Will AI Replace Finance Jobs in Peru? Here’s What to Do in 2025

Peru's finance sector should pay attention to AI in 2025 because global research shows the technology is already reshaping finance - from faster reconciliations and real‑time forecasting to new credit models that draw on alternative data - so banks and fintechs in Lima and beyond can either lead or be disrupted.

Stanford's 2025 AI Index documents AI moving into everyday finance and falling costs that widen access, while the World Economic Forum highlights how emerging‑market fintech is using AI to build financial identities and offer services like instant remittance‑triggered micro‑loans; those same patterns can help Peruvian lenders reach the unbanked or expose them to new cyber and governance risks.

Practical steps now - upskilling teams, piloting explainable models, and partnering with trusted vendors - will decide whether AI becomes a competitive advantage for BCP, ASBANC members and local fintechs or merely a compliance headache.

“AI and ML free accounting teams from manual tasks and support finance's effort to become value creators.” - Kainos Group Head of Finance Matt McManus

How AI is already changing finance work in Peru is visible in two linked shifts: better data capture and faster cash conversion. The IMF notes that Peru's move to e‑invoicing lifted reported sales, purchases and declared value by over 5%, demonstrating how digital invoices change the tax and reporting baseline (IMF analysis of Peru's e‑invoicing); at the same time, global AP deployments show what intelligent automation actually does on the ground - AI‑powered invoice processing can free up real working capital (one case cleared more than $50M), shorten monthly closes by about 1.5 days and handle 360,000+ documents without constant template fixes (AI‑powered invoice processing case study by USEReady).

Vendors and practitioners now talk about near‑touchless goals (95% touchless processing) and agentic reconciliation that matches payments to invoices automatically, reducing manual exceptions and fraud risk (Tungsten Automation AP automation and touchless processing).

For Peruvian banks and fintechs this means cleaner tax reporting, faster treasury visibility and the chance to turn reconciliations into timely analysis - provided governance, data quality and staged pilots keep pace with the technology.

Which finance jobs in Peru are most exposed to AI? Start with the routine, cognitive-heavy tasks that HLB highlights as ripe for automation - preparing and aggregating reports, transaction classification, invoicing and month‑end financial statement preparation - because advanced NLP and data‑wrangling tools can now pull unstructured inputs into real‑time reports and flag anomalies faster than manual review (HLB Peru report: AI trends to watch in 2024 and implications for finance automation).

Local evidence and global studies make the scale clear: the IMF flags that roughly 30% of Peru's workforce is highly exposed to AI, and the World Bank finds 26–38% of LAC jobs exposed to generative AI (while only 2–5% face full automation), meaning many finance roles will be augmented rather than erased - think AP clerks, expense auditors, routine analysts and reconciliation specialists seeing their busy work disappear so they can focus on interpretation and exceptions (IMF report: Peru labor force exposure to AI (2024), World Bank report: Generative AI and jobs in Latin America and the Caribbean).

Practical first moves for those roles include learning to supervise models, validate outputs and use tools like AI expense auditing to catch policy breaches faster than manual review (AppZen AI expense auditing for finance teams in Peru), transforming

stacks of invoices and midnight closes

into high‑value analysis.

As AI moves from pilot to production in Lima and beyond, fresh finance roles are appearing that match technical depth with industry know‑how: think machine‑learning engineers, NLP and generative‑AI specialists, data scientists and AI product managers who design lending models, credit scoring and customer automation for Peruvian banks (see the list of in‑demand AI careers in 2025).

These specialists won't work alone - governance and compliance leads that can translate regulation into model controls will be essential as Peru scales AI adoption, a shift the IMF links to the country's growing ability to embrace digitalization and boost productivity.

On the operational side, new analyst jobs will blend prompt literacy with domain judgment - for example, treasury analysts using a currency‑by‑currency risk score that feeds hedging recommendations, or expense teams using AI expense auditing to spot fraud and policy breaches faster than manual review.

The result is a mix of traditional finance roles upgraded with AI skills and entirely new hybrid positions that turn “stacks of invoices” into real‑time insight, creating measurable value if firms invest in hiring, training and thoughtful deployment.

Most in‑demand AI careers of 2025, IMF analysis of Peru's AI readiness, AI expense auditing by AppZen.

Peru's finance professionals should prioritize a pragmatic blend of tool‑selection, integration, governance and storytelling skills in 2025: start by learning how to evaluate AI fit and connectors so a new platform actually reduces work instead of creating more (define priorities, check ERP/BI integration and run a tight pilot as advised by Prezent), then build governance chops - role‑based access, explainable models and continuous monitoring - to satisfy auditors and regulators; sharpen FP&A and forecasting techniques so AI outputs become decision‑ready (move from annual budgets to AI‑driven scenarios as digital FP&A guidance recommends); and develop prompt literacy plus crisp data storytelling so forecasts and anomalies become board‑ready narratives, not raw tables.

Add basic model‑supervision and anomaly detection skills so unusual transactions are caught early, and a pragmatic vendor checklist to choose tools tailored to accounts payable, cash forecasting or expense audit needs.

