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

Orem's bank tellers, underwriters and back‑office teams should pay attention: state laws, rapid fintech growth, and real operational risks mean AI won't be a distant problem but a day‑to‑day reality.

Utah's 2024 Artificial Intelligence Policy Act (SB 149) already forces more transparency and accountability around generative AI in customer service and marketing - a change that matters for local lenders and credit unions (Utah AI Policy Act (SB 149) overview).

Regulators and industry experts flag the same core hazards - data quality, testing and trust, compliance, user error and adversarial attacks - that can turn AI shortcuts into legal and financial headaches for frontline staff (AI in financial services risks and compliance considerations).

That's especially urgent in Utah's booming fintech ecosystem, where wages are double the state average and the sector drives billions in economic activity - so upskilling in practical AI use, governance and prompt craft is a business imperative, not just tech curiosity (Utah fintech growth and economic impact).

“We have to involve industry and allow them inside the university to help us actually co-create students.”

Methodology - selections were grounded in local labor signals and employer listings: primary scans of university and employer career pages identified the roles showing up most often in Utah hiring pipelines, while state labor data helped confirm where volume and vulnerability overlap.

Specifically, University of Utah financial services postings (job titles like Associate Accountant and Clerk with posting numbers such as PRN42317B) supplied concrete role names and departments (University of Utah financial services job openings), Mountain America's careers portal revealed a heavy presence of teller and member‑service listings (the board shows “displaying 1 to 20 of 51 matching jobs”), which flags front‑line exposure (Mountain America careers job listings), and the Utah Department of Workforce Services provided statewide hiring context and the 35,000‑job searchable board used to gauge demand and mobility (Utah Department of Workforce Services jobs board).

The top five roles were chosen by frequency in local postings, clear task overlap with common AI automations, and cross‑checking against state employment trends - so the list reflects both what employers are hiring today and which everyday tasks (think repetitive data entry or standardized responses) are most likely to be automated.

Sources and what they provided:
• University of Utah job openings - Concrete role titles and posting numbers (e.g., Associate Accountant, Clerk).
• Mountain America / MACU careers - Count and variety of teller/member service listings (51 matching jobs).
• Utah Department of Workforce Services - Statewide job volume and labor-market context (35,000+ jobs searchable).

For front‑line roles like the Utah First Credit Union Personal Banker and WaFd Bank Personal Banker, the story is familiar: routine cash‑handling and scripted service tasks are the most exposed as banks shift to omnichannel, automated servicing, and branch footprints shrink - a trend analysts now link to an expected decline in teller headcount (estimates range from single‑digit to low‑double‑digit reductions over the coming years) highlighted in industry coverage such as ATMs Didn't Reduce Tellers, But Today's Tech Will.

In Utah that transition is visible in regulatory tracking and new digital entrants - the Department of Financial Institutions notes a wave of digital‑first activity, including new banks based in the state (Utah Department of Financial Institutions news) - and community banks are stressing the human side of service as a competitive edge (community banks vs. national banks in Utah).

Practical takeaway: personal bankers who convert routine interactions into advisory conversations, master digital cash‑management tools, and learn simple AI promptcraft will stay valuable - think less teller line, more trusted local financial coach, with one vivid shift already common: drive‑through and mobile channels replacing walk‑in traffic overnight for many branches.

“Banking has changed irrevocably as a result of the pandemic. The pivot to digital has been supercharged.”

Entry‑level financial analysts - think E‑Trade Financial Services Representatives or BCI Orion Associate Investment Strategists - face disproportionate exposure because their day‑to‑day is heavy on repeatable work: data entry, cleaning, report generation and routine modeling, the very tasks AI is built to automate; some estimates even suggest that two‑thirds of entry‑level finance jobs are at risk (Datarails analysis on AI's impact on entry-level finance jobs).

Research shows junior analysts often spend the bulk of their time on scripting and data prep (SQL/Python), and modern LLM pipelines can slash data‑extraction and reconciliation from days to under an hour - a vivid productivity gain that also shrinks learning‑by‑doing opportunities if roles don't evolve (V7 Labs exploration of whether AI will replace financial analysts).

The upside in Orem: AI can free analysts to become strategic partners if they upskill in SQL, visualization (Tableau/Power BI), cloud basics and AI literacy, and learn to turn model outputs into clear narratives for advisors and clients; local teams can start by testing small automations and measuring time saved (How AI is helping financial services companies in Orem to cut costs and improve efficiency).

“It's not just embracing and implementing technology. It's, ‘How are we going to use technology as accounting professionals to help translate trends and opportunities in our clients' businesses?' and ‘How is technology going to allow me to better deliver to those who consume my service?'”

