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

AI matters for financial-services jobs in Des Moines because banks, insurers, and credit unions are using machine learning and generative AI to discover research, automate routine tasks, personalize advice, and detect fraud - changes that shift value away from repetitive roles toward oversight and complex judgment (Impact of AI on financial services (2025 report)).

Locally, generative AI for back-office automation can streamline reconciliations and reduce processing times at Des Moines credit unions, meaning tellers and data-entry clerks face the highest near-term exposure while employers demand prompt-writing and AI-literacy.

Upskilling in practical workplace AI - how to use tools, craft prompts, and apply AI to business workflows - helps workers pivot; see the Nucamp AI Essentials for Work syllabus (15-week bootcamp) and our guide to AI cost-and-efficiency use cases for Des Moines financial services for concrete next steps.

“Writing code has become much faster with AI, but now the value is in testing and understanding it and seeing if it works for the business.”

The top-five at-risk list was built by cross-referencing national occupation-risk analyses with financial-services–specific research and Des Moines use cases: national risk signals from a jobs-risk roundup (Jobs Most at Risk of AI Automation - 2024 analysis) guided initial candidate roles (Jobs Most at Risk of AI Automation - 2024 analysis), Congressional analysis of AI/ML in finance clarified how models reshape underwriting, trading, and back-office work (Congressional Research Service report on AI and Machine Learning in Financial Services (2024)), and local Nucamp research on generative-AI back-office pilots in Des Moines validated which routine tasks employers are already automating (Generative AI for back-office automation in Des Moines - Nucamp research).

Selection criteria prioritized (1) task routineness and repeatability, (2) proven AI feasibility in finance, and (3) local prevalence; the practical takeaway: roles dominated by reconciliations, standardized forms, and predictable decision rules surfaced as highest near-term exposure in Des Moines.

“One in four US workers will see more AI/technology in their jobs.” - Anu Madgavkar (McKinsey), cited in reporting on AI's workforce impact

Bank tellers and routine customer-service representatives in Des Moines are most exposed because many core transactions - cash handling, deposits, and standard inquiries - are now handled by smarter ATMs and “interactive tellers” that let a single remote employee serve multiple communities, reducing the need for on-site staff; national analysis shows teller employment is projected to decline (about 8% over the next decade) even as roles shift toward relationship work (AEI analysis of ATMs' impact on bank tellers).

Iowa pilots show the effect: PCSB's interactive tellers produced 1,700 extra transactions in four months, average 43.13 transactions per day vs. 27.56 for traditional tellers, and several local positions moved into a Clarinda call center - preserving rural access but centralizing labor (Des Moines Register report on interactive tellers in Iowa).

The practical takeaway: Des Moines workers who pivot from routine processing to advisory, sales, fraud oversight, and basic AI literacy will be the ones who retain and grow value as branches shrink and workflows centralize.

"It's a machine, but it's not, because it's run by people." - James Johnson, PCSB

Data-entry and back-office processing clerks in Des Moines face clear, near-term exposure because the exact tasks they perform - OCR-driven document extraction, invoice matching, reconciliation, and repetitive data migration - are the core use cases for Robotic Process Automation; organizations that prioritize a use-case approach see the fastest returns (Top 100 RPA use cases with real-life examples and ROI).

Practical next steps for local credit unions, community banks, and insurers: map high-volume, rule-based processes (invoices, policy intake, reconciliations), pilot OCR+RPA on a single workflow, measure error and time reduction, then scale while training staff in bot oversight and exception handling - Oracle and industry guides recommend this phased approach for finance teams to preserve oversight roles and shift toward analysis and fraud review (Nucamp AI Essentials for Work syllabus: Generative AI for back-office automation).

The scale is striking: real-world cases show manual entry collapsing from hundreds of hours a month to effectively single-digit annual hours, so the practical

so what?

is simple - automation can eliminate the repetitive baseline of the job, making upskilling in RPA/OCR and exception-management the fastest path to stable employment.

Insurance claims processors in Des Moines are seeing the same forces reshaping claims work nationwide: AI that extracts and prioritizes data, speeds decisioning, and flags exceptions is moving routine intake and document work from people to models, which shifts the human role toward exception handling, fraud review, and governance.

