Top 10 AI Prompts and Use Cases and in the Financial Services Industry in Peru

Peru's banks and fintechs are at an inflection point: generative AI can sharpen fraud detection, speed credit decisions and turn manual compliance work into automated workflows while regulators demand explainability and data governance.

Global analyses like EY roadmap for AI in financial services and practical guides for Peru show how institutions can cut costs and boost customer service without sacrificing oversight; the Complete guide to using AI in Peru's financial sector (2025) highlights APDP-aligned checkpoints for models in production.

The real opportunity is pragmatic: use AI to convert stacks of contracts and transaction logs into searchable insights, free staff for higher-value review, and tighten real-time risk monitoring - while building clear governance so momentum doesn't outpace compliance.

Bootcamp	AI Essentials for Work
Length	15 Weeks
Courses	Foundations, Writing AI Prompts, Job-Based Practical AI Skills
Cost (early bird)	$3,582
Syllabus	AI Essentials for Work syllabus and details

Methodology - How these Top 10 Were Selected: selection prioritized practical impact for Peru's market by combining practitioner evidence, measurable ROI, and regulatory alignment: practitioner insights from a GARP LATAM chapter meeting in Lima (featuring speakers from BCP and ASBANC) helped surface real-world AI wins and operational constraints, Experian's GenAI guide shaped the ROI and use-case filter (priority to BI extraction, model monitoring and synthetic-data boosts), and local governance checkpoints - including APDP-aligned data governance and security-by-design recommendations - ensured each prompt or use case can meet Peruvian compliance needs; breadth-of-application checks (fraud, credit, customer service, ops) were validated against industry roundups of GenAI use cases.

The result: a Top 10 that favors deployable value over novelty, with each item scored for practitioner evidence, expected ROI, and regulatory readiness. Read more from the event and guides that informed this approach: GARP Lima meeting, Experian's GenAI use-case guide, and the APDP checklist for Peru.

Selection Criterion	Primary Source
Practitioner evidence	GARP LATAM chapter meeting in Lima - practitioner insights on AI in Peruvian finance
ROI & use-case value	Experian: Top generative AI use-cases in financial services (ROI and implementation guidance)
Regulatory & data governance	APDP checklist for Peru and related Nucamp resources (data governance and compliance)
Breadth of GenAI applications	Ciklum: Top generative AI use cases in finance and banking - breadth of applications

Real-time fraud and AML work in Peru needs pragmatic, explainable tools that catch bad actors without choking the customer journey: machine learning can score a transaction in under a second and orchestrate device, behavioral and consortia signals so genuine customers flow through while risky activity is stopped, a pattern Experian documents in its real-time fraud guide (Experian guide to real-time fraud detection with machine learning).

Academic work shows why this matters - Random Forest models can surface the bulk of high‑risk card fraud (the SSRN study reports ~92% detection in top-scoring bins and uses SHAP for local explainability), making model transparency practical for audit trails and AML reporting (SSRN study: Real‑Time Fraud Detection Using Machine Learning with Random Forest and SHAP).

Commercial platforms translate those advances into pre-authorization prevention and self‑learning orchestration that reduces false positives and manual reviews - tools Peru's UIF‑Perú and VASP operators can adopt while insisting on security‑by‑design and clear model explanations to meet local compliance expectations (Eastnets AI fraud prevention and orchestration solutions).

Metric / Finding	Source
Random Forest: ~92% detection in top fraud-score bin	SSRN study
ML can produce fraud scores in <1 second; 27% of firms detect fraud in real time	Experian guide
AI orchestration reduces false positives and manual reviews	Eastnets solutions

AI-driven credit decisioning in Peru is a high-impact, practical use case: multiple studies show that merging retail loyalty and transaction data with traditional records can reliably score thin-file applicants and expand access without materially raising default rates - simulations on Peruvian data increased approval rates for people with no credit history from about 16% to between 31% and 48% (while approvals for those with established histories stayed near ~88%) (SSRN study on alternative data for credit scoring in Peru, Notre Dame summary on proving creditworthiness without a credit history).

Practical signals - shopping cadence, purchases like fresh beans versus ready‑to‑eat meals, promotion responsiveness - translate into features that machine learning models (Random Forest, XGBoost and similar) can use to predict repayment; the result is meaningful financial inclusion if models are deployed with security‑by‑design and clear explainability to satisfy central‑bank and consumer‑protection checkpoints such as those covered in local APDP guidance (APDP data-governance checklist for Peruvian financial services AI), helping lenders meet BCRP and TCEA compliance expectations while responsibly saying “yes” to qualified applicants.

