Top 5 Jobs in Financial Services That Are Most at Risk from AI in Virginia Beach - And How to Adapt

Virginia Beach's finance workforce is squarely in the path of a fast-moving AI wave: industry reports show banks are reallocating IT budgets to GenAI and embedding automation into document-heavy workflows, fraud detection, and customer service - tasks that power many entry-level analyst and operations roles in the region.

Firms are already using AI to parse tax returns, pre-fill borrower profiles and draft loan memos (nCino's 2025 trends), while strategy papers from EY stress GenAI's role in personalized services and risk management; together these shifts mean routine reconciliation, data entry, and basic underwriting work can be automated or streamlined, putting pressure on local job descriptions and hiring patterns.

For Virginia Beach professionals and employers, the practical response is rapid reskilling - learning AI literacy, prompt-writing, and workflow design - and programs like Nucamp's AI Essentials for Work bootcamp (AI at Work: Foundations, Writing AI Prompts, Job Based Practical AI Skills) can teach those job-ready skills; see EY's analysis of industry change and nCino's banking use cases for why adaptation matters now.

“This year it's all about the customer,” said Kate Claassen, Head of Global Internet Investment Banking at Morgan Stanley.

Methodology: the list of top five Virginia Beach roles at risk was built from close readings of local and national job text and market snapshots - job descriptions that explicitly call out high-risk automation experience or ask for data‑cleansing/ERP skills were treated as high‑risk signals, while remote and local finance data governance roles that emphasize rules, validation and automation tools flagged mid‑level exposure.

A Zippia market snapshot of Finance Manager and analyst openings in the Virginia Beach area provided counts, salary bands and industry mix used to weight local prevalence; these three sources were cross‑referenced with Nucamp's Virginia Beach AI use‑case guides to map practical reskilling routes - together they turned subtle wording in listings into clear risk indicators, like a blinking amber light on a reconciled ledger demanding attention.

“Automation and AI”

A Zippia market snapshot and local job text readings were also used to identify prevalence and salary bands for weighting risk exposure.

These combined signals - explicit automation/AI requirements in employer postings, data‑governance responsibilities calling for validation and cleansing, and local market prevalence data - formed the basis for ranking Virginia Beach finance roles by near‑term AI exposure and identifying practical reskilling pathways.

Entry-level Financial Analysts in Virginia Beach are squarely at risk because their day-to-day - collecting and cleaning data, running trend and variance analyses, building basic financial models, and churning out recurring reports - is precisely what automation and GenAI are designed to streamline; see a typical entry-level job description for these duties from Gusto.

That doesn't mean the role disappears, but adaptation is urgent: strengthen core finance skills (financial modeling, Excel), add data-tool fluency (Python/R, Tableau or Power BI) and client-ready presentation abilities highlighted by Kaplan, and learn to design or supervise automated workflows so value shifts from “do the spreadsheet” to “interpret the model.” Local employers and candidates should also weigh market realities - the Robert Half salary bands show where hiring pressure concentrates - and use Virginia Beach–specific AI guidance to retool job tasks into higher‑value work rather than routine processing.

A vivid test: an analyst who pairs solid forecasting technique with basic scripting and clear storytelling goes from a stacked binder of monthly reports to being the person senior leaders call for actionable insight - exactly the shift that preserves and uplifts entry-level careers.

Accounts payable/accounts receivable specialists in Virginia Beach are squarely in the crosshairs because the job is built on high‑volume, rules‑based work - posting invoices, reconciling ledgers, chasing aging balances and keeping the cash wheel turning - which makes it a prime target for automation and smart invoicing tools; see the duties listed in standard AR job descriptions like the LinkedIn accounts receivable guide and the Bill.com automated invoicing templates that hiring sites publish.

That doesn't mean the role vanishes, but it does mean the local playbook must shift: automate routine posting and reminders (software like Bill.com-style automation speeds collections and turns a mailbox of 300 unpaid invoices into a short exceptions list), double down on relationship and negotiation skills for strained accounts, and add ERP/Excel and analytics fluency so specialists become exception managers and cash‑flow interpreters rather than clerks.

Virginia Beach employers who invest in targeted reskilling and local AI use‑cases - see the Nucamp AI Essentials for Work bootcamp for practical AI skills for the workplace - will keep skilled AP/AR staff vital by moving work from keystrokes to judgment and customer strategy.

