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

Brazil's financial ecosystem is sprinting toward AI-first workflows: Microsoft's AI Tour in São Paulo highlights enterprise pilots from Petrobras to B3 that put Azure OpenAI and copilots to work for real-time insights and customer service, while Open Finance has unlocked consented data flows that fintechs now use to tailor offers.

CloudWalk's InfinitePay - with more than 5 million active merchant clients - shows how AI-powered credit scoring and automatic fee reduction can approve working capital in seconds and save merchants millions, illustrating why regulators are already shaping a risk-based AI framework for finance.

For professionals and teams looking to apply prompts and practical AI tools in this fast-moving market, short, work-focused training like Nucamp's AI Essentials for Work bridges the skills gap so staff can deploy safe, productive AI in days, not years.

“Open Finance represents a structural shift in Brazil's financial market. It gives customers back control of their own data, enabling us to offer solutions more aligned with each business's reality - with greater speed and better terms - while leveling the playing field with traditional institutions,” - Fabrício Costa, CloudWalk

To shortlist the Brazil‑focused use cases in this series, the methodology blended published suptech and innovation research with practical vendor and pilot write‑ups: Cambridge SupTech Lab's analysis on AI in financial supervision guided the risk‑and‑regulatory lens (prioritising explainability, human‑in‑the‑loop and risk‑based supervision), while Cambridge Judge/CCAF insights on AI and personalisation helped surface customer‑facing prompts such as tailored credit offers and transaction recommendations; national context came from Brazil's official AI strategy to ensure alignment with local governance, skills and ethical pillars.

Selection criteria were simple and pragmatic: measurable impact for Brazilian FSIs, regulatory readiness, evidence of vendor or hyperscaler pilots (e.g., transaction‑level and treasury automation) and technical feasibility given regional infrastructure constraints.

Emphasis went to use cases that move the needle - fraud and suptech detection, alternative‑data credit scoring, real‑time payments and LLM‑assisted reporting - because, as the SupTech Lab notes, AI can compress days of manual supervisory analysis into seconds, a “so what?” that translates directly into faster decisions and lower operational cost for banks, fintechs and regulators alike; sources were read for empirical examples, governance guidance and infrastructure implications before final inclusion.

“AI poses unprecedented challenges for all of us. For central banks, one can see increased powers to monitor price and financial stability with the help of AI. At the same time, there is a possibility that AI might take over key decisions on price and financial stability and challenge what has been so far the “art” of central banking: a reliance on many models…Your generation will have to reflect on these new challenges sooner than later.”

Risk assessment in Brazil is shifting from exclusionary bureau scores to rich, AI‑ready signals that turn digital footprints into credit access: providers like RiskSeal alternative-data Brazil platform ingest 400+ real‑time data points from local platforms to score previously “unscorable” applicants, while industry pieces show AI and alternative data can lift approval rates by double‑digit percentages for underbanked groups; that matters because roughly 35 million Brazilians remain outside the formal system even as some 182 million are online, creating a huge opportunity to underwrite credit with device, telco, utility and behavioral signals.

Practical wins are already documented - from case studies reporting ~25% approval uplifts with AI‑driven models to vendor claims of up to 32% higher approvals using behavioural scores - so risk teams can both expand book growth and manage losses by layering explainable ML with permissioned transaction and psychometric inputs.

For lenders building pilots, the Brazilian student‑lending story is a vivid proof point: psychometrics, Open Banking and alternative feeds can convert thin files into repeatable, lower‑risk customers while regulators and risk committees map governance around explainability and data consent (see more from Equifax alternative-data guidance for credit risk and the Pravaler interview on alternative data and psychometrics).

“Alternative credit data came to complement our standard credit analysis. Currently, we use alternative data in about 30% of the received proposals, and we could increase our approval in 10% with its use.”

For Brazilian banks and payment processors that must protect instant, high-volume flows, real-time fraud detection is no longer optional - stream processing and anomaly detection stitch together events as they happen so teams can stop attacks before money moves; platforms like Ververica's Unified Streaming Data Platform show how Complex Event Processing (CEP) can correlate sequences (back-to-back high-value spends, rapid-fire micro‑tests, “impossible travel”) and apply in‑flight rules with no downtime (Ververica real-time fraud detection using Complex Event Processing (CEP)).

Lightweight, SQL-first stacks such as Tinybird illustrate how ingest → analysis → API publication lets fraud engines block or flag transactions in milliseconds (Tinybird real-time fraud detection architecture guide), while low‑latency stores like Redis are designed to serve risk scores and dynamic identity profiles at sub‑millisecond rates for Brazil's real‑time payments and merchant platforms (Redis Enterprise fraud detection solution for low-latency risk scores).

The so‑what: when detection windows shrink from hours to milliseconds, institutions can preserve customer experience and cut losses by stopping fraud the moment patterns emerge.

