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

Chile's financial landscape in 2025 is a fast-moving mix of opportunity and guardrails: the Fintech Law and the rollout of the Sistema de Finanzas Abiertas are unlocking consent-based data sharing and API-driven interoperability while the CMF tightens rules on governance, cybersecurity and AML/CTF - a regulatory backdrop described in the Fintech 2025 guide for Chile (Fintech 2025 – Chile practice guide).

At the same time, AI is already embedded in credit scoring, fraud detection, personalised advice and automated trading, and a pending national AI bill would class credit scoring as a high‑risk use and create an AI commission with authorisation and sanction powers (Analysis of Chile's AI regulation plan).

For finance teams this means combining practical AI skills with strict compliance and explainability: think of systems that flag anomalies once caught only by manual review.

Upskilling programs like the AI Essentials for Work bootcamp offer practical training to apply AI responsibly in real-world finance roles (AI Essentials for Work bootcamp (Nucamp)).

AI must serve humans (human-centric principle), sustainability purposes, inclusivity-centric, ethics and globalisation focused.

The future of AI in Chilean finance looks practical and tightly regulated: as the Chambers Fintech 2025 Chile trends and developments document, AI is already embedded in credit scoring, fraud detection, personalised advice and automated trading, and regulators are pushing for Open Finance, sandboxes and stronger governance to keep pace (Chambers Fintech 2025 Chile trends and developments).

Global industry research also shows fraud detection, customer experience and portfolio optimisation leading AI adoption, which aligns with local priorities (NVIDIA State of AI in Financial Services report).

That alignment matters because fraud surged - credit fraud +20% and debit fraud +113% in 2023 - so Chilean rules now demand stronger authentication and tighter procedures for chargebacks, pushing banks to pair AI detectors with airtight controls (LATAM financial regulations roundup on fraud and compliance).

The “so what” is simple: successful teams will blend explainable models, consent-based Open Finance APIs and sandbox-tested workflows so AI decisions are auditable, interoperable and resilient under CMF and AML scrutiny.

“Today's discussions demonstrate that we are moving towards the development of financial services that seamlessly connect with consumers' daily lives, reflecting their changing dynamics and needs,” said Pablo Pereyra Portugal.

Chile's national AI policy sits inside a tightly centralised fintech and banking reform package where the Fintech Law and CMF supervision set the terrain for how AI can be used in finance: the law channels fintech registration, governance and risk rules through the CMF, requires strong AML/CTF and data‑protection safeguards, and fosters an Open Finance System that will only work with consented, secure APIs (Chile Fintech 2025 practice guide – Fintech Law and CMF supervision).

Regulators have already tightened the registration and governance toolkit (see NCG 502 for the registration/authorisation regime and updated governance methodology), so any AI project touching credit scoring, advisory or payment initiation must slot into existing authorisation, cybersecurity and outsourcing rules (NCG 502 registration regime and Chile banking laws overview).

The Open Finance rollout is phased - implementation begins 24 months after publication with banks required to deploy APIs within six months of the start date - so teams must design models that are explainable, consent‑aware and auditable from day one.

Practically this means pairing model documentation, incident reporting and robust KYC/AML controls to satisfy CMF oversight and cross‑sector requirements; a useful mental image is an automated credit decision that can output both a customer‑friendly explanation and a regulator‑grade audit trail within seconds.

Keep an eye on CMF consultations and rule updates - recent proposals updating customer service channels show the regulator's ongoing push to modernise service, supervision and consumer protections in this new AI‑enabled landscape (CMF consultation on customer service rules for modernised AI-enabled supervision).

Finance professionals in Chile can start putting AI to work today in ways that are both practical and compliant: use machine‑learning models for smarter credit scoring and faster underwriting, pair anomaly‑detection systems with transaction monitoring to reduce fraud and AML risk, deploy GenAI assistants to summarise KYC documents or answer routine customer queries, and build consent‑aware pipelines that feed explainable models via the Sistema de Finanzas Abiertas APIs so decisions remain auditable under CMF rules (Chambers Fintech 2025 Chile legal guide).

Real-world templates and agent patterns - from fraud detection and AML copilots to document‑automation and RAG‑grounded copilots - are already proven at scale and can be adapted for Chilean banks, insurers and fintechs (Google Cloud generative AI real-world use cases).

