COMPLETE INTERVIEW GUIDE

Financial Analyst: Tricky Interview Questions, Best Answers & Complete Interview Guide

By salarymarkets.io · Original preparation guide · General advice, not employer-verified questions

Financial analyst interviews test how you move from data to a useful decision. Employers want reliable calculations, but they also want to see how you question assumptions, explain uncertainty and recommend action when the information is incomplete.

Prepare examples that connect the business question, source data, analytical method and recommendation. Distinguish observed results from forecasts and your contribution from the team's work. A persuasive answer explains what could change the conclusion rather than presenting a spreadsheet as unquestionable evidence.

01Understanding the Financial Analyst role

A financial analyst evaluates performance and supports planning, investment or resource decisions. Depending on the team, responsibilities may include budgets, forecasts, management reporting, profitability analysis, valuation and decision support. Corporate FP&A, treasury and investment research are different disciplines, so confirm the position's actual mandate.

Useful analysis begins by defining the decision, comparison and relevant time horizon. Analysts validate source information, select appropriate drivers and distinguish recurring operating performance from timing or one-time effects. They explain limitations so stakeholders can use the output responsibly.

Common tools include spreadsheets, planning platforms, ERP extracts, SQL and visualization software. Strong analysts maintain clear definitions, test calculations and document assumptions. They also work with accounting and operating teams to reconcile competing views of the business without treating every disagreement as a data error.

Average versus exceptional performance
Area	Average performer	Exceptional performer
Forecasting	Extends recent trends	Models relevant drivers and explains changing assumptions
Variance analysis	Reports differences	Connects verified causes to decisions and accountable actions
Modeling	Builds complex formulas	Creates transparent, tested logic appropriate to the question
Communication	Presents every calculation	Leads with the decision, evidence and important uncertainty
Partnership	Collects budget inputs	Challenges assumptions respectfully and resolves definition gaps
Governance	Saves the latest workbook	Maintains ownership, version control and reproducible sources

An FP&A analyst may focus on operating drivers and planning, while a treasury analyst studies liquidity and an investment analyst evaluates securities or transactions. Tailor your examples to the stated role and be explicit about specialist methods you have not yet used independently.

02What AI can and cannot do

AI can accelerate analysis preparation, but it cannot establish whether a business assumption is reasonable. Use approved tools, protect confidential information and independently verify every material calculation and claim.

What AI already does well

Suggesting structures for nonconfidential analytical briefs
Drafting spreadsheet formulas for independent testing
Creating fictional interview cases
Organizing approved reporting definitions into reference notes

Where AI helps but needs oversight

Variance narratives that may invent causes
SQL drafts that can duplicate records through joins
Forecast suggestions that may ignore operating constraints
Research summaries that require checking original sources

Where human judgment remains especially valuable

Choosing decision-relevant drivers
Assessing management assumptions and incentives
Balancing uncertainty against decision urgency
Communicating a recommendation and accepting accountability

Skills to develop now

Understanding approved data-use policies
Testing generated formulas and queries
Separating correlation from causation
Documenting assumptions and scenario boundaries
Explaining analytical limitations without obscuring the conclusion

0316 tricky interview questions and strong answers

1. “How do you build a forecast?”

Why they ask: Employers want a reasoned process rather than extrapolation alone.
A strong answer shows: Driver selection, validation and ownership.
Example answer: I would clarify the forecast's purpose, horizon and level of detail before choosing a method. I would reconcile historical inputs and identify the operating drivers that explain the main revenue, cost and cash movements. I would work with business owners to validate assumptions and distinguish committed activity from uncertain expectations. I would test the model against known periods and create scenarios for important uncertainties. After approval, I would track forecast error and update assumptions as new evidence arrives.
Avoid: Applying one growth rate to every line without a business rationale.

2. “How would you explain a revenue shortfall?”

Why they ask: A variance matters only if its drivers are understood.
A strong answer shows: Reconciliation and actionable decomposition.
Example answer: I would confirm consistent definitions, periods and revenue recognition before interpreting the shortfall. I would decompose the change into relevant volume, price, mix and timing effects, using a method that reconciles to the total without double counting. I would validate likely causes with commercial and accounting owners. I would then explain which effects are temporary, recurring or still uncertain and identify the decision needed. I would not describe a plausible explanation as proven simply because it fits the headline.
Avoid: Repeating the percentage difference without investigating its cause.