Junior Treasury Analysts in Virginia Beach face real near‑term exposure because the core daily work - monitoring cash positions, running bank reconciliations, generating regular treasury reports and maintaining documentation - is precisely what treasury management systems and automation can standardize and produce, as outlined in the Randstad treasury analyst profile and Bill.com's role overview; automation that turns manual reconciliations into a short exceptions list can quickly shrink the hours spent on rote processing.

The practical response is to shift from data collection to decision support: deepen cash‑forecasting and financial‑modeling chops (advanced Excel, scenario analysis), learn TMS/ERP workflows and reporting automation, and strengthen bank relationship and compliance skills so the role becomes the bridge between automated reports and executive liquidity decisions.

Local candidates should view certification and technical fluency (CTP, TMS familiarity) as leverage, while employers can preserve value by retraining juniors to own exception management and stress‑test scenarios rather than line‑item posting; for Virginia Beach practitioners, pairing a solid cash‑management toolkit with an ability to synthesize one concise liquidity recommendation each morning is the clearest way to stay indispensable.

See the Randstad treasury analyst profile, Bill.com treasury role overview, and Nucamp AI Essentials guidance for local AI use‑case recommendations.

“Nothing replaces talking to management to understand how they really think about their business.”

Junior investment research analysts in Virginia Beach are squarely in the crosshairs because much of their daily work - SWOTs, earnings-call summaries, extracting metrics from CIMs and stitching together comparable-company analysis - is exactly what advanced LLMs and specialist platforms now do faster and at scale; the CFA Institute's controlled tests found AI models producing institutional‑grade SWOTs (with top models taking 10–15 minutes) and V7's analysis shows a single model can cut CIM data‑extraction from days to under an hour, so the risk is real for roles built on repetitive synthesis.

The clear adaptation pathway for local analysts is hybridization: build prompt libraries and model‑selection skills, learn to evaluate reasoning‑focused models rather than rely on generic summaries, and shift time saved into idea generation, risk interpretation and original sourcing - especially the human conversations and context that machines miss.

Employers and hires in Virginia should prioritize practical AI literacy, structured prompt workflows, and client‑facing storytelling so junior analysts move from “data assembler” to “insight generator”; a vivid test: the analyst who can turn an AI's instant draft into one crisp, defensible investment thesis each morning becomes the person portfolio managers call, not the person replaced.

For deeper context, see the CFA Institute analysis on AI vs. analysts and V7's study on AI in financial analysis.

Data Entry and Financial Operations Associates in Virginia Beach are among the most exposed because their jobs - invoice extraction, transaction posting, bank reconciliations and routine compliance checks - are exactly the kinds of resource‑intensive, rules‑based work that automation and OCR/RPA tools swallow whole; industry trackers note data entry clerks are high‑risk and recommend transitioning into data management or analytics by learning Excel, SQL and Python (VKTR analysis of jobs most at risk from AI).

That doesn't mean the role disappears overnight: local employers can preserve value by redesigning tasks so people own exceptions, internal controls and vendor relationships while machines handle volume - FinOptimal's automation research shows automated processes can be tens of times faster and cut manual invoice work dramatically, freeing human time for oversight and story‑telling (FinOptimal accounting automation adoption statistics).

Practical pathways for Virginia Beach practitioners include hands‑on RPA and system‑integration training, formalizing exception‑handling playbooks, adding basic analytics and prompt workflows, and packaging measurable wins (reduced error rates, faster closes) to prove impact; picture a daily inbox that used to be 500 posted rows and now surfaces a five‑item exceptions list - those five items are where judgment and career growth live.

“Resource-intensive, repetitive tasks, such as data entry and transaction processing, are well suited to automation and AI.”

Practical next steps for Virginia Beach finance workers and employers start with focused, measurable skill moves: upskill in Power BI to turn messy ledgers into decision‑ready dashboards (see Coursera's Power BI career matrix and DataCamp's Power BI developer roadmap for clear learning milestones), pair those visualization skills with basic SQL/Python for data prep, and lock in AI literacy and prompt‑writing so automated workflows surface exceptions - not whole job piles - for human judgment; Nucamp's AI Essentials for Work (15-week bootcamp teaching workplace AI skills and prompt techniques for non-technical professionals) teaches those workplace AI skills and prompt techniques for non‑technical professionals.

Employers should fund short, role‑specific tracks (a 3‑month Power BI sprint, on‑the‑job prompt libraries, and exception‑handling playbooks) and measure impact with metrics like time‑to‑close and error rate - small wins that add up: a daily inbox that used to be 500 posted rows and now surfaces a five‑item exceptions list is where career value lives.

Start with a compact learning plan, align it to a Power BI job matrix, and use small employer pilots to prove out reskilling before scaling across teams.