
What moves you is a record of notes that were right about the facts and useful about the uncertainty. Keep a private file of work you are allowed to discuss, with confidential positions removed. In the next review, you should be able to point to a theme you identified from filings, a rating change you explained before the meeting asked, and an associate you made better. Technical range helps: corporates and municipals are different languages, and a person who can translate one without pretending to be an expert in the other is valuable. Pretending is how coverage errors happen.
Some analysts later move to a credit-risk role inside a bank, to a rating agency, or to investor relations at an issuer. Each of those is a translation of the same reading skill to a new audience. If you make the move, describe the audience change honestly. If you stay, deepen the sector until a portfolio manager can trust your paragraph on a name they do not have time to rebuild. That trust, renewed every earnings season, is the promotion that does not always come with a new title.
Reading the May 2025 analyst figures
The wages are Occupational Employment and Wage Statistics for May 2025. The series is Financial and Investment Analysts, a broader group than the bond, rate, and credit work this career describes, so the dollars cover that wider occupation. Entry pay is $63,720. The national median is $102,740. The gap from entry to the median is $39,020. An associate offer near $63,720, for a seat that already expects original notes and not only data pulls, can be discussed against that gap. Ask what would move the offer toward the median: a completed stretch of the CFA program, an internship on a credit desk, or coverage you can show in a work sample.
State medians are typical pay, a different statistic from the high end of a published range. New York's median is $127,930, which sits $25,190 above the national median. Oregon is $120,590, Massachusetts is $111,040, California is $109,110, and New Jersey is $108,610. A move should be negotiated against the median of the state where you will sit, with rent named in the same conversation. New York's median is the local midpoint for this broad analyst occupation, and a bond desk in that market can use it as the anchor while still explaining the specific coverage the seat owns. The lowest median in the published set is Puerto Rico at $63,000. The gap between New York's median and Puerto Rico's median is $64,930. Title matches across those places are weak guides. Use the median for the desk's actual city and state.
The high end of the published range in South Dakota is $239,700, where the series was large enough for the Bureau to show a high end. That figure is the high end of the published range in South Dakota. A state median is a different statistic, and the medians to quote for a move are the ones named above, not a number you invent for South Dakota. The stretch from the national median to that South Dakota high end is $136,960. Use $239,700 only when the seat, the market, and your record as a sector lead genuinely sit at the top of the published range. An associate negotiation belongs with $63,720 and the path toward $102,740. Name May 2025, remember the series is broader than bonds, and stop once the figure matches the rung.
The top of Fixed Income Analyst pay — and how to get there with AI
$239,700what Fixed Income Analyst pay reaches in South Dakota
Highest state-level top-of-range annual wage for Financial and Investment Analysts, 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 Managers — reaches $370,780 in New York.
$63,720entry$102,740middle$239,700top end
The fixed income analysts paid at the top of the range are the ones whose recurring reporting largely builds itself, leaving their hours for valuation, pricing and the recommendations somebody acts on.
Junior work in this seat is dominated by assembly: pulling prices, reconciling positions, rebuilding the same monthly pack, chasing a figure that moved. Analysts who break out do the same tasks in a fraction of the time and spend what they save on securities valuation, on monitoring fundamental economic and corporate developments closely enough to have a view, and on presenting written reports that change a decision. Models and the automation tools around them make the assembly work collapsible in a way it was not before, but only for the analyst who bothers to rebuild the process instead of grinding through it again.
Your playbook, by where you are now
Just startingRebuild your own recurring pack
Time yourself for one full monthly cycle and write down every manual step, including the ones you are embarrassed by.
Replace the worst three steps with formulas or a script, and have Claude write the first draft of the code.
Add reconciliation checks that fail loudly, so a wrong price cannot travel silently into a report.
Use Excel Copilot for the routine transformations and keep a dated copy of the workbook every cycle.
Write the pack's assumptions down in one place so somebody else could run it if you were away.
What proves it: A monthly pack that takes a fraction of the hours it did, with the check log to show it is still right.
Realistic span: Two to three cycles
A few years inAutomate what the whole desk repeats
Move the data preparation into Alteryx software or a query layer so it is not living inside one person's spreadsheet.
Build the standing dashboard the desk keeps asking for, in the business intelligence software your firm already licenses.