Applicant group	Traditional approval rate	With retail/alternative data	Source
No credit history	~16%	31%–48%	SSRN / Notre Dame
With credit history	~88%	~88% (unchanged)	SSRN / Kellogg summary

“It's the classic catch-22 in lending,” Yang said. “You need a credit history to get a loan, but you need a loan to build a credit history. We show that everyday shopping data can break that loop and help lenders say yes with confidence.”

Conversational AI that natively handles Spanish - and indigenous languages such as Quechua - is a practical lever for Peruvian banks and fintechs to reduce friction and boost inclusion: an onboarding bot can make-or-break new users (Verloop notes

“over 6 in 10 users”

judge a service by onboarding and that poor flows drive large early churn), so designing flows that use buttons, quick replies and channel-native features matters.

Pick channels where customers already live (WhatsApp and SMS for many Peruvian customers), start with a tight pilot for merchant or account onboarding, and bake in human handoffs, analytics and KPIs so the bot improves with real interactions; these are core chatbot best practices highlighted in industry playbooks like the GovTech/Denser guide to building smart AI bots.

Prioritise short, local-language prompts, smooth agent escalation and a living knowledge base so support is fast, accurate and auditable - the result is measurably fewer abandoned sign-ups and a more accessible service for customers across Peru's linguistic landscape, from urban Spanish speakers to Quechua users who benefit from conversational, step-by-step help.

Regulatory intelligence and compliance automation are fast becoming operational essentials for Peruvian banks and fintechs: RegTech can map activity-based obligations across SBS, SMV, UIF‑Perú, BCRP and APDP, automate KYC/AML checks and help generate the Registro de Operaciones and suspicious-activity outputs required by UIF‑Perú, and keep model and data governance aligned with APDP expectations as new AI rules land (Peru's draft AI bill adopts an EU‑style, risk‑based approach) - all while tying into SBS sandbox regimes so pilots run with explicit guardrails.

Practical deployments combine monitored rulebooks (to detect when a product needs SMV vs SBS oversight), explainable-model logs for audit trails, and consent-aware data flows so open‑finance and API orchestration don't trip data‑protection rules; Chambers' Fintech 2025 guide outlines these cross‑regulator realities and Access Partnership's briefing tracks the AI bill and accountability requirements, while SBS sandbox rules offer a clear path to test innovations under supervised conditions.

The payoff is tangible: turn static transaction registers into a living compliance risk map that flags reportable activity and documents explainability end-to-end for audits.

Regulator	Primary remit
Chambers Fintech 2025: Peru Practice Guide (SBS)	Banking supervision, sandbox for innovative models, prudential rules
SMV	Securities market oversight and crowdfunding/capital markets registration
UIF‑Perú	AML/CFT reporting and suspicious-activity registers
BCRP	Payment-system rules, QR/e-money interoperability
APDP	Personal data protection and model/data‑governance requirements

Intelligent document processing (IDP) turns Peru's paper-heavy contract and compliance backlog into decision-ready data - think scanned TCEA filings, digital‑signature audit trails and supplier agreements parsed into clause-level fields in seconds - so underwriters and compliance teams can search, verify and evidence obligations without months of manual review.

Modern IDP pipelines combine OCR to rescue low‑quality scans, NLP/LLMs to detect parties, dates and penalty clauses, and ML‑led validation plus human‑in‑the‑loop checks to meet APDP and audit-readiness expectations; practical vendors and guides show this in action, from Unstract's contract OCR and LLMWhisperer API that preserves layout and outputs JSON for CLM/ERP workflows to KlearStack's extraction playbook and Infrrd's roadmap for automated data extraction in finance.

The payoff is concrete: faster contract review, tighter digital‑signature checks, and auditable extracts that make regulators' spot checks far less painful - turning a cardboard box of contracts into a searchable contract library where every renewal date and signature hash is just a query away.