Remember Prezent's warning that

“every hour spent formatting spreadsheets…is an hour lost”

- the point is to reclaim those hours for analysis and strategic action, not more tooling headaches (How to choose the right AI tools for finance, Top AI finance tools comparison for finance teams).

Break the 90 days into Rippling's three tight phases but tailor each step to Peru's finance realities: days 1–30 are for listening and mapping - embed with treasury, AP and tax teams, document cash‑flow rhythms, and flag where e‑invoicing or legacy ERPs create bottlenecks; days 31–60 are for targeted fixes - consolidate unused software seats, launch a controlled automation pilot (start with AP and expense auditing - pilot tools like AppZen to catch policy breaches), and tighten forecasting assumptions; days 61–90 are for showcasing measurable wins and scaling - share metrics that matter to boards (hours reclaimed, faster closes, improved working capital), formalize vendor governance and model‑supervision routines, and publish a 12‑month AI adoption roadmap with clear KPIs.

Use Rippling's 90‑day playbook as a practical cadence (Rippling 90-day finance onboarding roadmap), aim for automation benchmarks seen in global deployments, and learn from vendor case studies on invoice automation to set realistic targets (AppZen AI expense auditing pilot tool, Esker 2025 finance automation and AI guide).

The payoff is vivid: reclaiming hours once lost to “stacks of invoices and midnight closes” and turning them into morning briefings that guide strategic decisions.

“Esker's AI-based recognition has significantly reduced manual work. We can now focus on improving other factors within our department rather than handling manual work. The interface is very user friendly and easy for new employees to use right off the bat, which has helped us save time in new hire trainings.” - Wynona Ho | Accounts Payable Manager

Employers in Peru should treat AI adoption as a combined hiring, governance and tech‑strategy problem: start by hiring or upskilling governance and compliance leads who can map models against Law 31814's risk categories (credit scoring and hiring tools are explicitly “high‑risk”), run lifecycle risk assessments, and embed mandatory human‑oversight and transparency controls so automated decisions remain explainable to auditors and customers (Peru Law 31814 AI regulation overview).

Build clear data‑governance rules - strict minimization, consent checks and controls for cross‑border transfers - and a vendor checklist that demands documentation, incident‑reporting pathways and regular audits (security incidents must be reported to Peru's digital security authority).

Use regulatory sandboxes and public‑private collaboration to pilot credit or AP models safely, align roles with the Secretariat of Government and Digital Transformation's guidance, and budget for compliance costs such as model documentation and audits; the regional policy context shows risk‑based frameworks and multistakeholder governance are the norm across LatAm, so early alignment becomes a competitive advantage (AI governance in Latin America: overview and emerging trends).

For practical help selecting partners and case studies, consult local vendor roundups that highlight Peru‑relevant implementations and sandbox learnings (AI in Peru finance: vendor case studies and sandbox learnings), then measure success by reduced exceptions, documented controls, and faster, auditable decision cycles.

Peru's promise and peril with AI in finance hinge on a tightening privacy rulebook: the Personal Data Protection Law (PDPL) remains the baseline, but the New Regulation (Supreme Decree 016‑2024‑JUS) that entered force on March 30, 2025 adds concrete obligations that matter to banks, fintechs and payroll systems - mandatory security‑incident notifications, stricter definitions for sensitive data and new triggers for appointing a Personal Data Officer (DPO) at larger processors (with staggered grace periods running to November 2028 for micro firms).

Practical consequences are immediate: prior, informed consent is still central for personal and sensitive data, cross‑border transfers must meet adequacy or contract standards, and large breaches must be reported to the NDPA within tight timeframes (48 hours for major incidents), or face steep sanctions (fines scale into the hundreds of thousands of soles or a percentage of annual revenue).

For finance teams, the lesson is clear and vivid - automating credit scoring or expense audits without documented consent, lifecycle controls and a vendor contract is not just a governance gap but a regulatory exposure that can cost real money and damage customer trust; start with data mapping, breach playbooks and vendor defensibility before scaling AI pilots.

Peru data protection laws and Peru's PDPL overview explain the core obligations.

Ready-to-use next steps for Peru's finance teams: start by picking a practical, paced course to build prompt literacy and model supervision - Nucamp's Nucamp AI Essentials for Work (15-week AI Essentials for Work bootcamp) is a 15-week, workforce-focused option that teaches prompt writing and real-world AI workflows - then layer in local, instructor-led practice with Peru-friendly providers like NobleProg AI training in Peru (Instructor-led AI training) to test pilots on-site; for bite-size, finance-specific modules consider online options such as Nicolas Boucher AI for Finance courses (ChatGPT & Copilot) to accelerate FP&A and expense‑audit skills.

Prioritize a 90‑day pilot (AP or expense auditing), demand vendor model documentation, and map data‑flows to meet Peru's PDPL rules before scaling - this mix of a structured bootcamp, local classroom practice and short, role-focused courses turns “stacks of invoices” into morning briefings that inform decisions, not just clean up work.

“Nicolas may be the best expert in the world on how finance leaders and professionals can be more effective and efficient using AI and large language models.” - Jeff Epstein, Partner at Bessemer Venture Partners