“Using Redis Enterprise in our fraud-detection service was an excellent decision for our organization. It is enabling us to easily manage billions of transactions per day, keep pace with our exponential growth rate, and speed fraud detection for all of our clients.” - Ravi Sandepudi

Brazilian finance teams are already using large language models to turn raw P&L, balance‑sheet and cash‑flow tables into board‑ready commentary, compliance narratives and fast audit trails - a shift that can compress days of manual write‑ups into minutes: Crediflow's fine‑tuned LLM, for example, claims it can produce a comprehensive credit paper in under three minutes, automating ratio calculations, risk flags and a credit recommendation in lender‑ready format (Crediflow fine-tuned LLM for financial statement analysis and credit papers).

Prompt engineering and multimodal LLMs can also let analysts ask natural‑language questions of stored statements - the n8n workflow shows how a chat front end can route queries to P&L or balance‑sheet databases and return precise, tabular answers (n8n chat-based P&L and balance-sheet workflow with GPT-4 and PostgreSQL) - while vendor guides demonstrate practical patterns for hypothesis testing, ratio interpretation and narrative generation that regulators and audit teams can validate (Amazon Bedrock guide to prompt engineering for financial statement analysis).

For Brazilian FSIs this means faster investor decks, consistent credit memos, and Portuguese‑ready narratives that preserve auditability and speed decision cycles without sacrificing oversight.

“We investigate whether an LLM can successfully perform financial statement analysis in a way similar to a professional human analyst. We provide standardized and anonymous financial statements to GPT4 and instruct the model to analyze them to determine the direction of future earnings. Even without any narrative or industry-specific information, the LLM outperforms financial analysts in its ability to predict earnings changes…”

Receivables optimisation in Brazil is fast becoming an AI story: AI‑driven collection assistants shift teams from reactive chasing to prescriptive cash management by predicting who will pay, prioritising high‑impact accounts, and automating cash application and personalised dunning.

Platforms like Gaviti AI-driven accounts receivable platform advertise tangible improvements - up to a 30% reduction in DSO and cash‑application accuracy above 90% - while professional services deployments have recorded durable gains (an EY case study reports a 22% reduction in the accounts‑receivable period after introducing an AI collection assistant, see EY case study on AI-powered collection assistant).

For Brazilian banks, fintechs and corporates this means smarter prioritisation (the single invoice that unlocks next week's payroll surfaces first), fewer manual matches, faster dispute routing and healthier liquidity - with agentic assistants and predictive models turning routine follow‑ups into strategic cash‑management actions that scale across the country's fast‑moving credit ecosystem.

In Brazil's evolving AML landscape, graph analytics and explainable machine learning are fast becoming the forensic microscope compliance teams need: network‑based models can collapse sprawling ownership records and payment rails into visual linkages that expose layering and mule chains, while explainable ML provides the audit trail regulators insist on when suspicious activity reports go to COAF. That matters because the FATF/GAFILAT mutual evaluation (Brazil MER 2023) flags gaps in beneficial‑ownership transparency and coordination between authorities - weaknesses that graph methods help compensate for by surfacing indirect relationships across corporate registries, virtual‑asset flows and cross‑border payments (FATF/GAFILAT Brazil MER 2023).

Vendors and ecosystems are already packaging typologies and shared repositories so banks can deploy proven detection patterns quickly; Tookitaki's AFC approach, for example, couples a typology library with ML models so institutions in Brazil can spot new laundering schemes sooner and tune rules with lower false positives (Tookitaki AFC ecosystem).

The practical payoff is clear: instead of thumbing through noisy alerts, investigators can follow a single flagged node and, in minutes, reveal a compact network - turning opaque spreadsheets into an actionable spiderweb for faster reporting, prosecution and asset recovery.

Contract analysis and regulatory reporting are prime use cases for LLMs and NLP in Brazil's financial sector: automated clause extraction and playbook enforcement let teams surface indemnities, auto‑renewals, and data‑protection clauses across thousands of agreements in minutes, turning a 50‑page contract into a one‑page executive summary so compliance officers can act before a deadline slips.

Tools that combine entity extraction, OCR and micro/macro NLP (see LexCheck clause extraction NLP explanation) make it practical to map obligations, generate audit‑ready reports and feed structured metadata into supervisory filings, while human‑in‑the‑loop workflows ensure legal sign‑off and continuous model improvement as Ontra and ContractPodAi automated contract data extraction describe.

For Brazilian FSIs this means faster regulatory responses, consistent Portuguese‑language summaries, and auditable redlines that trace each suggested edit back to a playbook or precedent - a capability that converts sprawling repositories into a searchable risk register and shrinks review bottlenecks for deal teams and compliance units (read more in ContractPodAi's automation guide and LegalFly 2025 AI contract review tool comparisons).

Customer support automation in Brazil is moving beyond FAQ bots to hybrid conversational assistants and agent copilots that shave minutes off resolution times, scale 24/7 support and free skilled agents for complex work - capabilities that matter in a market already pushing cloud, Pix and Open Banking.

