The Complete Guide to Using AI as a Finance Professional in Peru in 2025

AI has moved from buzzword to business imperative for Peru's finance professionals in 2025: local banks are already testing models - from BCP's algorithmic FX trading to institution-wide pilots for compliance, fraud detection and data security - showing that technical literacy is now core to everyday finance (see the GARP LATAM session on AI in Peru).

Regional reporting and advisory firms highlight real‑time risk analytics, personalized customer bots and smarter investment models as the biggest value drivers, while commentators warn that Peru's flurry of AI laws risks quantity without depth, so governance matters.

Practical skills pay off: targeted training like the Nucamp AI Essentials for Work 15-week bootcamp and local advisory reads from HLB Peru on AI in investment and risk management can help finance teams turn pilots into reliable, compliant tools that free analysts for higher‑value strategy.

Peru's fintech landscape in 2025 reads like a fast‑maturing playbook where digital adoption, progressive rules and AI are converging: regulators still apply activity‑based rules rather than a single fintech law (see the Chambers' Chambers Fintech 2025 Peru guide - trends and developments), the SBS sandbox remains a live option for pilots, and AI is already embedded across fraud detection, personalised chatbots, alternative credit scoring and regtech automation.

Growth is tangible - EY counted 237 active fintechs while Finnovista's radar puts the ecosystem at 346 firms, and Peruvian adoption metrics (59% of adults with a bank account by end‑2024) show inclusion rising beyond Lima as QR payments and real‑time rails spread into smaller districts (read Fynsa's Fynsa fintech boom in Peru 2025 roundup).

Market forecasts underline the momentum: the Peru fintech market topped roughly USD 1.03 billion in 2024 with double‑digit CAGR projections, while scale stories like Riqra's US$1+ billion in transactions demonstrate how local platforms can grow fast - a vivid reminder that finance teams must pair AI skillsets with strong governance and AML/KYC controls to turn pilots into reliable, compliant tools that truly expand access and cut operational toil.

Peru's regulatory scene for AI in finance in 2025 sits at the intersection of established banking supervision and an active push to make AI-safe: the Superintendencia de Banca, Seguros y AFP (SBS) still enforces the Peruvian Banking Law and detailed governance, AML/KYC and cybersecurity rules, while national action on AI (notably Law No 31814) puts Peru among the roughly 31 jurisdictions with

“hard” AI rules

a concrete nudge that brings concepts like human oversight, risk governance and disclosure into boardroom checklists (see the Chambers Banking Regulation 2025 Peru trends and developments).

Global research shows regulators combine hard laws with soft guidance, and that finance-focused AI guidance is still nascent, so Peruvian firms should expect a mix of prescriptive requirements and high‑level principles: model risk, transparency, third‑party/vendor scrutiny, and supervisory engagement will be recurring themes (summary in CGAP key regulatory developments for AI in finance).

The practical takeaway for finance teams is clear - pilot intelligently but embed governance from day one (board reporting, audit trails, AML/KYC controls and vendor due diligence) so AI moves from experiment to a resilient, compliant tool that reduces toil instead of creating new supervisory headaches.

High-value AI use cases for finance professionals in Peru cluster around liquidity, collections, planning and control - practical wins that turn pilots into measurable savings.

AI-driven cash flow forecasting now offers treasuries far greater accuracy and real‑time scenario testing (think dynamic 13‑week forecasts that update as receipts and market signals change), as explained in J.P. Morgan's piece on AI‑driven forecasting; predictive models also power early‑warning stress tests and Monte Carlo‑style simulations that help teams plan for FX swings and supply‑chain shocks.

Accounts‑receivable AI automates dunning, predicts late payments and speeds cash application so DSO falls and working capital frees up (see the Wise guide on AR AI), while vendor and bank integrations - from Savant's data‑blending forecasting tools to HighRadius and enterprise FP&A platforms - make consolidation and scenario planning faster and less error‑prone.