Set up Power Automate to distribute what used to be sent by hand, on a schedule nobody has to remember.
Feed the research publications you cannot keep up with into NotebookLM and question them, then verify anything you cite.
Take on the pricing and valuation work freed up by the time saved, and make sure your name is on the analysis.
What proves it: A process the desk depends on that runs whether or not you are at your desk.
Realistic span: One to three years
ExperiencedOwn the analytics the desk trades on
Own the valuation methodology itself, and document why it is right rather than merely inherited.
Prepare the materials for transactions to a standard where legal and operations stop returning them.
Present written and oral reports on industries and individual corporations that carry a clear recommendation.
Supervise and train junior team members on the automated process, so the capability outlives your role.
Review anything a model produced before it reaches a client, because the accountability is not delegable.
What proves it: A published methodology and a track record of recommendations attributable to you.
Realistic span: Three to seven years
The next 90 days
Take your next monthly cycle and instrument it. Keep a running note of every step you perform by hand, how long it took, and what would have caught the error if you had made one. At the end you will have a ranked list of what to kill first. Pick the top item, rebuild it with a script or a formula, and add a reconciliation check that fails loudly when a price does not tie. Then spend the hours you recovered on one piece of real analysis — a valuation you would defend, or a written view on a corporate development you have been watching — and put it in front of someone senior.
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).
Start with AI document analysis - it attacks the most time-consuming part of credit work. Point AlphaSense or Hebbia (or Bloomberg's AI document search) at issuer filings, earnings transcripts, and rating reports to extract financials and language in minutes instead of hours. Then verify every number against the source filing before it enters a model.
For modeling, use Excel with Microsoft 365 Copilot or Python; for synthesis and drafting, use Claude or ChatGPT, and Perplexity for cited macro context. Use public filings and general methods only - keep MNPI, restricted names, and client positions strictly inside your firm's approved systems.
The one rule, forever: Credit research runs on material non-public information rules - if you're brought over the wall on a private deal or restructuring, that name is restricted, and nothing about it goes into any AI tool. Never paste MNPI, client positions, or proprietary models into a consumer AI. AI-extracted financials and covenant readings are drafts that must be verified against the actual filing and indenture line by line - a hallucinated leverage ratio or misread covenant can sink a recommendation and real money.
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
Digest filings, transcripts, and indentures in minutes
Why this pays: Credit analysis is drowning in documents - 10-Ks, earnings calls, rating reports, offering memos. AI that extracts the financials and the key language fast lets you cover more issuers at the same depth, and breadth-with-depth is exactly what separates a junior analyst from a senior one who gets paid.
AlphaSenseHebbiaBloomberg Terminal
1
Use AlphaSense or Hebbia to search across an issuer's filings and transcripts for the things that move credit - liquidity, maturity walls, covenant headroom, guidance changes - then pull the exact source passages to verify.
2
Turn a filing into a structured credit summary you then check.
Copy-paste this prompt
Act as a credit analyst. From this public issuer filing [paste 10-K/10-Q text or section], extract into a table: revenue, EBITDA, total debt, cash, interest expense, and any maturities in the next 24 months. Then summarize the three biggest credit risks and any liquidity concerns, quoting the exact language for each. Note anything ambiguous I must confirm in the full filing.
Verify every extracted figure against the source filing before it touches a model - AI misreads financial tables, and one wrong number invalidates the credit view.
3
Keep a per-issuer file of AI-extracted, human-verified facts so your coverage stays current and your next update takes minutes, not a day.
What you'll haveBroad coverage without sacrificing depth - the analyst who knows more credits, more precisely, is the one who makes senior.
2
Build and stress credit models faster
Why this pays: The quality of a recommendation rests on the model behind it - default probability, recovery, downgrade risk under stress. AI that helps you build and scenario-test those models faster means more rigorous work per name, and rigor is what earns a PM's trust and the analyst's seat on the desk.
Microsoft 365 Copilot (Excel)Python (pandas)Claude
1
Use Excel with Copilot or Python to build the credit model - leverage and coverage trajectory, free-cash-flow, maturity ladder - and to run downside scenarios far faster than by hand.
2
Have AI design the stress cases you might not think of.