When designed with a clear purpose and human‑in‑the‑loop handoffs, chatbots improve response time, handle higher volumes and cut costs, as best practices from the Infosys conversational banking playbook explain; specialised vendors and banks report concrete gains such as around‑the-clock handling and dramatic query volumes, with agents stepping in for edge cases so customers never hit a dead end.

Practical risks - privacy, bias and oversight - mean supervision and reliable escalation paths are essential, but the upside is vivid: large virtual assistants like EVA have answered millions of queries and manage tens of thousands of conversations daily, proving bots can both keep service windows open at 3 a.m.

and funnel urgent cases to humans for fast remediation (see the Aivo chatbot benefits overview, the Spyro‑Soft chatbot risk review, and CFPB chatbot adoption projections).

Tomasz Smolarczyk, Head of Artificial Intelligence

Personalized financial planning and robo‑advisory in Brazil marry recommendation engines with strict local guardrails: algorithmic portfolios and chatty planning assistants can scale tailored advice, but regulators require that investment managers and consultants register, operate under written contracts and uphold suitability, disclosure and anti‑fraud duties - rules spelled out in the Brazilian guidance on investment advisers (Brazil Investment Adviser Regulation guidance) and implemented through CVM resolutions that emphasise product–client fit and transparency (see Brazil CVM regulation overview on suitability and registration).

Practical rollout must also embed AML/KYC controls and data‑privacy by design: LGPD consent, breach notification and processing limits shape what personal data robo‑advisors may ingest, how profiles are stored, and whether a DPO or impact assessments are needed (Brazil LGPD data protection law and ANPD guidance).

The so‑what is immediate - a recommendation engine that ignores CVM suitability bands risks regulatory sanctions and client harm, so design choices (model explainability, consented data sources, clear contract terms and audit trails) decide whether a robo‑advisor is a scalable advantage or a compliance exposure in Brazil (Brazil CVM regulation overview on suitability and registration).

Trading desks and portfolio teams in Brazil are increasingly adopting Aladdin‑style signal stacks - disciplined, quantitative pipelines that turn price data, macro nowcasts and alternative inputs into tradable signals and automated rebalances - so portfolio managers can move from intuition to rules‑based action without losing governance.

These approaches draw on the same ideas behind quantitative investment strategies: signal generation, back‑testing, and factor premia, while strategic asset-allocation frameworks (MVO, factor-based, risk-parity and tactical overlays) provide the guardrails for long‑term stewardship and stress‑testing.

The so‑what: in an emerging‑market episode - an FX shock or a rate pivot - a timely predictive signal can shift exposure in minutes, turning a potential drawdown into a managed repositioning and preserving liquidity for local obligations; success still depends on robust risk limits, careful signal vetting and rigorous testing across Brazilian market microstructure and liquidity conditions.

Smart contract security in Brazil's DeFi scene hinges on the basics - input validation, CEI (checks‑effects‑interaction), throttling, circuit breakers and regular static/dynamic scans - plus a new layer of urgency: AI‑scale attackers that can discover subtle economic flaws and execute an exploit in a single, atomic transaction before human teams can react.

Best practice toolchains (static analysers like Slither/MythX and fuzzers) and independent audits remain essential, as vendors advise, but defenders must also assume adversaries use automated scanners and mempool‑watching bots; the CISO playbook now recommends mandating advanced, AI‑augmented audits and on‑chain monitoring to spot attack patterns early (Delta6Labs smart contract security guide for DeFi risks and best practices).

The so‑what: a single unchecked vulnerability can become a flash‑loan heist in seconds, so Brazilian teams should couple rigorous code audits with emergency stop and speed‑bump patterns and real‑time AI defenses to keep liquidity, reputation and customer trust intact (analysis of AI‑driven DeFi attacks and smart contract exploit techniques).

Brazilian financial institutions can capture real value from AI only by pairing ambition with guardrails: White & Case's tracker shows Bill No. 2,338/2023 and the ANPD‑led framework already embedding risk classification, algorithmic impact assessments and logging requirements, so banks and fintechs must bake compliance into design rather than retrofit it (White & Case Brazil AI regulatory tracker).

At the same time, industry signals - like the survey findings that tie responsible AI to stronger ROI - underscore that governance, cross‑functional alignment and basic AI literacy are business priorities, not optional extras (Survey: responsible AI unlocks ROI in financial services).

That pragmatic stance - risk‑aware pilots, impact assessments, explainability and staff upskilling - lets institutions scale inclusion and automation safely, turning routine tasks into fast, auditable workflows and preserving customer trust; for teams wanting practical, work‑aligned skills, short courses such as Nucamp AI Essentials for Work bootcamp offer a structured way to operationalize prompts, governance and human‑in‑the‑loop patterns so Brazil's FSIs can move from experimentation to regulated, measurable impact.

“Tech-led solutions lack strategic nuance, while AI-led initiatives can miss infrastructure constraints. Cross-functional alignment is critical.”