Capability	Why it matters	Source
Layout‑preserving OCR	Keeps tables, clauses and signature blocks intact for legal review	Unstract contract OCR
NLP & LLM extraction	Converts free‑text clauses into structured JSON for workflows	KlearStack guide
HITL validation & compliance	Ensures accuracy, audit trails and readiness for APDP/TCEA checks	Infrrd: automated data extraction

Personalized robo‑advice and customer segmentation can turn Peru's fragmented retail-investor base into clearer suitability bands that meet SMV suitability rules while still offering practical, local choices: automated profiles can map risk tolerance, income source and investment horizon to low‑cost, Peruvian-focused instruments - for example steering a conservative saver toward a soles‑denominated sovereign ETF such as the VanEck El Dorado Perú Soberano ETF fund overview (ETFPESOV) for stable income, while matching a growth‑oriented client to the iShares MSCI Peru and Global Exposure ETF fund facts (EPU) for equity exposure - choices robo‑advisors can justify with auditable model logs and APDP‑aligned governance checkpoints.

This approach also aligns with how Peruvian pension funds and retail platforms use ETFs for cost‑efficient diversification, and it benefits from firm-level commitments to security‑by‑design and explainability in model outputs so advisers can document suitability decisions for regulators and clients alike; see the VanEck ETF overview, EPU fund facts, and the AI Essentials for Work syllabus - APDP data‑governance checklist (Nucamp) for implementation guidance.

Product	Ticker	Why useful for robo‑advice
VanEck El Dorado Perú Soberano ETF - VanEck fund overview	ETFPESOV	Soles‑denominated sovereign bonds for conservative, income‑focused profiles
iShares MSCI Peru and Global Exposure ETF - EPU fund facts	EPU	Equity exposure to Peruvian companies for growth‑oriented investors
AI Essentials for Work syllabus - APDP data‑governance checklist (Nucamp)	-	Governance and explainability checkpoints for robo‑advice model decisions

Predictive cash‑flow and treasury optimisation for Peruvian corporates means using AI to turn intermittent spreadsheets and siloed ERP feeds into a continuous, multi‑entity view that watches PEN/USD exposures and local real‑time rails so treasurers can act before a funding gap appears.

Platforms like Finacle cash flow forecasting solution show how granular, account‑level forecasts and ERP/TMS integration let banks offer dynamic what‑if simulations to clients, while J.P. Morgan's work on J.P. Morgan AI-driven cash flow forecasting insights highlights real‑time data fusion, thousands of scenario runs and the potential to cut forecast error rates by as much as 50% - a vivid shift from

“guesswork”

to a treasury radar.

Combine classical time‑series methods (naïve, moving averages, ARIMA) with ML ensembles to pick the right cadence - 13‑week operational forecasts for working capital, daily positioning for short‑term liquidity - and you get a system that flags FX stress, optimises cash buffers and sequences payments over local rails with confidence; GTreasury's primer on time‑series methods is a practical place to start (GTreasury time-series cash forecasting methods primer).

Capability	Why it matters	Source
Granular, ERP‑integrated forecasts	Real‑time visibility across accounts and entities	Finacle cash flow forecasting solution
AI scenario & stress testing	Simulate FX shocks and liquidity outcomes quickly	J.P. Morgan AI-driven cash flow forecasting insights
Time‑series method selection	Matches model to horizon (daily vs 13‑week) for accuracy	GTreasury time-series cash forecasting methods primer

Integrating POS/QR and local payment-rail feeds into an AI-first reconciliation pipeline can turn Peru's month‑end scramble into a near real‑time operation: AI-powered transaction matching not only ingests and normalises messy bank, PSP and POS descriptions, it applies probabilistic and rule‑based matching so high‑confidence items auto-post while exceptions land with rich context for fast resolution - a shift that turns

days of spreadsheet drudgery

into minutes and rescues the 44 hours/week many teams lose to manual fixes.

Platforms that harness LLMs and pattern learning can parse emailed remittances, ambiguous memos and QR receipts to infer invoice links and one‑to‑many matches (see how Ledge surfaces context from memo fields and remittance attachments), while end‑to‑end systems cite 100x faster processing and up to 98% fewer errors on matched flows.

For Peruvian banks and fintechs that must meet APDP governance and audit trails, embed explainability and consent checks so fast closes don't compromise compliance - the payoff is auditable, continuous reconciliation that accelerates the close, tightens cash visibility and plugs revenue leakage on high‑volume POS/QR volumes.