3. “How are profit and cash flow different?”

Why they ask: Analysts must avoid treating earnings as available liquidity.
A strong answer shows: Accrual understanding and cash conversion.
Example answer: Profit reflects recognized revenues and expenses under the applicable accounting basis, while cash flow reflects receipts and payments. I would bridge the two through noncash items, working-capital changes and other relevant adjustments, keeping operating, investing and financing activity distinct. For a growing business, I would investigate whether receivables or inventory consume cash despite reported profit. I would reconcile the bridge to the financial statements and check timing assumptions. The decision implication depends on liquidity needs, not earnings alone.
Avoid: Assuming positive net income means the company can meet every payment.

4. “What makes a financial model reliable?”

Why they ask: Complexity can conceal mistakes.
A strong answer shows: Transparent architecture and proportionate testing.
Example answer: I would separate inputs, calculations and outputs and make units, dates and assumptions explicit. I would include checks for reconciliations, balance consistency and unexpected signs or missing data. I would test known cases and important boundary conditions, then obtain an independent review proportionate to the decision risk. The documentation would identify sources, ownership and version changes. I would prefer a simpler model that answers the question to an elaborate workbook nobody can maintain.
Avoid: Equating advanced functions with analytical quality.

5. “How do you evaluate a capital investment?”

Why they ask: A project case requires more than an attractive headline return.
A strong answer shows: Incremental cash flows and uncertainty.
Example answer: I would define the alternatives, decision horizon and incremental after-tax cash flows using the company's approved methodology. I would consider investment timing, working capital, ongoing costs and terminal assumptions without counting sunk costs as future benefits. I would use an appropriate approved discount rate and explain sensitivity to key drivers. I would also assess operational feasibility and risks the model does not capture. My recommendation would state the conditions under which the project remains attractive and what needs validation before approval.
Avoid: Using accounting profit as a substitute for project cash flow.

6. “A business leader challenges your forecast. How do you respond?”

Why they ask: Partnership requires constructive challenge without surrendering rigor.
A strong answer shows: Listening, evidence and documented assumptions.
Example answer: I would ask which assumptions they disagree with and whether they have operational information I have not incorporated. I would walk through the relevant drivers and source evidence rather than defend the spreadsheet as a whole. If the new information is credible, I would update the forecast and record the change. If the disagreement remains uncertain, I would show alternative scenarios and route the assumption to the designated decision owner. I would keep the base forecast distinct from a target or aspiration.
Avoid: Changing the forecast solely to match a preferred result.

7. “How do you work with incomplete data?”

Why they ask: Decisions often cannot wait for perfect information.
A strong answer shows: Bounded assumptions and proportionate escalation.
Example answer: I would identify which missing inputs could materially change the decision and prioritize obtaining those first. For the remaining gaps, I would use transparent assumptions or ranges supported by available evidence. I would test sensitivity and explain whether the recommendation is robust or depends heavily on an uncertain input. If the gap is too important, I would recommend delaying or narrowing the decision rather than manufacture precision. I would document what needs to be refreshed when better data becomes available.
Avoid: Filling blanks silently or presenting estimates as observed facts.

8. “How do you reconcile conflicting data sources?”

Why they ask: Dashboards and ledger reports may use different definitions.
A strong answer shows: Data lineage and consistent scope.
Example answer: I would compare the definitions, time periods, entities, currencies and transaction statuses used by each source. I would reconcile control totals and trace a sample of differences to underlying records. I would work with the data owners to identify whether the discrepancy reflects timing, transformations or an error. The final analysis would state the chosen source and why it is appropriate for the question. I would not average conflicting totals or quietly select the one that supports the preferred narrative.
Avoid: Assuming the most polished dashboard is the authoritative source.

9. “How would you analyze product profitability?”

Why they ask: Cost allocation can distort a seemingly simple margin comparison.
A strong answer shows: Decision-relevant cost definitions.
Example answer: I would clarify whether the decision concerns pricing, capacity, product mix or long-term portfolio performance. I would reconcile revenue and direct costs and distinguish contribution margin from fully allocated profitability. I would test allocation methods and identify shared costs that may not disappear if a product is discontinued. I would also consider customer behavior, constraints and working-capital effects where relevant. I would present the result with its cost definitions and show how the recommendation changes under reasonable alternatives.
Avoid: Treating allocated overhead as automatically avoidable.

