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

Rochester matters for any conversation about AI and financial services because it's a resilient, one-million-plus metro where community banks, wealth managers and national firms collide - creating a compact lab for automation that can ripple across upstate New York.

Local bankers told the Rochester Business Journal they expect resilience into 2025 even as lending, housing supply, and rate uncertainty reshape branches and back-office work (Rochester-area bankers' outlook from the Rochester Business Journal).

The New York Fed highlights Rochester as the third-largest New York metro with a mixed growth record, meaning AI-driven efficiencies in underwriting, transaction processing, and routine advising could quickly change hiring needs here (New York Fed Rochester regional profile).

For workers and employers ready to pivot, practical training like the AI Essentials for Work bootcamp - covering prompt-writing and job-based AI skills - offers a concrete path to stay valuable as automation reshapes local financial roles (AI Essentials for Work syllabus and course details), turning disruption into opportunity rather than layoffs.

The Top 5 list reflects a practical, impact-first filter: roles that are high-volume, rule-based, document‑heavy or conversational - because those are the functions automation and AI are already eating into fastest.

Sources were chosen for measurable evidence (e.g., process automation that cuts invoice cycles from 15 days to 1.5 days and projections that ~35% of finance tasks could be fully automated by 2025), technology taxonomy (Intelligent Document Automation, conversational AI, AI agents) and vendor/implementation guidance so Rochester employers can act, not just panic; see the Ushur guide to automation for the IDA/agent view, Whatfix's rundown on AI value drivers in U.S. finance for adoption and KPI framing, and the NumberAnalytics stat pack for hard efficiency benchmarks.

Methodology also emphasized practitioner warnings about matching the right tool to the right task - automation for consistency, AI for adaptability - so recommendations spotlight re‑skilling and targeted pilots rather than wholesale cuts, delivering a clear “so what?”: routine throughput can shrink from weeks to hours, making retraining a faster ROI than rehiring.

“Accounting is not just about counting beans; it's about making every bean count.” – William Reed

Bank tellers and retail branch clerks in Rochester face one of the clearest frontlines of AI disruption: routine transactions, check handling and basic account questions are increasingly routed to smarter ATMs, apps and automated assistants, and banks are closing branches as digital channels take root - a shift that has already translated into dramatic job cuts in other markets and could drive a roughly 15% decline in teller roles by 2032 (about 53,000 positions nationally) as customers lean on fintech apps (an estimated 78% preference) rather than the branch line (UXDA banking digitalization job impacts, TROY teller decline and TellerCentral solutions).

The realistic adaptation path in Rochester isn't disappearing - it's shifting: cross-train toward universal-banker and digital‑troubleshooter skills, learn to guide customers through secure e‑services, and join targeted reskilling programs that pair change management, UX-aware customer coaching and basic fraud/cyber hygiene so a former teller becomes the human who solves the one-off problems machines can't (no more standing in line for hours to sign one paper - customers expect instant service).

“Automation has forced us to go back and relook our current processes…”

Back-office operations in Rochester - funds settlement, reconciliation and Know Your Customer (KYC) intake - are prime targets for automation because they're high-volume, rule‑bound and audit‑heavy: vendors and platforms show straight‑through settlement and API-driven fundflows can cut friction, speed funding and reduce manual error, turning batch reconciliations into near‑real‑time processes (Calastone insights on automating settlements in funds, Modulr merchant settlement automation benefits).

KYC and onboarding are equally exposed: agentic orchestration and RPA combine document extraction, identity checks and exception routing so banks can move from manual review queues to analyst‑led exception handling - meaning staff spend less time on paperwork and more on nuanced risk decisions (UiPath banking automation and KYC orchestration for financial services).

The practical “so what?” is immediate: faster settlements improve cash flow and vendor relationships, while automated KYC slashes onboarding bottlenecks - exceptions become the only things humans touch, not stacks of forms - and that shift is already reshaping operational roles across U.S. finance.

“A fast and easy solution for our partners and customers, with all of the licensing and regulations we needed to operate in this space - all via a single API.” - Si Brayshaw, Director of Payments at Motorway

Basic credit analysts and underwriters who handle standardized loans in New York are squarely in AI's sights: intelligent document processing, automated spreading and LLM-driven scoring can turn the old underwriter's stack of PDFs and tax returns into structured inputs in minutes, not days, so routine approvals and knockout decisions are increasingly automated (AI commercial loan underwriting for lenders).

