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Eighty-eight percent of Brazilian banks already use generative artificial intelligence in their operations, according to the Febraban Banking Technology Survey 2025. If you work in corporate finance, the chances are high that your competitors are part of that group. The question, then, is no longer “whether” to adopt AI for finance: it is understanding what it does, where it delivers real results, and what to evaluate before implementing it.
This article answers exactly that. You will find the main applications of AI in the finance department, up-to-date market data, the most common mistakes when adopting it, and a look at the next frontier: autonomous AI agents that are already changing the way companies run their day-to-day financial operations.
How to use AI for finance?
AI for finance is the set of artificial intelligence technologies, such as machine learning, natural language processing, and generative models, applied to processes, decisions, and operations in the financial sector. The scope ranges from automating repetitive operational tasks to supporting high-level strategic decisions, such as risk analysis, cash flow forecasting, and real-time fraud detection.
What differentiates AI from traditional automation is straightforward: automation executes fixed rules; AI learns from data. A traditional automation system posts an expense automatically when a field is filled in. An AI-powered system identifies whether that entry conflicts with the company’s internal policy or contains signs of fraud, even in situations the rules did not anticipate.
According to the KPMG Global AI in Finance Report, 71% of companies worldwide already use AI in their financial operations, with 41% applying it at a moderate or high level. In Brazil, 58% of companies are in the implementation phase, and 15% have already reached maturity, according to the national cut of the same study.
Why adopt now: the landscape in Brazil in 2026
The Brazilian AI market is growing at an accelerating pace. Spending on artificial intelligence in the country is expected to grow 30% in 2025, surpassing US$ 2.4 billion, driven primarily by generative AI and autonomous agents, according to IDC Predictions Brazil 2025. Seventy-eight percent of Brazilian companies plan to increase their investments in the technology by the end of this year, according to an IBM/Morning Consult survey of more than 2,400 IT decision-makers across 13 countries.
These numbers matter for a concrete reason: 48% of companies in Brazil that have already adopted AI report a positive return on investment (ROI). McKinsey estimates that companies that do not adopt AI may lose up to 20% of their revenue potential over the next twelve months, due to lower operational efficiency and an inability to personalize offerings.
There is a competitive window open. The financial sector, with its combination of high volumes of structured data and high-cost repetitive processes, is one of the most fertile environments for achieving rapid results with AI. Those who implement strategically now will gain a meaningful head start.
Where AI operates in finance: concrete applications
Accounts payable and receivable automation (AP/AR)
AI reads and processes invoices without manual data entry, automates payment reminders, and continuously reconciles payments. When a delay occurs, AI agents can step in to assess the situation and autonomously negotiate installment plans, with well-defined business rules, without human intervention.
The result is more predictable cash flow and a finance team that moves away from operational work to focus on strategy. It is estimated that AI automation can cover up to 60% of finance department routines.
NextAge’s AI agents integrate with ERPs and CRMs to execute the complete AP/AR workflow: from invoice processing to payment alert dispatch, without manual intervention. Learn more about our agents.
Intelligent bank reconciliation
Rather than manually comparing entries, AI automatically identifies matches between bank statements and internal records. It learns from the company’s patterns, detects inconsistencies, and flags errors that would go unnoticed in a manual review.
The direct impact is on the monthly close: processes that used to take hours or days are now completed in minutes.
Fraud detection and prevention
Brazil recorded 3,468,255 attempted digital banking fraud cases in the first quarter of 2025 alone, according to Serasa Experian. Banks with AI-based anti-fraud systems have reduced losses by up to 40% compared to rule-based models.
Machine learning models analyze hundreds of variables in real time: behavioral patterns, geolocation, device fingerprinting, and transaction history. Detection happens before the damage occurs, not after.
FP&A: predictive financial planning and analysis
AI crosses internal data with external macroeconomic indicators (interest rates, exchange rates, sector-level default rates) to generate cash flow forecasts that are more accurate than any spreadsheet model. Scenario simulations that used to take hours are now generated in seconds.
The benefit for the CFO and the FP&A team is direct: less time assembling data, more time interpreting results and supporting business decision-making. AI does not replace the financial analyst; it repositions them as a strategist.
Automated financial reporting
Generative AI produces management reports, income statements, and personalized dashboards automatically, with clear visualizations and descriptive analysis. For companies with multiple units or branches, this eliminates the manual consolidation work that is one of the biggest bottlenecks in the accounting close.
According to an IMF survey, 64% of Brazilian companies still perform accounting activities manually. That figure represents both a problem and an opportunity: there is enormous room for improvement for those who automate.
Compliance and RegTech
Regulatory compliance in Brazil is complex: Central Bank, CVM, LGPD, Basel III. AI continuously monitors transactions to identify violations, automatically generates regulatory reports, and classifies documents for audit purposes, reducing the operational burden on compliance teams.