Benefit / Metric	Claim	Source
Time savings	Turns days of manual reconciliation into minutes	Solvexia article on AI transaction matching
Accuracy & error reduction	Up to 98% fewer errors; AI can process transactions 100x faster	Solvexia reconciliation benefits case study
Multi‑source parsing	Parses memos, remittance advice and many‑to‑many matches for POS/QR and PSP feeds	Ledge AI reconciliation use cases for finance

Market and investment research in Peru gets a practical turbo‑charge when programmatic access to central bank time series meets company‑level signals: the BCRP Model Context Protocol (MCP) server lets AI agents pull monetary and economic series on demand so models can fuse inflation, policy rate and FX flows with market reads in seconds (BCRP Model Context Protocol (MCP) server - Peru central bank time series API).

Paired with granular issuer briefs - like the Manpower Perú credit snapshot that surfaces a 1.167% one‑year PD, a 2.6% current spread and clear macro sensitivities to inflation, the S&P 500 and the US dollar - algorithms can spot momentum shifts or contagion risks before manual reports close the loop (Manpower Perú issuer credit brief - PD, spread and macro sensitivity analysis).

The payoff is a living research stack: automated alerts, backtestable factor exposures and explainable signal logs that feed traders, credit officers and compliance teams while keeping APDP‑style governance and model explainability front and centre.

Source	Primary Use	Example Signal
Peru BCRP Model Context Protocol (MCP) server - central bank economic time series API	Programmatic access to economic & monetary time series	Inflation, policy rate, FX time series for model inputs
Manpower Perú issuer credit brief - probability of default and spread metrics	Issuer‑level credit & market signals	PD 1.167%, spread 2.6%, macro sensitivities

Data‑sharing and open‑finance orchestration are the practical backbone for AI to unlock cross‑product value in Peru: standardized APIs plus APDP‑aligned consent records let banks, wallets and fintechs exchange signals for credit, liquidity and fraud models while keeping data protection and audit trails front and centre.

Peru still lags some neighbours - Chile's fintech law highlights that Peru remains the only Pacific Alliance country without a formal open‑banking regime - so momentum is now driven by regulator proposals and industry pilots that emphasise broad

open finance benefits like empowerment, competition and inclusion

Mastercard insights on open banking in Latin America.

The recent Fintech 2025 – Peru trends and developments practice guide notes ongoing SBS/BCRP interest, plus familiar concerns about consent mechanics, privacy and implementation - so the practical path is API standards, a tamper‑evident consent ledger and sandboxed pilots to prove interoperability and compliance.

For teams building or supervising these flows, a simple checklist to map APDP requirements into consent records and model governance is essential; Nucamp's APDP checklist is a good starting point for operationalising those controls: Nucamp AI Essentials APDP data‑governance checklist (syllabus), because a single auditable consent token - like a digital handshake logged with every API call - can be the difference between usable open finance and regulatory friction.

Practical next steps for Peruvian banks and fintechs: treat Peru's updated PDPL as the starting line - map data flows, register databases with the ANPD, and bake explicit consent controls into every customer touchpoint (a single auditable consent token - like a digital handshake logged with every API call - is the practical goal).

Prioritise privacy‑by‑design, automated ARCO workflows and fast breach playbooks (new rules require prompt notifications in many cases), and plan for the phased DPO/representative deadlines so governance scales with revenue and risk; see the PDPL compliance guide for what rights and obligations to operationalise now (access, rectification, erasure, portability and limits on automated decisioning) and use sandboxed pilots with SBS to validate explainability and model logs before full rollout.

Invest in consent management and RoPA tooling, harden vendor contracts for cross‑border transfers, and upskill teams on model validation and data stewardship - practical training such as Nucamp's AI Essentials for Work syllabus can accelerate operational readiness while keeping APDP checkpoints visible.

Start small, document everything, and convert proofs of concept into audit‑ready controls so AI delivers inclusion and efficiency without regulatory friction.

Immediate action	Why / source
Data mapping & ANPD registration	Peru PDPL compliance guide: Personal Data Protection Law (PDPL)
Consent & RoPA tooling	Automate ARCO responses and consent records (PDPL / Securiti guidance)
Prepare for phased DPO/representative rules	Phased deadlines for DPOs and local representatives (regulatory updates)
Upskill teams on AI governance	Nucamp AI Essentials for Work syllabus: practical AI skills for the workplace