Travelers' New Business Submission Automation - recognized with a 2025 CIO 100 Award - illustrates the speed gains insurers chase, cutting registration time from about two hours to two minutes and freeing staff for higher-value tasks (Travelers New Business Submission Automation CIO 100 Award); likewise, deep-learning analysis of aerial imagery lets carriers advance payments on many wildfire total-loss claims before an in-person inspection, shortening recovery timelines and changing where human judgment is needed (Travelers data-driven AI insurance technology overview).

Practical AI in claims combines OCR, predictive fraud detection, and prescriptive automation - so Des Moines processors should prioritize AI oversight, claims triage, and skills in model validation to remain indispensable as routine processing is automated (Aon article on AI in claims management).

“The strength and reliability of AI governance impacts ROI analysis and ultimately an insurer's appetite to integrate AI into its claims processes.” - Margaret Leathers, Aon

Underwriting assistants and junior underwriters in Des Moines must shift from manual file-pulling to supervising predictive analytics: firms are building AI that captures veteran decision patterns so newcomers can tap “decades of accumulated wisdom” at the point of decision (Wipfli insurance industry outlook: People vs AI), while generative tools that digest dense guidance allow a less-experienced underwriter to answer complex broker questions in seconds instead of hours - Allianz's BRIAN handled nearly 3,000 pilot questions and links answers back to the exact section of 600–800 page manuals for on-demand context (Allianz BRIAN underwriting assistant overview).

Traditional predictive models already show concrete wins - Swiss Re cites >95% accuracy in targeted non-smoker detection - so the practical takeaway for Des Moines hires is clear: learn model validation, explainability, and exception triage (not rote data entry), pair AI outputs with local underwriting judgment, and use mentorship-plus-AI programs to preserve institutional knowledge as seniors retire (Swiss Re predictive underwriting non-smoker accuracy).

“What sets BRIAN apart is its dual functionality.” - Benjamin Blackie, Allianz

Accounting, bookkeeping, and payroll clerks in Des Moines face rapid task-shifting as SaaS tools and robotic accounting automation take over bank-feed reconciliations, invoice matching, and routine payroll calculations; practical local pivots are to become the person who configures, audits, and explains those automations rather than doing line-by-line entry.

Popular platforms now bundle AI agents and OCR so a single clerk can manage far more volume - Intuit's QuickBooks AI agent, for example, reportedly saves users

more than ten hours every month

by creating invoices, categorizing expenses, and reconciling transactions (QuickBooks AI accounting automation features).

Payroll specialists should map payroll exceptions and tax edge cases while learning tools like Gusto and ADP that automate filings and time tracking (Payroll automation tools overview), and bookkeepers can pilot RPA/OCR for receipts and reconciliations using the 13-tool playbook for robotic accounting automation to cut errors and reclaim advisory time (Otio 13-tool robotic accounting automation playbook).

The practical “so what?”: mastering a small set of automation tools plus exception-triage and report storytelling turns an at-risk role into a higher-value internal consultant for Des Moines firms moving to cloud-first finance.

Des Moines financial-services workers should move from worry to action: first, connect with Iowa Workforce Development to access no‑cost upskilling, workshops, and employer-funded training - start at the Programs That Help Iowans Work page to explore WIOA services, job‑training grants, and the Last‑Dollar Scholarship that can cover tuition gaps (Iowa Workforce Development - Programs That Help Iowans Work); second, register for practical workshops at your local IowaWORKS center (resume coaching, mock interviews, certificate training, and virtual presentations on career planning and financial literacy) to translate existing banking or claims experience into AI‑oversight and exception‑management roles (IowaWORKS workshops and resources); finally, consider a focused course that teaches prompt‑writing, AI tool use, and job‑based AI skills - Nucamp's 15‑week AI Essentials for Work bootcamp maps directly to the oversight and productivity skills employers in Des Moines are hiring for (AI Essentials for Work syllabus).

These steps - use local grants, finish a targeted workshop, and complete a practical AI bootcamp - turn at‑risk tasks into oversight, governance, and advisory opportunities employers value now.