Closer to audit, AI reconciliation in tools like BlackLine trims month‑end toil and surfaces anomalies before they become control failures. These use cases are practical, measurable and well suited to Peru's growing fintech ecosystem: they reduce grunt work, tighten liquidity and let finance teams focus on strategic decisions that move the business forward.

“The ‘special sauce' of forecasting is the human element: knowing how to interpret the data and anticipate market uncertainty.”

Preparing an AI‑ready finance stack in Peru starts with pragmatic plumbing: codify and centralize transactional records, pick an integration path (API, host‑to‑host, middleware or plug‑in) that matches transaction volumes and IT capacity, and map every field so reconciliation and training data are trustworthy - JPMorgan's guide to ERP‑bank integration shows why mapping, testing and cross‑functional teams are non‑negotiable for a smooth cutover.

Local banks are already moving in this direction - Banco Pichincha is rebuilding teams and APIs so it can consolidate “all a customer's accounts in the bank's app” and later treat those APIs as income‑generating products - a useful playbook for Peruvian firms that must balance innovation with regulatory sensitivity.

Banking APIs aren't just technical connectors; they define consent, access levels and security controls, so follow the basics of banking APIs (authentication, read vs write endpoints, and data privacy) to avoid creating risky data swamps.

Treasury and finance teams should prioritize real‑time feeds for cash visibility, a robust canonical chart of accounts for ML friendliness, and vendor due diligence on middleware or TMS providers - platforms like Kyriba illustrate how extensive bank and ERP connectors can accelerate time‑to‑value while reducing manual handoffs.

“The idea is that we can monetize our APIs and see them as a product”

Turn AI ambition into repeatable value with a clear roadmap tailored to Peruvian finance teams: start by reviewing current processes and setting measurable goals (accuracy, time‑savings, KPIs) so pilots are judged by impact, not novelty; next, prepare and secure data - clean, canonical ledgers and end‑to‑end lineage are non‑negotiable for reliable models; select tools that integrate with ERP/banking systems and vendor ecosystems, then build and validate models on 3–5 years of historical data using standard metrics (MAE/MSE) before widening scope; integrate incrementally - connect one system (general ledger or bank feed) first, monitor performance and automate reconciliations to shave month‑end toil; train teams with hands‑on workshops and practical prompts (trial the prompts in DFIN's finance guide to speed report drafting and error checks) so humans can interpret model outputs, not blindly trust them; codify governance and audit readiness early - maintain an AI inventory, digital audit trails and bias/Explainability records; and finally, iterate: monitor drift, retrain periodically and use an AI‑powered audit checklist (see the Plooto 10‑point list) to stay ready for regulators and auditors.

Think of the roadmap like a 13‑week rolling forecast that updates as new receipts arrive - small, testable wins compound into strategic capacity and real risk reduction.

“When a vendor delivers an AI-powered software solution, the responsibility for its performance, fairness and risk still rests with the deploying business. Auditors expect these companies to provide evidence that they understand what the AI system does and clearly document known limitations and intended uses.”

Managing risks, governance and compliance of AI in Peru means turning the obligations in Peru's Law 31814 into everyday finance practice: treat systems by risk class (the law flags prohibited uses and lists credit scoring, employment screening and biometric ID as high‑risk), bake in human oversight, transparency, data‑minimisation and regular audits, and be ready to report security incidents to national authorities as required (see the Nemko summary of Law 31814).

Boards and senior leaders must own this shift - director duties now include clear accountability for AI strategy, third‑party vendor risk and the very real liability of “AI washing” if controls are overstated - so update D&O reviews and stress‑test insurance as part of rollout planning (see AJG on directors' duties).

Practical steps for finance teams include a living model inventory, risk assessments for each use case, explainability and model cards for high‑risk workflows, robust vendor due diligence for outsourced models, encryption and access controls around training data, and operational monitoring that catches drift or bias before month‑end close; this matters because a missing audit trail or an unlogged model change can turn a helpful FX‑forecast into a regulatory headache.

