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

Chile's financial services sector is at an inflection point: a draft AI bill that borrows the EU's risk-based approach is moving credit scoring, biometric checks and other high‑stakes systems into a tighter compliance frame, while proposing a National Commission to authorize and monitor AI - noncompliance can mean fines like 200 UTM (roughly USD $14,000) or worse (see the bill analysis).

At the same time, homegrown initiatives such as GobLab's Ethical Algorithms are already pushing transparency and bias audits into public procurement, and regional evidence shows AI can expand access by using alternative data to underwrite loans for underbanked customers.

That mix of stricter governance and clear business upside means Chilean banks, AFPs and fintechs must pair product pilots with explainability, risk plans and staff upskilling; practical programs like Nucamp AI Essentials for Work 15-week bootcamp prepare nontechnical teams to write prompts, run pilots and embed AI responsibly across retail and corporate finance.

“We found it intriguing to focus on public procurement. We saw an opportunity to foster public-private collaboration, raise awareness, and build capacity in ethical AI, while developing concrete tools to ensure a positive social impact.” - Carolina Carrasco

For full course details and syllabus, see the AI Essentials for Work syllabus and course details.

The methodology prioritized real-world impact, regulatory fit and technical feasibility inside Chile's fast-maturing fintech ecosystem: use cases were selected where AI already shows traction (credit scoring, fraud detection, personalised advice and automated trading) and where Chile's Fintech Law, the CMF's oversight and the Open Finance rollout create clear operational paths for deployment (see Chambers' Fintech 2025 - Chile).

Priority criteria included alignment with CMF disclosure and governance expectations (transparent model criteria, suitability and audit trails), measurable business upside for banks, AFPs and fintechs (fraud reduction, alternative-data underwriting) and practical risk controls for AML/CTF and data protection.

Market-readiness also mattered - Chile's growing pool of 300+ fintechs and rising demand for AI-driven fraud and payment solutions signalled where pilots could scale quickly (see Fintech Market in Chile and Top Fintech Startups and Scaleups from Chile).

Prompts were therefore chosen to produce explainable outputs (human‑readable rationales and CMF‑friendly summaries), to leverage Open Finance data for inclusive underwriting, and to suit sandbox-style tests that regulators are already supporting - so the result is a shortlist of prompts that balance innovation, compliance and clear operational benefit for Chilean financial services.

Conversational finance and voice AI turn round‑the‑clock service from a nice-to-have into an operational must for Chilean banks, AFPs and fintechs - cutting wait times, speeding resolutions and scaling personalised advice across web, mobile, messaging and voice channels.

Well‑trained AI agents can handle balance checks, payment reminders, onboarding KYC steps and even real‑time fraud confirmations, then escalate high‑stakes cases to humans with full context, which preserves trust and regulatory audit trails (see the Cognigy case study on banking benefits).

Voice biometrics and real‑time speech analytics add a security layer - Telnyx shows how voice authentication and low‑latency media let institutions verify identity in seconds while logging transcripts for compliance - so a customer can check a balance at midnight and freeze a suspicious transaction without a long hold.

Practical rollout advice is familiar: start with high‑volume, low‑complexity flows, integrate via APIs to legacy cores, and measure containment and CSAT before widening scope; for Chilean teams focused on cost reduction and inclusion, conversational AI is a fast path to 24/7 accessibility and operational efficiency (read how AI is helping financial services in Chile).

Regulatory reporting and CMF document automation can convert compliance from a late‑stage scramble into a predictable, auditable workflow for Chilean banks, AFPs and fintechs: automations that produce CMF‑ready summaries, vendor due‑diligence packets and clear audit trails address exactly the outsourcing and disclosure expectations set out in Chapter 20‑7, while aligning with the CMF's updated formats and IFRS compendium updates (see the CMF's outsourcing regulation changes).

Cloud providers have begun to make this practical - vendor mappings and compliance templates (for example, Google Cloud's CMF Chapter 20‑7 mappings) help teams map contracts, business continuity evidence and security controls back to regulator checklists, and AWS guidance highlights the RAN rules and privacy considerations teams must track when moving regulated workloads to the cloud.

In a market moving fast toward Open Finance and stricter conduct standards, automated, provenance‑aware summaries that bundle methodology, data lineage and SLA evidence into a single packet let compliance teams prove suitability without drowning in pages of appendices - so regulators get the transparency they need and product teams get the speed to innovate within the CMF's guardrails (see Fintech 2025 - Chile for the regulatory context).

Credit decisioning in Chile must now balance inclusion with airtight explainability: with the CMF moving debtor reporting from monthly to weekly - shrinking the reporting lag from as much as 50 days to roughly 16 days - lenders can refresh risk views faster and use Open Finance signals to underwrite thin‑file customers, but only if denials come with clear, auditable rationales that satisfy supervisors and customers alike CMF weekly debtor reporting.