Underwriting and credit teams should prioritise clean data, bias testing and explainability so ML lifts accuracy without creating regulatory headaches - a path echoed by industry guidance on modern credit decisioning and explainable ML for underwriting (Experian credit underwriting and modern decisioning guidance).

The pragmatic “so what” is this: small pilot projects - think an AI pipeline that converts messy documents into structured attributes and a challenger credit model that outputs a plain‑language reason for each decision - can cut manual review time while producing the audit trail regulators will expect.

"Artificial intelligence (AI) will “undoubtedly” still be the key theme in insurance in 2025, according to a new report."

Start small and regulatory‑first: map every model and dataset to Chile's four‑tier risk taxonomy, then run a focused regulatory gap analysis so teams know which systems count as “high‑risk” under the draft AI law and require audits and conformity checks (Chile AI law project overview - Janus GRC); next, perform formal risk assessments, document training data and validation plans, and align testing to international standards referenced in Chile's framework to make compliance auditable (Chile AI regulation framework & requirements - Nemko Digital).

Deploy an Ethics Channel as the day‑one governance tool: set clear escalation workflows, human‑in‑the‑loop checkpoints, and an incident pathway so an overnight model‑drift alert can surface to compliance and produce a regulator‑grade trail by morning.

Run pilots that prioritise explainability, bias testing and robust logging (model versioning, bias metrics, decision records), tie KYC/AML detectors to documented human review, and keep third‑party conformity assessment in scope for any high‑impact credit or fraud systems.

After 6–12 months, review channel metrics (report volume, risk types, resource needs) and, if multiple high‑risk systems or multi‑law exposure exists, migrate to a unified GRC platform to automate continuous monitoring, predictive compliance and cross‑law incident management; the “so what” is tangible - this staged path turns regulatory complexity into operational advantage by producing repeatable audit evidence, faster remediation, and a trustworthy trail for both customers and the Personal Data Protection Agency.

Compliance for AI in Chileese finance runs through three clear pipes: Law No. 19,913 and the Financial Analysis Unit (UAF) which mandate suspicious‑transaction reporting and cash reporting thresholds, CMF supervision and the Fintech/Open Finance regime that enforces KYC, consented APIs and tighter outsourcing rules, and recent tax and compliance updates (Law No.

21,713) that broaden reporting and inter‑agency access to data; read the legal landscape in the Chile Banking Regulation 2025 guide (Chile Banking Regulation 2025 guide) and the Chile Tax Compliance Law AML & CFT measures summary (Chile Tax Compliance Law: AML & CFT measures).

Practically this means AI pipelines must bake in KYC checks, human escalation paths and auditable logs (records kept for a minimum five years and SARs/cash‑reporting to the UAF are mandatory), ensure PEP and enhanced due diligence workflows, and be designed to produce regulator‑grade evidence on demand;

the “so what” is tangible - an automated model that can surface a flagged transaction plus a time‑stamped dossier for the UAF turns regulatory duty into operational hygiene, not an afterthought.

Finance teams building or buying models should therefore map CMF/UAF obligations to data flows, appoint clear compliance ownership, and require vendors to meet Chile's outsourcing and data‑access expectations before deployment.

Open Finance in Chile turns AI projects from data‑hungry experiments into API‑driven production workflows where consent, traceability and resilience are non‑negotiable: General Rule NCG 514 frames the OFS as an API ecosystem with a phased rollout (implementation begins 24 months after publication, i.e.

from July 4, 2026) and banks/card issuers required to expose core APIs within six months of that start date, so any model that scores credit or surfaces personalised offers must be able to ingest consented API payloads and publish verifiable logs of that consent (Carey summary of Chilean NCG 514 Open Finance regulation).

Recent CMF proposals add a Technical Annex and operational rules - digital certificates, sandbox testing validated by external certifiers, mandatory participant directories and a required secondary delivery mechanism for continuity - raising the bar on functional testing, authentication and incident traceability that AI teams must bake into pipelines (CMF public consultation on NCG 514 technical annex and proposed amendments).

Practically this means models need consent‑aware ingestion, certificate validation, detailed API call and decision logs, CMF‑grade monitoring and fallback channels for outages; imagine an automated credit decision that won't issue a rate unless it can show a consent timestamp, the issuing certificate and the API trace - that single, auditable packet turns compliance into an operational advantage for trustable AI in Chile.