10. “What would you put in an executive presentation?”

Why they ask: Decision makers need focus, not a workbook tour.
A strong answer shows: Clear recommendation and supporting evidence.
Example answer: I would begin with the decision or performance issue, then state the recommendation and its main supporting drivers. I would use a small number of clearly labeled visuals with consistent definitions and units. Important assumptions, risks and alternatives would be visible rather than hidden in an appendix. Detailed calculations would remain available for challenge and review. I would end with the action, owner and timing needed, adjusting the depth to the audience without concealing uncertainty.
Avoid: Overloading slides with tables or implying certainty through formatting.

11. “How do you analyze forecast accuracy?”

Why they ask: Accuracy reviews should improve decisions rather than assign blame.
A strong answer shows: Comparable measures and root-cause learning.
Example answer: I would preserve the forecast version and compare it with actuals on a consistent basis. I would choose error measures appropriate to the data, avoiding misleading percentage metrics when actuals are near zero. I would separate model issues, assumption changes and unforeseen events, then look for persistent bias across periods or owners. The review would focus on improving drivers and update timing. I would not claim a method is superior based on a single favorable period.
Avoid: Replacing the original forecast before evaluating its error.

12. “How would you build a headcount forecast?”

Why they ask: Staffing costs depend on timing and policy details.
A strong answer shows: Operational realism and confidentiality.
Example answer: I would reconcile the approved employee roster and planned roles with HR and budget owners using authorized access. I would model start dates, vacancies, compensation assumptions, benefits and other relevant costs under the company's planning policies. I would separate approved positions from proposed additions and test hiring delays or attrition scenarios. I would reconcile the output with payroll and the broader expense plan. Sensitive employee information would stay in approved systems, with only necessary detail shared.
Avoid: Multiplying year-end headcount by a single annual cost without timing adjustments.

13. “What if you discover an error in a recommendation already sent?”

Why they ask: Analytical integrity includes correcting the record promptly.
A strong answer shows: Containment, transparency and prevention.
Example answer: I would verify the error, quantify its effect and identify which recipients or decisions are affected. I would alert the responsible manager promptly and issue a clearly labeled correction through the appropriate channel. If the recommendation changes, I would explain the revised conclusion and any action needed. I would preserve version history rather than overwrite the evidence silently. After containment, I would improve the specific test or review step that failed and avoid claiming the issue is harmless before assessing its impact.
Avoid: Waiting to see whether anyone notices.

14. “How do you prioritize ad hoc requests?”

Why they ask: Urgent analysis can crowd out essential planning work.
A strong answer shows: Decision value and capacity management.
Example answer: I would clarify the decision, deadline and minimum useful output for each request. I would prioritize by impact, urgency and dependencies, distinguishing a real decision need from a preference for additional detail. Where requests conflict, I would agree tradeoffs with the manager and offer a staged answer if appropriate. I would state limitations when delivering an initial view rather than imply that a rapid estimate is a complete review. Repeated requests would be candidates for a controlled recurring report.
Avoid: Accepting every deadline without communicating the consequences.

15. “How do you use SQL or automation responsibly?”

Why they ask: Faster extraction does not guarantee accurate analysis.
A strong answer shows: Query validation and controlled reuse.
Example answer: I would first understand the data grain, keys and relevant business definitions. I would validate joins, filters and aggregation against source totals and known examples, paying particular attention to duplicate matches and missing records. Reusable logic would have documentation, version control and appropriate review. I would use approved access and avoid moving confidential data to unapproved tools. If I lack experience with a platform, I would say so and demonstrate how I would test my work under supervision.
Avoid: Claiming a query is correct because it runs without an error.

16. “What would your early priorities be?”

Why they ask: A practical onboarding plan signals sound judgment.
A strong answer shows: Business learning and dependable delivery.
Example answer: I would learn the business model, reporting definitions, planning calendar and the decisions the team supports. I would reconcile key reports and understand how accounting and operational systems connect. I would meet the owners of important forecast drivers and clarify review expectations. After delivering reliable initial work, I would propose a focused improvement based on observed needs. I would seek training in unfamiliar industry or technical areas rather than promise immediate transformation.
Avoid: Promising a new forecast system before understanding the current process.

