How AI Is Helping Financial Services Companies in Chile Cut Costs and Improve Efficiency

Chile's financial services sector is already reaping efficiency gains from AI: a fast-maturing fintech ecosystem (348 active startups in 2024) is pairing with top regional AI readiness to drive automation across payments, lending and risk workstreams.

The Latin American Artificial Intelligence Index places Chile at the forefront for infrastructure and talent, and its high connectivity (average broadband ~89.18 Mbps) makes cloud and data-driven models practical at scale.

Regulators and open-banking momentum are prompting banks and fintechs to adopt AI-driven fraud detection and secure transaction services, creating cost-savings and faster decisions that translate directly into leaner operations and better customer experiences.

“Having an index of this kind helps us move forward with sound policies and is critical for the success of these strategies... The challenges are infinite and having a tool like this is fundamental.”

Chile is building the digital heavy-lifting that will let banks and fintechs move from pilot projects to production: a Corfo‑backed call is seeding a national supercomputing push that offers co‑funding of up to US$7 million per project to universities, tech companies and AI transfer centers, while the National Data Center Plan maps out regional AI campuses powered by renewables and secure connectivity.

The University of Chile‑led SCAI‑Lab will sit on the NLHPC backbone (whose Leftraru system delivers the kind of raw throughput “equivalent to around 25 thousand notebooks working at the same time”), and a complementary CSIAA hub in Valparaíso aims to provide scalable inference services from a Tier‑III data center - together these projects (supported by Inria Chile and dozens of partners) create local capacity for training and running large models, retain specialized talent, and reduce dependence on foreign compute.

For financial firms this means lower latency for large risk models, on‑shore options for sensitive data, and easier collaboration with universities and vendors to industrialize AI workflows.

See the Corfo and InvestChile supercomputing announcement and the University of Chile SCAI‑Lab project details for how the pieces fit together.

“Countries with advanced supercomputing capabilities have demonstrated improvements of over 10% in productivity in sectors such as mining and agribusiness,” said José Miguel Benavente, Executive Vice President of CORFO.

Operational automation in Chilean financial services is already moving beyond pilots into everyday savings and faster service: AI chatbots and virtual assistants handle routine inquiries and product acquisition (often via WhatsApp), cutting call‑center volume by up to 40% and boosting conversion on conversational campaigns, while debt‑recovery flows run ~20% more effectively through messaging channels, according to regional studies on conversational banking; that momentum aligns with a broader Latin America AI‑in‑finance market growing at a projected 26.9% CAGR through 2032, meaning these gains scale quickly.

Locally, a healthy vendor ecosystem - from Elipse and Cognitiva to Kranio, which reworked Caja Los Andes' voice and WhatsApp bot with Google Dialogflow CX - lets banks pair omnichannel bots, voice AI and OCR/NLP document parsing to automate balance queries, card controls, loan intake and large‑scale form extraction, and to funnel complex cases to human experts (the result often feels like turning a packed general help queue into a few focused specialist desks).

For a market overview see the Latin America AI in Finance report and the Top Chatbot Companies in Chile directory, or read more on conversational banking use cases and metrics.

For Chilean banks and fintechs the bottom-line story is simple: global evidence and industry studies point to material cost and productivity gains that map directly onto local use cases already in production - chatbots that cut call‑center volume by up to 40% and automated debt‑recovery flows that run ~20% more effectively.

Broad studies show AI can trim operational costs by roughly 22–25% on average and that 36% of financial services executives have used AI to shave at least 10% off costs, while AI tools can process transactions up to 90% faster than traditional methods (see detailed AI in Finance statistics).

Deploying GenAI and automation also delivers software-engineering savings - Deloitte projects 20–40% reductions in bank software investments by 2028 - so investment in models and tooling can pay back in lower IT and labor overheads.

Crucially, S&P Global notes a 10% reduction in staff costs can improve return‑on‑equity by about 100 basis points and tighten cost‑to‑income by ~3 percentage points, a vivid reminder that modest efficiency gains can translate into meaningful financial impact; the 2025 AI Index and EY's industry analysis both underscore that these productivity gains are being realized as adoption scales and governance matures.

Chile's move to an Open Finance System and a proportional Fintech Law is unlocking real financial‑inclusion gains and new revenue streams: AI‑driven credit scoring and alternative‑data underwriting can turn utility payments, mobile patterns and payment histories into accept‑able collateral for people and MSMEs that lack formal credit files, while personalised robo‑advice and embedded payments create cross‑sell opportunities for banks and neobanks.

The CMF's registration and sandbox rules, plus data‑protection and AML/CTF safeguards, steer innovation toward trustworthy products, so lenders can scale responsibly while meeting consent and auditability requirements; see the Open Finance rollout and regulatory overview in the Fintech 2025 Chile guide.

Homegrown AI firms and fintechs - from Colektia's machine‑learning collections play to Xepelin's SME financing and Fintual's automated investing - illustrate how diverse business models can both broaden access and generate fee, interest and platform revenues as adoption grows (read the 2025 roundup of Chilean startups for specifics).

A vivid practical payoff: models that add even one alternative signal (rent, telecom or utility payment) can convert a previously unscorable applicant into a performing customer - expanding markets and turning inclusion into measurable top‑line growth.

