The Complete Guide to Using AI in the Financial Services Industry in Brazil in 2025

Brazil's financial services landscape is rapidly shifting as AI moves from pilot to production: investments in AI and generative projects are forecast to exceed BRL13 billion by 2025, while the federal PBIA earmarks roughly BRL23 billion for 2024–28 to build infrastructure (including the Santos Dumont supercomputer) and Portuguese-language models.

Banks and fintechs already use machine learning for credit scoring, fraud detection, customer segmentation and algorithmic trading, but the regulatory backdrop is changing fast - Bill No.

2,338/2023 (approved by the Senate in December 2024 and now before the Chamber) adopts a risk-based approach and proposes ANPD-led oversight - so firms must pair innovation with LGPD-aligned DPIAs, human-in-the-loop safeguards and bias testing.

For a clear legal primer, see Chambers' Brazil AI guide and White & Case's AI Watch tracker, which track sectoral duties, risk classes and enforcement trends that every Brazilian FSI leader should know.

The near-term future for AI in Brazil's financial services looks less like a distant sci‑fi forecast and more like a fast‑arriving infrastructure and regulatory moment: generative models and machine learning are set to drive hyper‑personalised banking experiences and productivity gains, supported by investments expected to top BRL13 billion by 2025 and the PBIA's BRL23 billion programme that even backs the Santos Dumont supercomputer and Portuguese‑language models; for a practical read on how Brazil's finance sector is reshaping itself, see Microsoft analysis: Brazil redefining finance and Chambers guide: Brazil AI legal trends and duties.

Market signals reinforce the surge - fintech AI revenues were about US$192.78M in 2024 with long‑term forecasts rising to US$650M by 2035, and specialist “AI agents” in finance are projected to grow rapidly through 2030 - but scaling these gains will depend less on raw horsepower than on governance: Bill No.

2,338/2023's risk‑based rules, ANPD guidance and existing LGPD obligations make DPIAs, human‑in‑the‑loop safeguards, bias testing and clear contractual warranties non‑negotiable; the memorable takeaway is simple - Brazilian banks can build next‑gen, ultra‑personal services, but only if models are as auditable as the ledgers they touch.

Brazil's 2025 industry outlook is one of fast, capital‑heavy scaling where public and private bets are converging: investments in AI and generative projects are expected to top BRL13 billion this year and PBIA's BRL23 billion programme is underwriting infrastructure and Portuguese‑language models, while market forecasts signal steep growth for both generative systems and specialised AI agents - Grand View Research projects Brazil's generative AI market to surge from roughly US$109.8M in 2024 toward hundreds of millions by 2030 and expects AI agents in financial services to reach about US$119.7M by 2030 with a blistering 43.7% CAGR (2025–2030), so banks should plan for rapid productisation alongside tightened oversight; for a legal and policy snapshot see the Chambers practice guide on Brazil's AI trends and developments and for market detail see Grand View's Brazil outlook.

The practical takeaway: demand for data‑centre capacity, cloud services and compliant model governance will spike together, turning what was experimental proof‑of‑concept work into regulated, revenue‑generating platforms - a shift that makes investments in explainability, LGPD‑aligned DPIAs and human‑in‑the‑loop controls business‑critical, not optional.

AI now underpins the core of Brazil's financial workflows - from onboarding and credit decisions to the three pillars of fraud defence: onboarding, transaction monitoring and offboarding - turning what used to be slow, manual checks into millisecond decisions that protect customers and reduce friction.

Leading banks and insurers deploy real‑time ML, behavioral biometrics, graph analysis and document‑forgery detection to spot mule‑accounts, synthetic IDs and deepfakes: Bradesco's SAFER on the FICO Platform analyzes nearly 1 billion PIX transactions monthly and scores up to 25 million PIX payments a day with ~50 ms decisioning, cutting transactions held for review by 89% and enabling instant account openings that grew new accounts ~11% (see Bradesco case study).

Sectorwide projects - like CNseg's SAS deployment - improved suspicious‑claim accuracy by 67% and multiplied alerts, while Central Bank rules and BCB Normative Nº 491 force device registration and R$200/R$1,000 caps on unregistered devices, shifting some detection upstream into device controls and UX design (read Feedzai's breakdown).