Adapt these examples to your real experience — never invent results you can't discuss in detail.

04Practical tests and real-world challenges

Driver-based forecasting case

Evaluated: Business logic, model structure and assumption discipline.
Approach: Validate the supplied history, choose relevant drivers and build a forecast with explicit inputs, checks and scenarios.
Excellent looks like: A transparent model that reconciles to the source and explains which assumptions most affect the decision.
Common mistakes: Hard-coding outcomes, inventing missing facts or overlooking capacity and timing constraints.

Variance analysis and executive brief

Evaluated: Reconciliation, causal reasoning and concise communication.
Approach: Confirm definitions, bridge actuals to plan and write a short recommendation separating evidence from hypotheses.
Excellent looks like: A complete bridge with no double counting, verified drivers and a clear action or follow-up owner.
Common mistakes: Repeating percentages, claiming causation without evidence or burying uncertainty.

Investment appraisal exercise

Evaluated: Incremental cash-flow logic and decision judgment.
Approach: Define alternatives, model relevant cash flows under stated assumptions and test sensitivity using the approved evaluation method.
Excellent looks like: A defensible recommendation that explains timing, risks and the conditions that would reverse the conclusion.
Common mistakes: Counting sunk costs as future cash flows, using unsupported discount assumptions or ignoring working capital.

0510 ways to stand out

Prepare examples that connect analysis to a real decision without invented results.
Explain the operating drivers behind your forecast rather than only its formulas.
Practice reconciling a variance bridge fully to the reported total.
Use self-created sample data when demonstrating modeling skills.
State uncertainty and show sensitivity without avoiding a recommendation.
Describe a respectful challenge to a business assumption.
Explain model checks and data-lineage validation clearly.
Distinguish a forecast from a target and a scenario from a prediction.
Discuss approved AI use alongside independent verification and confidentiality.
Tailor technical preparation to FP&A, treasury, investment or other actual responsibilities.

0610 questions to ask your interviewer

Which decisions will this role influence most? — Clarifies the purpose of the analysis.
What are the main forecast drivers? — Reveals the operating model and planning complexity.
How are financial definitions governed? — Identifies consistency and data-quality risks.
Who owns and approves forecast assumptions? — Clarifies accountability and challenge routes.
How does finance partner with operations? — Shows how analysis becomes action.
What recurring reports need the most improvement? — Surfaces realistic early opportunities.
How are models reviewed and versioned? — Tests analytical governance.
How is forecast performance evaluated? — Reveals whether reviews support learning.
What are the busiest planning periods? — Clarifies workload and scheduling expectations.
What would successful onboarding look like? — Sets achievable early priorities.

07Interview expectations by company type

These categories overlap, and every employer is different. Use them as typical patterns, not universal rules.

Large corporate FP&A team

Priorities: Consistent planning, executive reporting and business partnership.
How the role differs: Supporting budgets, forecasts and performance reviews for assigned units.
Evaluated skills: Driver modeling, reconciliation and concise commentary.

Q1. How do you consolidate unit forecasts?
Validate shared definitions and templates, reconcile submissions and resolve inconsistent assumptions before combining them.
Q2. What if a unit misses its submission?
Escalate the dependency early, agree a transparent interim assumption and track the required replacement rather than hide the gap.
Q3. How do you explain corporate allocations?
Document the approved methodology and separate allocated effects from the unit's controllable operating drivers.

Prepare: Review the business segments and planning calendar.
Avoid: Assuming all business units have identical drivers.
Smart question: “How are central planning assumptions balanced with unit-level knowledge?”

Early-stage company

Priorities: Cash visibility, practical planning and adaptable decision support.
How the role differs: Building focused analyses with evolving systems and limited historical data.
Evaluated skills: Scenario planning, source validation and prioritization.

Q1. How would you assess cash runway?
Reconcile available cash, model payment and collection timing and show how hiring or revenue scenarios affect liquidity.
Q2. What if historical data is sparse?
Use explicit driver assumptions and ranges, validate them with operating evidence and update the model as observations accumulate.
Q3. Would you build a complex planning system immediately?
I would first define the decisions and data needs, then choose a proportionate, controlled solution the team can maintain.

Prepare: Understand the funding model and distinguish public facts from assumptions.
Avoid: Presenting optimistic growth as committed cash generation.
Smart question: “Which decisions most urgently require better financial visibility?”