Chile's funding and partnership ecosystem is tilting the odds in favor of banks and fintechs that need AI talent and real pilots: national research grants from ANID foster university collaborations and equipment access, Start‑Up Chile's Female Founder Factor offers an equity‑free 15 million‑peso boost plus mentoring and network entry, and CORFO's Early Stage Tech Fund and High Tech Program provide go‑to‑market support, lab infrastructure and non‑reimbursable co‑funding that scale projects beyond prototypes - while the Consolidation and Commercial Expansion program can deliver up to USD 125,000 to accelerate market entry.

Together these instruments do more than write checks: they build bridges to universities, vendors and mentors so teams can convert prototypes (think explainable credit‑denial models or OCR/NLP document parsing pipelines) into audited, bank‑ready services; read the government grants overview for program details and see how explainable credit decisions and document parsing are already practical building blocks.

The result is a pay‑it‑forward talent pipeline where funded projects become training grounds for specialists and a vivid payoff - one modest grant can be the spark that turns alternative data into a deployable credit signal for underserved customers.

Robust governance and RegTech are becoming the backbone that lets Chilean banks and fintechs scale AI safely: automated regulatory monitoring, transaction surveillance, eKYC and audit‑ready reporting turn manual, error‑prone chores into repeatable digital workflows that free compliance teams to focus on judgment and strategy.

Local initiatives - from Chilean regtechs like Ceptinel and the government's Contraloría privada app to regional vendors catalogued in the RegTech universe - show how data management, real‑time monitoring and automated filings can be woven into legacy systems to reduce risk and cost; see a practical overview of RegTech in Latin America and how AI augments compliance analytics and supervisory cooperation.

Applying AI in RegTech makes compliance a source of insight, not just expense: machine learning sharpens anomaly detection, shortens reporting cycles and builds immutable audit trails that support human‑in‑the‑loop reviews (important for explainable credit denials and CMF fairness requirements).

The “so what?” is simple and vivid - imagine a digital watchdog that flags suspicious flows in seconds instead of waiting for an overnight manual review, preventing fines and turning compliance into a competitive advantage for Chilean firms looking to scale responsibly.

“Regulatory compliance is fundamental to ensuring that financial services firms build trust with customers, contribute towards market stability, and improve their service offerings.”

Move from promise to production by treating AI rollout as a staged pathway: pilot in innovation labs, validate with controlled tests and fintech partners, then harden integrations for core systems and compliance.

Chilean banks already use labs to evaluate far‑looking tech - running large‑scale, controlled testing and even combining a fintech with a digital factoring solution to create new services - so begin with narrowly scoped experiments that prove a business metric before scaling (see Bci Labs' approach).

Parallel pilots should explore Open X/Open Finance opportunities - for example, projects that connect APIs, payments and sustainability data to extend credit and services to rural farmers - because those pilots reveal real payment volumes and downstream revenue potential.

Finally, validate models on production data and cloud platforms that support inference at scale: local use cases such as Kredito's AI risk model show how a focused production model can measurably improve payment prediction, so tie model metrics to credit outcomes, compliance checkpoints and rollback rules before wide release.

The most practical path is incremental, partnership‑driven, and tightly monitored: small experiments that prove value, then rapid but governed scale‑up.

“To take a quantum leap, you really need to go five or seven years into the future and see those things that make you think ‘hey, banks don't have this yet; it's not used by the financial sector but it could be if I plug it in somehow.' A disruption,”

Chile's technology stack for practical AI in finance looks like a hybrid mix of global platforms and local know‑how: proven building blocks - Document AI, Contact Center AI and anomaly detection - can be deployed alongside conversational and inference services to automate onboarding, speed underwriting and harden fraud/AML pipelines; see how Google Cloud AI in Banking solutions package document processing, predictive models and contact‑center tooling for exactly these tasks.

Global case studies and vendor toolkits (IBM's generative‑AI playbook, for example) show how banks use GenAI to summarize regulatory filings, draft credit memos and automate SARs while keeping human oversight in the loop - a pattern that maps directly to Chilean priorities like explainable decisions and compliant workflows (IBM generative AI in banking playbook).

Local implementations then plug in: OCR/NLP pipelines and explainable credit‑denial patterns let lenders convert stacks of loan docs into decision‑ready summaries in seconds and produce audit‑ready rationales that satisfy CMF fairness expectations (see Nucamp's notes on Nucamp AI Essentials for Work explainable credit denials), a vivid payoff for operations-focused AI adoption in Chile.

Chile's path from promising pilots to measurable cost‑savings is clear: combine the nation's strong connectivity and growing on‑shore compute with staged, sandboxed experiments, robust explainability and tight data governance so models cut costs without creating regulatory risk.

Firms should treat AI rollouts as incremental business projects - prove metrics in controlled tests, embed human‑in‑the‑loop checkpoints, and tie models to audit trails - while tracking the region's move to risk‑based rules and multistakeholder governance (see a concise overview of Latin America's AI proposals).

Upskilling product, compliance and ops teams is equally practical: short, job‑focused courses such as Nucamp's AI Essentials for Work bootcamp accelerate promptcraft and responsible deployment skills.

With the ILIA index highlighting Chile's infrastructure lead, the “so what?” is immediate - a single explainable model (or one added alternative data signal) can turn an unscorable applicant into a performing customer and fund the next phase of scaled automation; the next steps are disciplined pilots, compliance‑first operations, and focused talent investment.

“the introduction of this legislation aims to ensure that artificial intelligence is an instrument for people and a positive force in society and that its ultimate purpose should always be to increase human welfare.”