The practical image to remember: in Brazil's real‑time payments economy, effective AI must not only spot fraud but act faster than a scam completed in 24 hours, or even 24 minutes.

“Bradesco is more than a bank; we are a technology-driven financial institution that continuously evolves to meet customer needs.”

Bill No. 2,338/2023 - approved by the Senate on 10 December 2024 - lays out a risk‑based national AI framework that will touch every stage of model development and deployment in Brazil: developers and deployers must run preliminary risk classifications and, for high‑risk or systemically risky general‑purpose/generative systems, perform algorithmic impact assessments, implement bias‑mitigation, transparency and logging measures, and ensure data‑management and explainability safeguards; the National Data Protection Authority (ANPD) is charged with coordinating the National System for Regulation and Governance of Artificial Intelligence (SIA), while sectoral regulators retain powers to target specific uses.

The Bill also bans excessive‑risk practices (social scoring by authorities, certain real‑time remote biometric ID and other harms), creates a regulatory sandbox, and advances creator protections that require disclosure of copyrighted materials used in training and mechanisms for remuneration or opt‑out - all under strict enforcement (fines up to BRL 50 million or 2% of turnover) and staggered timelines (many rules 730 days after publication; generative/general‑purpose and copyright provisions 180 days).

The practical lesson for Brazilian FSIs is clear: scale AI, but bake in AIAs, human review and LGPD‑aligned governance from day one - see the Senate‑approved Bill summary at the Brazil AI Act overview (Bill 2,338/2023) and White & Case AI Watch: Brazil regulatory tracker for the evolving legislative details.

“With the passage of the AI Regulatory Framework (Bill 2338/2023), the Brazilian Senate has positioned Brazil at the forefront of the global debate. This is a significant step forward for protecting copyrights and Brazilian intellectual production. There is still much work ahead in the Chamber of Deputies (Brazilian Lower House), but the Senate has set an example and fulfilled its role in defending Brazilians' fundamental rights.”

Implementation in Brazil means engineering for scale, interoperability and regulatory hygiene from day one: start by wrapping legacy cores with a robust API layer and microservices approach so new ML models and consent flows can access data without risky rewrites - tools that auto‑generate APIs from mainframes speed this work while cutting time‑to‑market (see OpenLegacy's legacy‑to‑API modernization approach).

Secure, standards‑based auth and data flows are non‑negotiable: implement the Open Finance security profile (OAuth2/OIDC with PKCE, MTLS, PAR, JARM), signed JWT payloads and encrypted JWE tokens to meet payload‑integrity and non‑repudiation requirements described in Brazil's Open Banking specs and SecureAuth guidance.

Design for real‑time resilience and observability - Pix's national scale (over 6 billion monthly transactions and single‑day peaks above 250 million) means low latency, autoscaling, chaos‑tested services and end‑to‑end logging for auditability.

Pair technical choices with consent management, fine‑grained access controls and LGPD‑aware data handling so models can be audited and DPIAs produced; foster partnerships with cloud, API‑management and specialist integrators to share implementation risk rather than rebuilding every stack.

Finally, bake human‑in‑the‑loop checks, explainability hooks and continuous monitoring into CI/CD pipelines so model drift, bias and fraud signals are caught before they affect customers - practical engineering that treats compliance and customer experience as two sides of the same architecture.

“Following the evolution of open insurance in Brazil, we have seen Insurance companies discussing how important it is to put their customer in the center. We have to create the best experience we can for customers and reduce complexity for them.”

Brazilian firms must treat data governance as a business imperative, not a compliance afterthought: the LGPD sets GDPR‑style principles (purpose, necessity, transparency and accountability), requires controllers to keep records, appoint a public DPO where appropriate, and enables data‑subject rights including review of automated decisions, while the ANPD enforces security and breach rules plus technical standards - so expect DPIAs for high‑risk processing, strict breach notifications (controllers must notify the ANPD and affected individuals within three working days when risk is likely), and steep penalties (fines up to BRL 50 million or 2% of turnover).

Practical controls include anonymization/pseudonymization with documented re‑identification risk assessments (ANPD guidance), encryption, access logging, and retention limits; international transfers are tightly constrained and now governed by new ANPD transfer rules (including standard clauses to be implemented by August 2025), so cloud and cross‑border architectures must bake in contractual safeguards and demonstrable technical measures.