Manufacturing company

Priorities: Capacity, cost performance and disciplined capital allocation.
How the role differs: Analyzing production drivers, margins and investment alternatives.
Evaluated skills: Volume and cost bridges, operational partnership and cash-flow analysis.

Q1. How do you assess an equipment proposal?
Model incremental cash flows, implementation timing and capacity benefits, then test utilization and cost assumptions with operations.
Q2. Why did margin decline?
Separate price, volume, mix and production-cost effects using a reconciled method and validate causes with plant owners.
Q3. How do you evaluate inventory growth?
Connect inventory to demand, lead times and cash needs, distinguishing strategic buffers from slow-moving stock.

Prepare: Learn the production model and major cost drivers.
Avoid: Assuming higher output always improves cash or profitability.
Smart question: “How do analysts work with plant leadership on operating assumptions?”

Software subscription business

Priorities: Retention, growth efficiency and sustainable cash generation.
How the role differs: Modeling customer and revenue drivers alongside spending and liquidity.
Evaluated skills: Cohort analysis, metric definitions and scenario modeling.

Q1. How do you define recurring revenue?
Use the company's documented definition and reconcile it to underlying contracts, keeping the metric distinct from recognized accounting revenue.
Q2. How do you analyze churn?
Separate customer and revenue measures, define the cohort and period, and investigate changes in product, segment or renewal behavior.
Q3. What if acquisition costs rise?
Validate the cost and customer definitions, examine cohort economics and timing, and avoid treating a blended metric as a complete answer.

Prepare: Understand the subscription model and stated metric definitions.
Avoid: Mixing bookings, billings, revenue and cash.
Smart question: “Who governs recurring-revenue and retention definitions?”

Retail and e-commerce

Priorities: Margin, inventory productivity and seasonal cash planning.
How the role differs: Supporting merchandising, channel and operating decisions.
Evaluated skills: Seasonality analysis, contribution economics and demand scenarios.

Q1. How do you evaluate a promotion?
Estimate incremental contribution after discounts, returns and relevant costs, accounting for displaced sales and timing where evidence permits.
Q2. How do you forecast seasonal sales?
Use comparable periods and current drivers, adjust for calendar and channel changes and show uncertainty around demand and stock availability.
Q3. What if sales rise but margin falls?
Analyze price, mix, discounts, fulfillment and returns before recommending action.

Prepare: Review channel mix, seasonality and inventory exposure.
Avoid: Treating gross sales growth as proof of stronger economics.
Smart question: “How are commercial and inventory plans reconciled?”

Healthcare organization

Priorities: Service sustainability, capacity and reliable financial planning.
How the role differs: Analyzing volumes, reimbursement assumptions and operating costs with specialist owners.
Evaluated skills: Definition discipline, sensitive-data handling and scenario analysis.

Q1. How do you forecast revenue?
Validate service volume and payer assumptions with the relevant specialists and distinguish charges, expected revenue and collection timing.
Q2. How do you assess a new service?
Model incremental costs, realistic capacity and revenue assumptions while recognizing operational and patient-access constraints.
Q3. How do you protect sensitive data?
Use approved access and aggregate outputs where appropriate, sharing only the detail necessary for the analysis.

Prepare: Learn the service model and reimbursement context.
Avoid: Assuming every visit produces the same revenue or resource use.
Smart question: “How are finance assumptions reviewed with clinical and revenue-cycle teams?”

Bank treasury or finance team

Priorities: Liquidity, balance-sheet insight and controlled reporting.
How the role differs: Supporting assigned planning or treasury analysis under defined policies.
Evaluated skills: Scenario discipline, reconciliation and product-specific learning.

Q1. How do you approach rate sensitivity?
Use the approved methodology and balance-sheet assumptions, validate inputs and explain limitations rather than improvise a regulatory conclusion.
Q2. What if liquidity assumptions change?
Update the relevant scenarios, quantify the effect and escalate through the designated treasury review process.
Q3. Are all finance analyst roles regulatory roles?
No; I would clarify the assigned reporting and approval responsibilities and seek training for unfamiliar requirements.

Prepare: Research the actual products and team mandate.
Avoid: Treating corporate FP&A and bank treasury methods as interchangeable.
Smart question: “Which liquidity or balance-sheet methods must the analyst use independently?”