Finally, expect evolving guidance specific to financial services - CGAP warns finance guidance remains nascent - so document decisions, train staff on governance and treat compliance as an ongoing programme, not a one‑off checkbox.

“The key to managing these fears is transparent communication. Organizations need to reassure employees that AI adoption will be a gradual process and that there will be plenty of opportunities for reskilling. It's about creating a culture where employees feel empowered to adapt,” explains Ben Warren.

For Peru's finance teams the tool decision often comes down to two questions: keep the Excel workflows that everyone trusts, or move to an AI‑native platform that connects ERPs, banks and real‑time feeds.

Excel‑friendly vendors like Datarails (with its FP&A Genius chat, storyboards and Excel focus) and Vena (deep Microsoft 365/Excel integration and Vena Copilot) let teams lift manual reporting and variance analysis without abandoning familiar spreadsheets, while newer spreadsheet‑first options like Cube and Jirav add agentic forecasting and one‑click forecasts to reduce days of spreadsheet toil (see the Datarails FP&A roundup).

For bigger, multi‑entity finance stacks, enterprise players - Planful, Anaplan, Workday and SAP - bring scalable ML forecasting and scenario engines; meanwhile Microsoft's Copilot for Finance promises built‑in reconciliation, anomaly detection and exportable slide/report workflows that simplify bank and ERP connectors.

Practical Peru use: pair AI reconciliation (for example, BlackLine-style workflows) with an Excel‑native FP&A layer to speed audits and keep regulators happy, turning manual month‑end grind into board‑ready insights faster than before.

For Peru's finance teams, prompt engineering is the practical skill that turns generative AI from a novelty into a dependable assistant: write precise, role‑based prompts (tell the model “you are a treasury analyst”) and break big jobs into ordered steps so the model digests one task at a time; specify output format (JSON, CSV, one‑slide board summary) and what not to include; use few‑shot examples when drafting disclosure notes or variance commentary, and chain‑of‑thought prompts for root‑cause analysis or complex calculations so the model shows its reasoning.

Start in a safe sandbox - many organisations run internal LLMs - and iterate: test, review and tighten prompts until outputs are audit‑ready. Practical finance prompts and templates for summaries, trend analysis and disclosure drafting can speed reporting and free analysts for strategy (see useful AI prompts for financial reporting), while finance‑focused prompt categories and recipes help teams standardise how they ask for forecasting, extraction and reformatting (see prompt engineering tips for finance).

The payoff is concrete: a messy month‑end file can become a clean, board‑ready KPI snapshot in minutes if prompts are engineered to be specific, contextual and reviewable - saving time without sacrificing control.

“It sounds simple, but 30 minutes with a prompt engineer can often make an application work when it wasn't before.”

Conclusion - practical next steps for Peru's finance professionals in 2025: treat AI adoption as a governance project first and a technology project second - start with risk classification under Peru's Law 31814 (the risk‑based framework that flags credit scoring, employment screening and biometric ID as high‑risk) and align pilots to SBS expectations and real‑world fraud rules like the 2FA liability shift coming from SBS Regulation No.

2286‑2024; resources such as Nemko's guide to AI regulation in Peru and the Chambers Fintech 2025 Peru guide make the compliance landscape concrete.

Practically, run a rapid model inventory and risk assessment, lock down data minimisation and cross‑border rules, pilot one high‑value use case (cash‑flow forecasting, AR automation or AI reconciliation) with measurable KPIs, and use sandboxes or bank API pilots to validate end‑to‑end workflows; think of governance like two‑factor authentication for models - without logged oversight a single unrecorded change can create regulatory liability as surely as an unauthorised card transaction.

For teams ready to build skills, the 15‑week Nucamp AI Essentials for Work bootcamp focuses on prompts, practical tools and workplace application so analysts can move from manual toil to strategic oversight while keeping regulators and auditors satisfied.