Copy-paste this prompt
Act as a senior credit analyst stress-testing an issuer. Given this base-case credit profile [paste public figures: revenue, EBITDA, debt, maturities, margins], design three downside scenarios (mild, moderate, severe) with specific assumptions for revenue decline, margin compression, and refinancing cost. For each, tell me what to compute - leverage, interest coverage, liquidity runway - and at what thresholds the credit is at risk of downgrade or distress. Show the logic.
The scenarios are a framework; run the actual math in your model and verify every input against filings. AI can suggest a stress case but can't be trusted to compute your credit's numbers.
3
Keep models in a consistent, version-controlled template so your work is auditable and comparable across your coverage - consistency is what makes your calls credible.
What you'll haveMore rigorous, stress-tested credit work per name - the analytical depth that gets your recommendations traded on.
3
Read covenants and documentation for hidden risk and edge
Why this pays: The value in credit often hides in the documents - a covenant loophole, a restricted-payment basket, a weak-lien structure. AI that helps you parse dense indentures surfaces protections and traps other analysts miss, and finding those first is a genuine, defensible source of edge.
HebbiaAlphaSenseClaude
1
Use Hebbia or Claude on the public offering memo or indenture to locate and explain the covenant package - restricted payments, debt incurrence, liens, change-of-control - then read the actual clauses yourself.
2
Translate legalese into credit-relevant plain English.
Copy-paste this prompt
Act as a leveraged-finance credit analyst. From this public indenture excerpt [paste covenant section], explain in plain English: what this covenant restricts, how much headroom the issuer has, what carve-outs or baskets weaken it, and how it could be used to move value away from bondholders. Flag the single biggest risk to a creditor and quote the exact language. Tell me what defined terms I must check.
AI reads covenants unevenly and can miss cross-references and defined-term traps. Always confirm the actual clause and definitions in the full document - the legal reading is yours to own.
3
Build a covenant-comparison note across similar issuers so you can argue which bonds have real structural protection and which are cheap for a reason.
What you'll haveStructural risks and protections spotted before the market prices them - the documentation edge behind differentiated, well-paid credit calls.
4
Find relative value across the curve and rating buckets
Why this pays: Analysts get paid for actionable calls, not encyclopedias. AI that helps you screen for spread dislocations - bonds cheap or rich to their curve, peers, or rating - turns your research into concrete relative-value recommendations a PM can act on, the output that defines a top analyst.
Bloomberg TerminalCreditSightsClaude
1
Use Bloomberg and CreditSights to gather spreads and fundamentals across an issuer's curve and its peer group, then look for names trading off where their credit quality says they should.
2
Pressure-test a relative-value idea before you write it up.
Copy-paste this prompt
Act as a relative-value credit analyst. Given these public comparable bonds [paste: issuers, maturities, ratings, spreads, key credit metrics], identify which look cheap or rich relative to fundamentals and the curve. For each, explain what could justify the gap (liquidity, structure, trajectory) versus what looks like genuine mispricing, and what would confirm the thesis. Analysis only, not a recommendation.
A screen for ideas, not a call. Verify every spread and metric on your terminal, and pair each idea with the catalyst and risk before it goes to a PM.
3
Write each idea as a clear recommendation with entry level, catalyst, target, and downside - a call with a defined risk/reward is what a PM actually trades.
What you'll haveActionable relative-value recommendations, not just reports - the analyst output that moves a portfolio and earns the PM track.
5
Publish sharp research and monitor your credits for early warnings
Why this pays: A credit analyst's reputation is built on being early - flagging the deterioration before the downgrade, the recovery before the rally. AI that drafts crisp research and monitors your names for warning signs lets you cover more and react first, the early-and-right record that makes analysts indispensable.
ClaudeAlphaSenseBloomberg Terminal
1
Set AlphaSense and Bloomberg alerts on your covered issuers for rating actions, guidance cuts, covenant news, and management changes so deterioration reaches you first, not last.
2
Draft institutional-grade research fast without losing your judgment.
Copy-paste this prompt
Turn my credit analysis notes into a concise institutional research note. Notes: [paste your own verified analysis - thesis, key metrics, risks, recommendation]. Structure it as: recommendation and rationale up front, credit summary, key risks, and catalysts/what would change the view. Keep it tight and professional, and add no facts or numbers I didn't provide.