Investment research firm

Priorities: Defensible research, valuation discipline and clear investment reasoning.
How the role differs: Analyzing companies or securities within the firm's research and compliance framework.
Evaluated skills: Source evaluation, valuation and thesis testing.

Q1. How do you form an investment thesis?
Connect business economics and valuation to explicit expectations, identify contrary evidence and state what would invalidate the thesis.
Q2. What if valuation methods disagree?
Investigate assumptions, comparability and sensitivities rather than average outputs mechanically.
Q3. How do you handle potentially nonpublic information?
Stop inappropriate use and consult the designated compliance process rather than incorporate it into research.

Prepare: Review the strategy, asset class and permitted research process.
Avoid: Presenting a price target without assumptions or risk discussion.
Smart question: “How are research conclusions challenged and reviewed?”

Nonprofit organization

Priorities: Program sustainability, restricted-resource planning and stewardship.
How the role differs: Supporting budgets, grants and resource decisions with program leaders.
Evaluated skills: Funding analysis, scenario planning and allocation clarity.

Q1. How do you forecast grant-funded activity?
Align the plan with award terms, timing and allowable spending, distinguishing committed funding from uncertain renewals.
Q2. How do you compare program costs?
Use consistent definitions and transparent allocations while acknowledging service outcomes that financial measures alone cannot capture.
Q3. What if funding falls short?
Model feasible options with program owners, show operational implications and respect restrictions on available resources.

Prepare: Review funding sources and program commitments.
Avoid: Treating restricted resources as freely available cash.
Smart question: “How are program priorities balanced against funding uncertainty?”

Consulting and advisory firm

Priorities: Decision-focused client work, defensible methods and clear deliverables.
How the role differs: Building analyses and recommendations within an engagement's scope.
Evaluated skills: Problem framing, client communication and reviewable modeling.

Q1. How do you begin an ambiguous case?
Clarify the decision, scope and success criteria, then identify the minimum evidence needed to evaluate credible alternatives.
Q2. What if client data is unreliable?
Document quality gaps, reconcile key totals and agree limitations or additional evidence with the engagement lead.
Q3. How do you manage scope changes?
Explain the effect on timing and deliverables and obtain agreement through the engagement process before expanding the work.

Prepare: Learn the practice area and typical engagement format.
Avoid: Promising a precise answer without sufficient evidence.
Smart question: “How are analytical quality and client scope managed together?”

08Compensation and salary negotiation

Financial analyst compensation depends on location, experience, industry and whether the position focuses on operating planning, treasury, investments or another specialty. Modeling complexity, decision ownership and stakeholder responsibilities make superficially similar titles difficult to compare.

Research current local postings and credible market information for roles with comparable scope. Distinguish entry-level analysis from senior business partnership and permanent employment from project-based assignments. Treat advertised incentives as conditional unless the terms establish otherwise.

Discuss expectations using a researched market range and the verified responsibilities. Clarify planning-cycle hours, reporting deadlines, travel, hybrid requirements and responsibility for major systems or transaction projects before assessing an offer.

Evaluate base pay alongside incentives, benefits, retirement contributions, paid leave and professional development. Ask how performance is measured, how variable compensation is determined and when reviews occur. Read repayment and restrictive provisions carefully and seek qualified advice when appropriate.

09Final interview preparation checklist

One week before

Research the employer's business model and analyst mandate
Prepare forecasting, variance and stakeholder examples
Review relevant modeling and cash-flow methods
Research comparable compensation and planning-cycle expectations

One day before

Confirm assessment format and permitted tools
Practice a model and a concise recommendation
Remove confidential information from examples
Prepare questions about data, assumptions and review

During the interview

Clarify the decision, period and definitions
Separate evidence from assumptions
Explain checks and sensitivity before claiming precision
State your contribution and experience honestly

Immediately afterward

Send a concise thank-you message
Share only appropriate promised materials
Note analytical topics needing further practice
Assess the fit between responsibilities, data support and review capacity

Five essential takeaways

Start with the decision and choose analysis that serves it.
Reconcile data and define metrics before interpreting results.
Make assumptions visible and test important uncertainties.
Communicate a recommendation, its evidence and the conditions that could change it.
Tailor preparation to the actual analytical discipline and employer model.

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