Top 5 Jobs in Financial Services That Are Most at Risk from AI in Brazil - And How to Adapt

Brazil's financial sector is at a tipping point: global investment in AI is surging (RGP projects AI spending in financial services to hit about $97 billion by 2027), and lawmakers are accelerating oversight as legislative attention to AI climbs worldwide (RGP report: AI adoption in financial services 2025, Stanford HAI AI Index 2025 report).

For Brazilian banks and insurers, the upside is concrete - hyper-automation and GenAI can shrink back-office cycle times dramatically and boost fraud detection, while risk analysis and predictive credit scoring are already widening access to credit across Latin America - but the flip side is real: opaque models, algorithmic bias and data-privacy risks under LGPD demand strong governance.

Practical safeguards - LGPD-aligned controls, human-in-the-loop review, and vendor vetting - are essential if AI is to deliver inclusion without harm (LGPD governance and human-in-the-loop safeguards for financial services in Brazil).

Brazilian finance workers who pair adoption with reskilling will be best positioned to turn disruption into opportunity.

Selection prioritized financial roles most exposed to automated decisioning, high-volume data flows, and regulatory friction in Brazil's shifting digital landscape: jobs that are heavily rules-based (back-office processing, teller transactions), reliant on customer data assembled via Open Banking APIs, or involved in screening and credit decisions where AI/ML is already replacing manual judgment.

Criteria drew directly from Brazil's evolving policy signals - the Ministry of Finance 2024 proposal and the stalled Bill No. 2,768/2022 highlight how “systemically relevant” platforms, algorithmic transparency, mandatory interoperability, and data-portability rules can reshape who controls customer data and which tasks are automatable (see the Ministry of Finance 2024 proposal).

Roles were also scored for AML and compliance exposure given the Central Bank's risk-based AML rules and the growing use of AI for transaction monitoring. Particular attention went to scale and data control as risk multipliers - the low BRL 70 million revenue threshold in earlier drafts illustrates how quickly a provider can fall under strict obligations - and to tasks that can be made redundant by well-governed APIs and machine learning models.

The final short list balances technical susceptibility to automation with practical reskilling pathways for Brazilian finance workers. For background on the regulatory and data-sharing foundations used in this assessment, consult the Ministry of Finance proposal and Brazil's Open Banking rules.

“Open Banking, in the view of the Central Bank of Brazil, is considered the sharing of data, products and services by financial institutions and other authorized institutions, through the opening and integration of information system platforms and infrastructures, in a secure, agile and convenient manner.”

Bank Teller (Agente de Caixa): the classic, face-to-face role is built on a bundle of routine, rules-based tasks - processing deposits, withdrawals and transfers, handling cash and balancing drawers, answering customer questions - that make it one of the most automatable jobs in a branch network; a clear overview appears in a typical Bank teller job description - Corporate Finance Institute.

When day-to-day throughput is reducible to software and standard operating rules, intelligent kiosks and remote channels can absorb much of the volume, so the tellers who thrive will be those who pair transactional accuracy with stronger customer-service and problem-resolution skills.

Reskilling pathways that emphasize digital customer support, transaction exception handling and basic AML triage turn cash-handling experience into durable, higher-value work - see the detailed duties and cash-control expectations in this Bank teller responsibilities and job template - Betterteam.

Picture the audible clack of rolled coins and the careful, to-the-cent balancing of a cash drawer - those tactile skills can be reframed into oversight and exception-management roles as branches modernize.

Back-office Operations Clerk (Operador de Back Office): routine reconciliation, document processing and transaction-posting are precisely the kind of high-volume, rules-based work that RPA and AI are eating into fastest - Brazilian banks are already ramping up, planning a 61% jump in AI, analytics and big-data spend to 1.8bn reais (and a 59% rise in cloud migration to 3.13bn reais) in 2025, signaling automation at scale (BNamericas report on AI and cloud investment in Brazilian banks (2025)).

FEBRABAN's Tech 2025 outlook reinforces the momentum with R$47.8 billion in sector tech investment, driven by Pix volumes, Open Finance and mobile-first flows that push more work into centralized digital pipelines (FEBRABAN Tech 2025 financial sector technology outlook (analysis)).

The consequence is tangible: up to 11% of traditional banking roles were already automated by AI in 2025, primarily in back-office tasks, so the image to keep in mind is not pink slips but overnight batch jobs and dashboards replacing stacks of paper (AI in banking statistics and automation impact (Coinlaw)).

The practical response is reskilling toward exception-handling, regtech oversight, data-quality roles and hybrid RPA+human workflows - areas where domain knowledge still beats a rules engine and where career value can be preserved as banks chase efficiency.

Credit Analyst (Analista de Crédito): this role sits at the crossroads of data, judgment and regulation - assessing capacity to pay using tools like the Central Bank's Credit Information System (SCR) and bureau scores that in Brazil typically run on a 0–1000 scale - so the job is both highly automatable and uniquely sensitive to nuance (Credit Information System (SCR), Brazil's evolving credit-bureau regime).