Loan officers and underwriters - from local loan processing teams to private‑client bankers (roles like those at JPMorgan Chase) - should be squarely focused on how AI is reshaping credit decisioning in Utah: AI‑powered credit scoring and automated underwriting can push routine file reviews from days into minutes, let lenders safely expand to underserved borrowers, and triage work so experienced underwriters concentrate on exceptions and relationship decisions rather than form‑filling.

Regional adopters already report the ability to auto‑decision a large share of consumer applications and to use alternative data (banking transactions, rent/utilities, etc.) to build more predictive borrower profiles (AI-powered credit scoring for regional banks and alternative-data underwriting), while vendor solutions are shifting AI from generic automation to workflow‑level plays - parsing tax returns, prioritizing queues, and drafting loan memos so teams can scale without sacrificing oversight (AI applied to high-friction lending workflows and automated loan memo drafting).

The upshot for Orem lenders: invest in explainable models, human‑in‑the‑loop controls, and small pilots that measure time‑saved and downstream risk - otherwise the productivity gains will arrive without the governance needed to keep decisions fair and defensible.

“At a time when delinquencies are reaching the highest levels we have seen in recent history, the need for a credit score that gives deeper insights into a member's ability to pay back is critical.”

Settlement specialists and back‑office teams - exemplified by Goldman Sachs' Salt Lake City listings - sit at a crossroads in Utah's financial ecosystem: the role still requires human judgment for escalations, client coordination and control‑remediation even as routine reconciliations and exception triage are prime targets for automation.

The Goldman Sachs VP, Settlements posting highlights duties that matter locally: escalating and summarizing control failures, reviewing high‑notional payments and multi‑currency cashflows, coaching junior staff, and identifying process and controls improvements while working with engineering to prototype fixes (Goldman Sachs Vice President Settlements job listing in Salt Lake City).

That mix - deep product knowledge (swaps/CFDs), transmission protocols (SWIFT/IBAN/Fedwire) and a knack for tooling - means Utah teams can protect their roles by mastering operations tools, data analysis and small automation pilots that cut manual load without losing oversight; for practical ideas on what to automate first, see how automation frees Orem teams to focus on exceptions and client work (Automation of manual tasks for Orem financial services teams).

Financial counseling representatives - exemplified by R1's U.S. Financial Counselors - are frontline problem‑solvers for uninsured and under‑insured patients, offering calm, compassionate support and helping people develop realistic medical‑debt plans while building community trust (R1 careers: Financial Counselors).

At the same time, R1's announced acquisition signals fresh investment in “intelligent automation and the use of AI in revenue management,” a shift that heightens both productivity upside and governance questions for local revenue‑cycle teams in Orem (R1 acquisition press release).

Health‑care AI research also flags concrete risks - bias, explainability and workflow impacts - that deserve attention as systems start auto‑triaging eligibility checks and payment estimations (review of AI benefits and risks in health care).

The practical takeaway for Utah revenue teams: protect the human judgment and relationship skills R1 celebrates (peer mentorship, eLearning and career paths) so automation frees time for the hardest, most empathetic conversations rather than replacing them - the role's “no two days are ever the same” mix of triage and empathy makes that tradeoff unmistakable.

“At R1, no two days are ever the same and that's the thing I enjoy most. My job has lots of different aspects to it which means it's never boring, and I love multitasking to get things done!”

Orem finance workers should treat AI as both an urgent risk and a concrete opportunity: global forecasts warn that by the mid‑2030s up to 30% of jobs and 44% of lower‑skilled workers may be exposed to automation, and regional studies show Salt Lake's labor market already has six‑figure exposure to AI disruption - clear signals for nearby Orem (World Economic Forum analysis on AI, job risk, and reskilling; Salt Lake risk analysis).

Practical next steps are straightforward and measurable: map the daily tasks most likely to be automated, run small pilots that measure time‑saved, and pivot toward advisory, exception‑handling and governance work where human judgment is still required.

Upskilling should combine promptcraft and AI literacy with concrete technical skills (SQL, basic automation, cloud/Excel integrations), plus certifications or short bootcamps that teach workplace AI use - one local option is Nucamp's 15‑week AI Essentials for Work course that covers AI foundations, writing effective prompts, and job‑based practical AI skills (AI Essentials for Work syllabus and course details); early‑bird pricing and monthly payment plans make reskilling feasible for individuals and employers.

The goal is clear: automate the repetitive, amplify the human, and move up the value chain before the market forces the change.

“Emerging AI capabilities can provide predictive analytics on which employees may be at most risk of leaving, empowering HR to take a proactive ...”