The memorable takeaway for Brazilian finance teams: design models and pipelines so explanations, DPIAs and audit logs are retrievable on demand - because LGPD, ANPD guidance and the new transfer/security rules turn explainability and incident traceability into competitive as well as regulatory requirements (see the DLA Piper LGPD guide and ANPD anonymization study summarized by Mayer Brown).

“Anonymized data shall not be considered personal data, for purposes of this Law, except when the process of anonymization to which the data were ...”

Brazil's operational‑resilience story in 2025 is a race between rapid innovation and relentless attackers: the Pix rails that power instant transfers have made speed a feature and a vulnerability - QED Investors estimates R$10.1 billion in fraud losses in 2024 and notes that 61% of scams close within 24 hours - so a single vendor‑credential compromise can cascade quickly into national headlines, as happened when attackers used legitimate IT credentials to target Sinqia and attempt a $130 million heist via Pix (access later suspended by the Central Bank).

Those incidents underline why the government is embedding AI into national cyber strategy and why firms must shift from siloed defenses to shared, AI‑enhanced detection, threat hunting and rapid containment: see the Security Office's roadmap on AI and regulation and the new industry response to fast fraud.

Private sector moves mirror this shift - PwC Brazil and Feedzai launched a Center of Excellence to deliver AI‑native fraud prevention at scale - while innovators are pairing behavioral biometrics, graph networks and blockchain‑backed identity proofs to harden onboarding and destination‑side controls.

The practical takeaway for FSIs is clear: resilient operations now mean zero‑trust vendor controls, callable forensic playbooks, cross‑institution signal sharing and AI pipelines that can stop a scam in milliseconds, not hours.

"People don't understand what AI demands from the IT environment. So, we need governance over people, scope and data. We need to manage risks, understand [those risks] to achieve the awards, [and] we need to balance what risk we are going to take."

Practical, Brazil‑centric case studies show a clear roadmap: start by platformizing first‑party data and moving proofs‑of‑concept into reliable, observable systems - Nubank's talks on platformization and its Hyperplane acquisition illustrate how foundational models and MLE teams can operationalize personalization and decisioning at scale; pair that with lightweight copilots and RAG for internal search and agent workflows to cut friction quickly.

Invest in engineerable “plumbing” (APIs, model serving, CI/CD and monitoring) so AI productivity gains aren't one‑off toys but repeatable features, and favor outcome‑based vendor arrangements where possible as Nubank leaders suggest; reskill DS/MLE staff into roles that coach product teams and own production quality rather than merely prototyping.

Don't underestimate the infrastructure mandate: Brazil's instant‑payments and Open Finance stack (Pix and large Open Finance adoption) mean solutions must be built for national scale and interoperability.

For pragmatic inspiration, read Nubank's operational lessons on moving models to production and team design in their Purple MinDS series and the broader fintech infrastructure context in The Financial Revolutionist's look at Brazil's Pix and Open Finance surge.

“AI can be the vehicle that will lead us to expand this love even further, either by enhancing products, services, or by reducing costs for the customer.”

For beginners eyeing AI in Brazil's financial services, start small and build governance into every step: learn the LGPD and the risk‑based expectations of Bill No.

2,338/2023 (ANPD guidance and algorithmic impact assessments are already the bar), pick focused, high‑value pilots such as fraud detection or credit scoring that Ax Legal flags as early wins in finance, and design each pilot so DPIAs, human‑in‑the‑loop reviews and explainability are retrievable on demand; policy caution is real - automation has already produced fast, problematic outcomes (the INSS case of an automatic denial in six minutes is a sharp reminder).

Take advantage of Brazil's public push - the PBIA and national plans are funding infrastructure and Portuguese‑language models - while investing in practical skills and vendor contracts that force data‑provenance warranties and audit rights (see the legal snapshot at Chambers' Brazil AI guide on trends and developments).

Finally, get hands‑on: a short, workplace‑focused course like Nucamp's AI Essentials for Work bootcamp - practical AI skills for the workplace teaches prompts, tools and DPIA‑aware workflows so non‑technical product owners can move pilots into compliant production without reinventing the stack.

“Instead of waiting for AI to come from China, the U.S., South Korea, Japan, why not have our own?”