The model formats and sharpens; it must invent nothing. Every figure is one you verified, and the recommendation and its risk are yours to stand behind.
3
Track how your calls age - which credits you flagged early, which you missed - and feed that back into your process; a documented, honest track record is what earns the promotion.
What you'll haveMore coverage, earlier warnings, and sharper published research - the early-and-right reputation that lifts a credit analyst into the top of the band.
Your 12-month sequence to the top of the range
How the plays above stack into a path from median pay toward the $239,700 tier.
Month 1
Put AI document analysis (AlphaSense, Hebbia, or Bloomberg AI) at the front of your workflow and build the habit of verifying every extracted number against the filing.
Months 2-3
Rebuild your credit model with Copilot/Python and add AI-designed stress scenarios; standardize a version-controlled model template across your coverage.
Months 3-6
Add AI covenant analysis and relative-value screening; start writing every idea as an actionable recommendation with catalyst, target, and downside.
Months 6-12
Set AI monitoring on all your credits, publish sharp research consistently, and track your calls - build the early-and-right record that puts you on the PM track.
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. This page names Python (pandas) as a play tool next to Excel Copilot for the credit-model rebuild. Not leftover 94 CFP and not CFA Level I as the lead (that is financial-analyst / credit-analyst).
Next steps for a Fixed Income 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.
Fixed Income Analyst work is specific enough that a stamped 'check out these courses' block would be noise. BLS files this work as Financial and Investment Analysts (SOC 13-2051). 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.
Fixed Income Analysts in this dataset list Alteryx software among the tools in use, so a program that names that stack is a better fit than a survey course.
The next title this dataset points at is Financial Managers; a credential aimed that way is a clearer step than another year in the same seat.
Coursera search for accounting and finance — 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 Fixed Income Analyst work, not a claim that they list a counted SOC 13-2051 inventory.
Write a Fixed Income Analyst resume, or one aimed at Financial Managers, instead of a blank template. Resume Now is a resume builder; we are not claiming a counted template set for this SOC.
A Fixed Income Analyst resume that names the actual tasks on this page, or the step-up title Financial Managers, beats a blank template when you apply.
What Fixed Income Analysts earn by state
These are the Bureau of Labor Statistics’ own figures for Financial and Investment Analysts, 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.
New York
$127,930
highest of them · +25% vs the national median
Puerto Rico
$63,000
lowest of the 44 states and territories that qualify · -39% vs the national median
The same job pays $64,930 more a year at the median in New York than in Puerto Rico — 103% 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, $239,700, is a different statistic in a different place: it is the 90th-percentile wage in South Dakota. 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 13-2051. 44 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 - it replaces the document grunt work, not the judgment. AI can extract financials and summarize an indenture, but deciding whether a credit is money-good, where the spread should trade, and when the rating agencies are behind is human analysis with a career and a portfolio on the line. Analysts who use AI to cover more credits at more depth become more valuable; those who spend their days manually copying numbers from filings are doing the part that's being automated.
Is it safe to use ChatGPT or Claude for credit research?
Only with public filings and general methods. Material non-public information - anything you're over the wall on, plus client positions and proprietary models - can never touch an AI tool. And treat every AI-extracted number and covenant reading as a draft: verify it against the actual filing and indenture, because a hallucinated ratio or misread clause can wreck a recommendation.
Can AI build my credit models for me?
It can accelerate them - scaffolding the spreadsheet, suggesting stress scenarios, speeding the calculations - but the assumptions and the verification are yours. AI doesn't know your issuer's real numbers and will confidently invent them if you let it. Use it to build faster, then confirm every input against filings and own the analysis.
How does AI actually increase a fixed income analyst's pay?
By expanding your coverage and sharpening your calls. AI document analysis lets you cover more issuers deeply; faster modeling makes each call more rigorous; covenant and relative-value work surface edge others miss; and monitoring gets you early on deterioration. Analysts are paid for actionable, early, correct recommendations - and AI is leverage on all of it. That's the path from the $103k median to the $240k senior seat.
Which AI capability should a fixed income analyst adopt first?
AI document analysis - AlphaSense, Hebbia, or Bloomberg's AI search. It attacks the single biggest time sink in credit work and frees hours for the judgment that actually pays. Build the discipline of verifying every extracted figure against the source, then layer AI modeling and covenant analysis on top.
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.