Tools that auto‑spread financials and calculate DSCR, EBITDA and other ratios remove the slow, error‑prone parts of the job, letting banks scale volume without proportional headcount and improving consistency across branches - a material change for New York regional and community lenders competing for fast deal flow (automated spreading impact on credit decisioning).

At the same time, automation shuffles human work toward exceptions, portfolio monitoring and deal structuring; lenders that begin with focused pilots and keep underwriters in the loop preserve judgment while capturing 20–60% productivity gains and 50–75% reductions in time‑to‑decision, turning what used to be a paper shoebox of documents into an auditable, searchable credit package in minutes (credit risk analysis automation for lenders).

The “so what?” is simple: routine scoring is becoming a table‑stake service, and underwriters who master exception review and AI‑augmented analysis will stay essential.

Customer service reps in Rochester are squarely in AI's crosshairs because conversational agents and co‑pilot tools are handling the routine churn - balance checks, card blocks, simple loan questions and password resets - so human teams increasingly handle the 10‑to‑15 percent of calls that need empathy or complex judgment; AI boosts speed and consistency while shrinking repetitive tasks, which matters in a market where agents already juggle 40–60 calls per day.

Generative AI is proving most useful across three fronts - customer‑facing chatbots, real‑time agent support and analytics - and practical steps like deploying post‑call summaries (which can save agents 2–4 minutes per call) or agentic flows for card replacement and fraud alerts cut hold times and improve first‑call resolution (generative AI best practices for banking contact centers).

Platforms that safely run AI agents - tested with human‑in‑the‑loop controls - can contain costs and lift CSAT, but Rochester institutions should pair tech pilots with reskilling so reps move from rote scripts to handling escalations and relationship work (AI agents for banking use cases and benefits), while enterprise examples show broad internal uptake of AI tools across contact centers (Bank of America AI adoption improves employee productivity).

“AI is having a transformative effect on employee efficiency and operational excellence.” - Aditya Bhasin, Bank of America

Routine financial reporting and junior accounting data‑entry in New York - including Rochester - are being hollowed out by software that ingests invoices, reconciles ledgers and spits out audit‑ready summaries, turning a month‑end “shoebox” of receipts into a live dashboard; junior staff who once fed those stacks are especially vulnerable because they're more likely to accept AI outputs at face value unless trained to validate uncertainty flags, according to Stanford's analysis of accounting work (Stanford GSB analysis of AI reshaping accounting jobs).

Firms already report fast upticks in GenAI adoption and see clear wins for invoice processing, reconciliations and real‑time reporting, so the practical play for Rochester employers is to pair automation pilots with data‑skills bootcamps (Excel, SQL, Tableau) and tool‑validation routines so junior hires move from data entry to exception handling, client storytelling and model oversight (Thomson Reuters Institute on how AI will affect accounting jobs, Docyt article on AI impact on entry-level accountants), making displaced hours a launchpad for analytical careers rather than unemployment.

“Embracing AI in finance isn't about replacing people - it's about strategically evolving roles, fostering continuous learning, and ensuring that human insight guides every step of the transformation.”

Rochester's smartest next move is pragmatic: map which local roles are likely to shrink, then invest in targeted reskilling rather than wait for disruptions to force exits - employers can co‑fund training and use county programs like mPower and Monroe on the Job (which reimburses up to $4,000 per employee) to retrain staff into higher‑value work, while RochesterWorks' Incumbent Worker Training grants offer employer reimbursements that make upskilling affordable; see the Monroe County workforce development page and the Rochester Business Journal's roundup on local reskilling options for details.

Pair rapid pilots (start with a handful of high‑volume workflows), run human‑in‑the‑loop validation, and route exceptions to trained analysts; parallel funding models - from employer cost‑share to consolidated grants - keep programs sustainable.

For workers, short, practical courses that teach prompt‑writing, AI at work, and tool validation (for example, Nucamp AI Essentials for Work registration) turn job risk into mobility: the concrete goal is internal mobility, not unemployment, with legal safeguards like WARN and Shared Work available to smooth transitions.

“…businesses also have a huge role to play. They have a lot of opportunities, especially in today's tight labor market, to look within and identify people they can reskill for roles where they're facing shortages. One of the pieces of analysis we did recently suggests that up to 75 percent of upskilling initiatives actually create value for the business. So, yes, the employee benefits, but so does the business, so it's a huge win‑win.” - Tera Allas