Practical workflows layer alternative data and consented OFS APIs into a transparent scoring pipeline that records data lineage, feature importance and human‑readable reasons for decline so regulators and borrowers get the

“why” not just the outcome

those governance expectations mirror the CMF's new corporate standards and risk‑management rules such as NCG 507–509 that demand documented risk appetites and board‑level oversight Chile NCG 507–509 general standards.

At the same time, emerging rules around the consolidated debt registry and Open Finance create operational paths for inclusive pilots - paired with proportional disclosures about fees and suitability - so lenders can expand access while generating CMF‑ready audit trails Fintech 2025 Open Finance and Fintech Law analysis.

A vivid payoff: with weekly data feeds and explainable denials, a previously invisible micro‑entrepreneur can be evaluated on fresh cash‑flow signals and receive a clear, actionable denial that guides them to qualify next time, not just a closed door.

Fraud detection in Chilean finance should centre on cutting false positives as much as catching fraud: machine learning and hybrid pipelines - supervised models, unsupervised anomaly detectors and streaming active‑learning loops - can sift large transaction streams and spot subtle, evolving attack patterns while preserving legitimate flows (see practical notes on how ML works for payment fraud detection).

The technical challenge is familiar: extreme class imbalance (the well‑known European credit‑card dataset contains only 492 frauds in 284,807 transactions) means teams must combine resampling, cost‑sensitive learning and ensemble methods to avoid noisy alarms; academic and practitioner work recommends precision/recall and AUPRC as the right metrics.

Real‑world deployments add layered controls (device and geolocation features, velocity checks, human review queues) and a feedback loop so models learn from investigator decisions - SPD's case study shows ML can cut false positives by ~30% while keeping real‑time responses under 100 ms.

For Chilean banks and fintechs, prioritise small, high‑impact flows for pilot tests, instrument rapid retraining from investigation labels, and use public datasets and benchmarking to validate thresholds before scaling into production; this keeps customers moving and regulators satisfied without letting fraudsters slip through.

“By applying our method, we minimize false positives, or in other words, genuine instances marked as fraud, which is key to improving fraud detection.”

Synthetic data generation is a practical privacy-first lever for Chilean banks, AFPs and fintechs that must both innovate fast and protect customer PII: generative models can produce large, customizable datasets that mirror real transaction patterns - letting teams train fraud detectors, stress‑test credit models and run vendor due‑diligence without exposing live records (see Dataversity's primer on generative synthetic datasets).

For fraud and AML work this matters in a tangible way: rare, high‑risk behaviours can be oversampled synthetically so models learn to spot subtle anomalies that would otherwise be invisible, while secure synthetic shares enable cross‑team collaboration and sandbox testing with partners.

Leading financial research teams show synthetic sets can simulate customer journeys and payments at scale to accelerate R&D, but they also flag the tradeoffs - fidelity vs.

privacy, bias amplification and the “sim2real” gap - so best practice is hybrid pilots, differential‑privacy or watermarking, and rigorous validation before production (see J.P. Morgan's synthetic data research and banking guidance from the ABA).

The “so what?” is simple: privacy‑preserving data lets product teams iterate faster and regulators see airtight controls, but only when synthetic pipelines are audited, bias‑tested and paired with a steady flow of real‑world validation.

“Synthetic data allows us to carry out our experiments at scale.”

Automated financial reporting, forecasting and scenario analysis make CLP‑focused stress testing practical by turning conceptual macro scenarios into auditable outputs that feed capital planning and contingency decisions: studies show that incorporating macro variables can improve out‑of‑sample forecasts (Federal Reserve study on using macro variables in bank stress‑testing and forecasting), while stress‑testing playbooks emphasise coherent, dynamic scenarios that map macro shocks into revenues, losses and balance‑sheet effects (AnalystPrep guide to stress‑testing banks and scenario mapping).

For Chilean teams, AI can automate scenario generation, produce CMF‑ready reporting templates and speed the translation of CLP movements into quarterly P&L and capital trajectories - letting modelers iterate on coherent shocks and validation tests instead of wrestling with spreadsheets.

Practical pilots pair synthetic scenario libraries and back‑tested macro‑feature mappings with human review, so automated reports are both fast and explainable; for teams building those capabilities, targeted AI Essentials for Work bootcamp syllabus - AI tools and training for financial services in Chile bridge the gap between data science prototypes and regulator‑grade deliverables.

Back‑office modernization in Chile increasingly means turning decades‑old COBOL jobs into maintainable services with AI help: generative tools and agentic frameworks can extract buried business rules, map copybook dependencies and produce idiomatic Java instead of unreadable “JOBOL” literal translations, so teams can retire costly mainframe cycles and recruit modern Java talent.