With Brazil’s National Data Protection Authority (ANPD) stepping up enforcement in 2025, compliance automation is not merely an efficiency gain: it is risk management.
Credit analysis and risk management
ML models analyze hundreds of variables simultaneously to enable more accurate credit decisions, reducing default rates and minimizing the blocking of legitimate customers (false positives). This directly impacts revenue and the customer experience, especially for companies with large account bases.
There is also a financial inclusion angle: AI allows the evaluation of profiles historically underserved by traditional scoring methods, expanding portfolios with controlled risk.
Benefits: what changes in your finance function
The gains from AI adoption in finance are distributed across three dimensions:
Operational efficiency: reduction in costs associated with manual back-office processes (reconciliation, account opening, document analysis, report generation). According to KPMG, this automation can represent savings of hundreds of millions of reais per year for large institutions.
Decision quality: AI models outperform traditional methods in tasks involving large volumes of variables: more accurate credit analysis reduces default rates; more robust fraud detection lowers losses; dynamic pricing optimizes margins in real time.
Scale without linear cost: AI agents grow with the volume of operations without requiring proportional hiring. An operation that processes 10,000 invoices per month with AI can process 100,000 with the same resources.
Trends: what comes next
The financial sector no longer debates whether to adopt AI. The debate now is about which architecture, at what pace, and with what level of governance.
The most relevant trends for the next two years:
Autonomous agents at scale. If 2024 was the year of experimentation, 2026 is the year of large-scale implementation. Agents capable of operating across multiple systems with minimal supervision will become part of the financial infrastructure of competitive companies.
AI + Open Finance. Data sharing via APIs, driven by Open Finance in Brazil, accelerates the personalization of financial products. Combined with AI, it enables hyper-personalized recommendations at the precise moment the customer needs them.
Customer hypersegmentation. Financial institutions will stop treating generic customer groups and start understanding individuals. Models that analyze transactional behavior identify the exact moment for a credit, insurance, or investment offer, increasing conversion and reducing friction.
Generative AI for advanced FP&A. Scenario simulations, sensitivity analysis, and financial projections generated by generative AI will become standard in large organizations, replacing lengthy planning cycles with continuous, adaptive processes.
RegTech growth. As the regulatory environment becomes more demanding, AI-driven compliance automation will shift from a competitive differentiator to an operational requirement.
NextAge develops, integrates, and continuously evolves AI agents for the financial operations of mid-sized and large companies. If you want to understand how to apply AI in your finance function with security, governance, and measurable results, our specialists are available for a no-commitment conversation. Talk to NextAge →
Frequently asked questions about AI for finance
What is AI for finance?
AI for finance is the application of artificial intelligence technologies, such as machine learning, natural language processing, and generative models, to processes and decisions in the corporate financial sector. It covers everything from the automation of operational tasks (reconciliation, AP/AR, reporting) to support for strategic decisions (credit analysis, cash flow forecasting, fraud detection).
Which financial processes can be automated with AI?
The main ones are: invoice processing and accounts payable/receivable, bank reconciliation, financial report generation, fraud detection, credit analysis, compliance monitoring, and financial planning (FP&A). Processes with high volume, low variability, and high error cost are the best starting points.
What is the difference between an AI agent and a financial chatbot?
A chatbot answers questions and executes simple actions based on predefined commands. An autonomous AI agent operates proactively, executing complete workflows across multiple systems (ERP, CRM, databases) with minimal human supervision. In finance, an agent can identify an overdue invoice, assess the customer’s profile, choose the appropriate collection approach, and log the result automatically.
Do I need to replace my ERP to use AI in finance?
No. Well-architected AI solutions integrate with your existing stack via APIs and connectors, without requiring system replacements. Integration with ERPs such as SAP, TOTVS, and Oracle is a standard part of any serious AI for finance project.
Is AI in finance safe from a data protection standpoint?
Yes, provided it is implemented with the right governance. This means having clear legal bases for data processing, anonymization mechanisms, privacy impact assessments (DPIA), and channels for data subjects to request review of automated decisions, as required by Article 20 of Brazil’s LGPD. Compliance must be built in from the architecture phase, not added later.
How long does it take to implement AI in finance?
It depends on the complexity and the process chosen. Point automations (invoice processing, reconciliation) can be operational within eight to twelve weeks. Projects involving autonomous agents integrated with multiple systems, with governance and model training, typically require three to six months for the initial cycle, with continuous evolution afterward.
What are the main benefits of AI for the CFO?
Greater cash flow predictability, faster financial close, reduced operational errors, more accurate credit and risk decisions, automated compliance, and a finance team operating strategically rather than operationally. In terms of ROI: 48% of Brazilian companies that have adopted AI already report a positive return, according to IBM.