Automation and ML can crunch payment histories and positive-data streams faster than any person, but lenders are already layering new signals - SME-focused scores such as the Quod‑FICO solution - and alternative, digital‑footprint inputs (RiskSeal's 400+ data points from local platforms) to expand approvals and refine risk models (Quod Score PJ PME by FICO, alternative data for Brazilian lenders).

The practical challenge for analysts is to shift from rote scoring to oversight: validating model inputs, flagging bias or LGPD issues, interpreting edge cases, and explaining why a score moved - picture a dashboard where a single new payment or ride‑share record flips a borrower's score and demands a human sign‑off.

Those who learn model governance, positive‑data interpretation and explainability will turn automation into a career accelerator rather than a displacement risk.

“As we expand into additional countries, FICO brings a depth of global analytic and scoring experience to enable faster, more precise credit decisions. With the introduction of Quod Score PJ PME by FICO, we continue to safely expand credit access for small and medium-sized businesses in this key region,” says Alexandre Graff, vice president and general manager of FICO Latin America and the Caribbean.

Insurance underwriters (Subscritores de Seguros) are the gatekeepers who turn messy human details - medical histories, financial statements, driving records and lifestyle flags - into a price and a yes/no decision, setting premiums that reflect the likelihood of a claim; Western Southern lays out how underwriters judge medical and financial risk to decide terms and rates (What is underwriting and how it works).

In Brazil, the routine cases increasingly flow through automated engines, while complex or borderline files still demand a human's judgment - Kaplan notes that automation handles common policies but underwriters intervene for nuanced decisions and to review software recommendations (What underwriters do).

The practical pivot for workers is clear: keep sharp analytical skills, learn to validate model outputs and explain adverse decisions, and treat every sudden new data point - a single abnormal blood test or a revealed hazardous hobby - as the moment that moves a file from “standard” to “rated.” That blend of rule-based review, portfolio thinking and clear documentation is what preserves underwriting as a resilient career even as insurers speed up processes with data and AI.

Financial Customer Service Representative / Call Center Agent (Atendente de Atendimento Financeiro): in Brazil's banks and insurers, routine account questions, password resets and basic transaction checks are already prime fodder for chatbots and IVRs, with GenAI powering smarter sentiment analysis and 24/7 virtual assistants that deflect high volumes while tailoring replies to customer intent (ISG report on Brazilian contact centers and GenAI).

That shift doesn't mean humans vanish - rather, agents are being asked to handle the “hard” moments: escalations, compliance-sensitive verifications, fraud flags and emotionally charged complaints that bots can't fully resolve; Calabrio finds 61% of centers seeing more difficult interactions even as 98% use AI, so training in emotional intelligence and real‑time AI copilots becomes the ticket to staying relevant (Calabrio State of the Contact Center 2025 report).

Capgemini's analysis adds that banks can turn contact centers into value creators by combining conversational AI with agent assistance to lift personalization and cross-sell - picture a system that auto-summarizes a customer's recent transactions for a reprepared agent the moment a frustrated tone is detected, turning a near‑lost customer into a resolved, loyal one (Capgemini 2024 report on intelligent bank contact centers).

The practical pathway: learn copilot workflows, master explainable responses for customers, and specialize in exception handling and LGPD‑aware data practices so automation becomes a springboard, not a dead end.

“GenAI still has vast potential that has yet to be tapped. Providers and enterprises will continue to invest in it to improve productivity and customer experience.” - Jan Erik Aase, ISG

The bottom line for Brazil's finance workers: automation is real but reskilling is the smart response - generative AI may affect 31.3 million jobs in Brazil, with 5.5 million in the highest‑exposure group, so transformation is far likelier than wholesale job loss (LCA 4Intelligence study on generative AI job impact - Valor); at the same time, a national, risk‑based AI framework (Bill No.

2,338/2023) is pushing institutions to adopt impact assessments, transparency and human oversight, meaning compliance and explainability skills will be a differentiator (Brazil AI regulatory tracker - White & Case).

Practically, the fastest way to shift from vulnerability to advantage is targeted, work‑focused training: courses that teach prompt design, model governance and AI‑assisted workflows turn routine tasks into supervised, higher‑value work - for example, Nucamp's AI Essentials for Work bootcamp - Nucamp (15-week) focuses on prompts, foundations and job‑based AI skills so finance professionals can read alerts, validate models and resolve exceptions instead of being displaced; imagine a branch where a single anomalous transaction lights up a compliance dashboard and a trained human steps in to interpret it - that's the new job.

“Most occupations include tasks that still require human involvement, which suggests that job transformation is the most likely outcome of generative AI, rather than full automation.” - Bruno Imaizumi (LCA 4Intelligence study, cited in Valor)