Practical playbooks from Swimm show how application‑understanding and semantic diffing speed discovery and traceability, IBM's watsonx Code Assistant for Z demonstrates selective, context‑aware COBOL→Java translation tuned for enterprise Z environments, and recent agent‑based approaches from Microsoft illustrate staged pipelines (analyze, convert, test) that preserve logic while producing Quarkus‑ready microservices - all useful blueprints for Chilean banks, AFPs and fintechs aiming for safer, auditable cutovers.

Start small (non‑critical modules), keep legacy and new systems in parallel for semantic validation, and log AI prompts and mappings so regulators and auditors can trace each conversion decision - a clear audit trail makes modernization a compliance story, not a gamble.

“Generative AI can make modernization less overwhelming for enterprises.” - Ruchir Puri

Robo‑advisers and dynamic portfolio optimisation are a clear growth vector for AFPs and retail channels in Chile: global market forecasts show robo‑advisory adoption exploding into the tens of billions by 2025, underscoring strong demand for automated, low‑cost wealth management and personalised goal‑based plans (Global robo-advisor market forecasts).

Locally, the Fintech Law, the Sistema de Finanzas Abiertas and CMF rules on suitability, disclosure and registration create a regulatory path for banks, AFPs and fintechs to deploy hybrid robo models that combine algorithmic optimisation with human oversight - essential for meeting suitability and best‑execution standards in Chile (Chile fintech regulatory guide - Fintech 2025).

Practically this means providers can use consented Open Finance data to build CLP‑aware, goal‑based glidepaths and automated rebalancing that were once the preserve of high‑net‑worth clients; the memorable payoff is simple and tangible - personalised retirement advice at scale, not just premium service on demand.

Startups and incumbents should prioritise hybrid pilots in the CMF sandbox, clear suitability disclosures, and tight audit trails so automated advice scales without regulatory friction.

Trading teams building IPSA‑focused analytics should treat modern backtesting as more than replaying history: combine robust backtests with synthetic, agent‑based market simulations to stress strategies across many “what‑if” realities.

Retrospective simulation techniques can generate thousands of alternate historical paths (for example, 1,000 simulated price histories) so parameter choices aren't overfit to a single realised market, and backtesting best practices - clean data, out‑of‑sample validation and conservative slippage assumptions - keep results honest (see a practical backtesting guide).

At the same time, agent‑based models let practitioners seed realistic market microstructure - growth, momentum and value agents, liquidity limits and news shocks - so IPSA scenarios can show how sector tilts or large flows change tail behaviour (AWS's ABM guide explains calibration and scalable deployments).

The “so what?” is vivid: instead of a single equity curve, teams get a forest of outcomes that reveal when a strategy might blow up in the tails, letting risk teams calculate VaR/CVaR and design contingency execution rules before real capital is at risk.

Model monitoring and governance are the safety net that keeps Chilean banks, AFPs and fintech models honest: continuous checks for data, prediction and concept drift catch silent degradation before it becomes a customer‑facing error or regulatory incident.

Practical programs combine regular backtests and ground‑truth rollups with fast drift statistics (PSI, K‑S, Jensen‑Shannon) and a small set of well‑tuned alerts to avoid fatigue, as recommended in Datadog ML model monitoring best practices.

Governance ties those signals into an escalation path and board reporting so issues get fixed, not buried - a lapse in ongoing monitoring has cost institutions dearly in the past (Grant Thornton documents a $2B loss tied to poor model oversight), underlining why audit trails, logged feature lineage and retraining cadences belong to every model's lifecycle (Grant Thornton report on ongoing monitoring in model risk management).

Lightweight open‑source checks and visualization (for example, Evidently's monitoring tools) make it practical to monitor fairness metrics and cohort performance, surface bias early and trigger human review or automated retraining - so Chilean teams can scale AI while keeping regulators, customers and boards confident that models won't quietly drift into harm Evidently AI model monitoring and observability tools.

Where to start in Chile: treat AI as a disciplined playbook, not a one‑off experiment - first fix the plumbing (cloud data lakes and strong data governance) so models have clean, auditable inputs (see how banks in Chile are preparing data for AI via BNamericas), then run small, high‑impact pilots on customer‑facing flows that also reduce cost and risk - conversational banking, real‑time notifications and smart call deflection are proven starters (conversational banking delivers faster acquisition, 24/7 service and big call‑center savings, see Latinia's conversational banking primer).

Pair each pilot with expert‑in‑the‑loop controls, documented lineage, fairness tests and continuous monitoring so regulators and boards get explainable outputs; Capgemini's operational playbook highlights data quality, governance and iterative scaling as non‑negotiable.

Practical proof: large banks using smart deflection have cut wait times by ~70% and saved millions - showing the

so what?

in plain terms.

Finally, close the skills gap with targeted training for nontechnical teams so product owners and compliance can drive pilots - Nucamp's Nucamp AI Essentials for Work 15‑Week Bootcamp registration is built to teach promptcraft, tool use and pilot readiness for business teams ready to move from prototype to CMF‑grade production.