"The development of this website will contribute significantly to the implementation of the OFS, incentivizing end users' participation through the Citizen Section, and facilitating the flow of key information to operate the System with the Sections for Developers and Participants."

AI governance for Chilean finance teams must be practical, auditable and vendor-wise: the draft AI Bill and related guidance embed a risk‑based classification, transparency and human‑in‑the‑loop requirements that push lending, fraud and underwriting models to publish clear documentation, explainability outputs and governance trails (see the Chile draft AI Bill official analysis: traceability and explainability principles at Chile draft AI Bill official analysis: traceability and explainability principles).

Lessons from public procurement and live pilots show how this works in practice - ChileCompra's (earlier) bidding terms and SUSESO's machine‑learning projects illustrate the tradeoffs between cost, supplier capability and responsible‑AI criteria, and how audit tools and bias metrics became decisive in vendor selection (see the SUSESO AI governance case study and procurement lessons at SUSESO AI governance case study and procurement lessons).

Finance teams should adopt GobLab's transparency and bias‑measurement patterns - algorithmic transparency report cards, statistical parity checks and documented human overrides - so every automated decision can produce a regulator‑grade packet: model version, training data lineage, bias metrics and a time‑stamped human review.

The “so what” is simple and tangible - a single, auditable packet that ties a credit score to an explainable reason and a human verification transforms regulatory burden into competitive trust, making AI decisions defensible to customers, the CMF and the future Personal Data Protection Agency (see GobLab Ethical Algorithms tools for algorithmic transparency and bias measurement at GobLab Ethical Algorithms tools for algorithmic transparency and bias measurement).

traceability and explainability shall be provided, ensuring that individuals know and are aware ...

Becoming an AI expert in Chile in 2025 is a practical, stepwise craft: start with the core foundations - mathematics, Python, data cleaning and machine‑learning basics - then add supporting skills like SQL, cloud deployment, Git and MLOps so models actually run in production; the ODSC “AI Skills Roadmap” lays out these building blocks clearly (ODSC AI Skills Roadmap for 2025).

From there, pick a career trajectory - AI/ML engineer, data scientist, generative‑AI specialist, product manager or ethics/governance lead - and use role maps to align learning and interviews (the AI Engineer roadmap is a handy step‑by‑step guide: AI Engineer Roadmap (roadmap.sh)); Coursera's job‑leveling matrix can help frame promotions and skill checkpoints as responsibilities grow (Coursera job‑leveling matrix for AI career pathways).

Build a compact portfolio that matters to Chilean finance employers: a GitHub notebook and API demo that turns transaction data into explainable credit features and a plain‑language decision reason is worth more than dozens of slides - tangible evidence of domain fluency, responsible design and deployment wins interviews and regulatory trust.

Finally, choose a certification or a focused bootcamp to validate skills, practise real projects, and keep learning iteratively as tools and rules evolve.

Conclusion: finance teams in Chile should treat AI as a regulated, strategic capability - not a one-off experiment - by starting with small, well‑scoped pilots tied to clear business metrics, rigorous governance and auditable trails that map to the draft national AI Bill's risk framework (Analysis of Chile's AI Bill (2025)); that matters because Chile's fast-growing fintech ecosystem (348 active companies in 2024) and tighter CMF/Open Finance rules will favour projects that are explainable, consent‑aware and operationally resilient.

Beware pilot purgatory - enterprise research shows most experiments stall without firm sponsor alignment, vendor accountability and frontline adoption - so move from proof‑of‑concept to production only after measurable ROI, vendor SLAs and compliance checks are in place (Analysis: why most AI pilots never take flight).

Practical next steps: map systems to Chile's risk tiers, run bias and audit tests, tie KYC/AML logs to model outputs, and invest in people over buzz - for non‑technical finance leads, a structured upskilling path such as Nucamp AI Essentials for Work bootcamp helps translate governance into day‑to‑day practices and prompts that protect customers and regulators alike; the payoff is tangible - faster, auditable decisions that build trust with clients and supervisors rather than friction in the back office.

“With the right strategy, CFOs can create substantial benefits by deploying emerging technologies such as AI.”