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Artificial intelligence (AI) is reshaping the way the financial sector operates, from risk analysis to the automation of complex processes. More than a trend, AI has become a strategic tool for financial institutions that want to increase their efficiency, reduce costs and offer personalized experiences. Within this scenario, the use of AI agents for finance has been gaining ground as a practical and accessible application for companies of all sizes.

AI Software Development in the Financial Sector

Creating AI-based solutions in the financial context requires robustness, security, and adaptability. Developing this type of software requires an architecture that is prepared to handle large volumes of data, continuous learning, and the ability to provide accurate insights.

In addition, systems need to be able to handle sensitive data, integrate with multiple sources (such as banks, brokerages, and ERPs), and adapt quickly to regulatory changes in the industry. Flexibility and modularity are core elements of any AI architecture for finance.

Integration with Existing Infrastructures

Much of AI’s success in the financial sector depends on its integration with legacy systems. This includes internet banking platforms, CRMs, payment gateways, and compliance tools. Using NoCode platforms such as make up or N8N allows you to create effective connections without the complexity of traditional development.

By the way, if you want to experience in practice how to integrate financial flows with AI, No-Code Start-Up provides a free N8N course with full video on YouTube. It's a great opportunity to explore real automations and understand how to structure secure and intelligent integrations in an accessible way.

With this approach, banks and fintechs can activate intelligent flows based on real data, such as automatic sending of alerts, personalized segmentations and recommendations based on consumer behavior.

Challenges in AI Development for the Financial Sector

Despite the enormous potential, there are challenges that need to be considered. Among the most relevant are:

• Data quality: models are only effective if fed by clean and organized data.
  
• Explainability: It is essential to understand how the AI arrived at a particular recommendation.
  
• Cultural resistance: Traditional teams may resist adopting automation and algorithm-based decisions.
  

As highlighted by Deloitte, the combination of data governance, team training and ethical monitoring of AI is essential to mitigate risks and generate consistent results.

Security and Regulatory Compliance

The financial sector is one of the most regulated in the world. Therefore, all AI applications must comply with standards such as LGPD, GDPR and Central Bank regulations.

The adoption of good practices Data Privacy by Design, end-to-end encryption and role-based access control are just some of the basic requirements. Platforms such as Xano offer robust infrastructure with a focus on security for those who want to develop financial backends with AI.

Software Scalability and Resilience

As AI becomes a critical part of operations, it is necessary to ensure that systems are scalable and resilient. This means being able to grow as demand dictates, without compromising performance or security. Cloud computing and the adoption of microservices are essential strategies in this journey.

Companies like Goldman Sachs and Bank of Brazil have already demonstrated, in different contexts, how AI models can be deployed gradually, safely testing hypotheses before scaling to the entire operation.

AI Agents for Finance: Use Cases and Applications in the Financial Sector

1. Automated credit analysis

Companies like Credits use AI to evaluate hundreds of variables — including banking history, spending habits, and public data — to offer personalized credit. This reduces default rates and expands access to credit in a fairer way. According to McKinsey, automation can reduce analysis time by up to 70%.

2. Fraud prevention

O Bradesco and other institutions have implemented machine learning models that detect fraud based on behavioral patterns. When a transaction deviates from the pattern, the system triggers an automatic block or sends an additional verification to the user. According to Visa, the use of artificial intelligence helps prevent fraud totaling approximately US$14T25 billion.

3. Automated investment management

Robo-advisors like the ones from XP Investments use algorithms that analyze investor profiles, financial goals and market conditions to assemble and rebalance portfolios autonomously. CB Insights highlights that these systems are democratizing access to quality financial services, previously restricted to large investors.

4. AI-powered customer service

O Itau has incorporated AI into its digital channels, allowing customers to renegotiate debts, request second copies of bills or consult invoices using natural language. This reduces response time, improves customer experience and frees up human teams for more complex cases. According to Accenture, up to 80% of first-level banking interactions can now be automated using artificial intelligence.

5. Cash flow forecast

Financial management startups use AI agents for finance that integrate data on accounts payable and receivable, seasonality and market trends to predict cash flow for the coming months with high accuracy. Based on this information, more assertive decisions can be made. Harvard Business Review reinforces that this approach reduces the margin of error in financial projections and improves strategic planning.

The Role of AI Agents for Finance

Among all the applications, the AI agents for finance stand out for their versatility and accessibility. They function as intelligent “copilots”, performing tasks such as:

• Automatic generation of financial reports
  
• Sending alerts about targets or deviations
  
• Predictive profitability analysis
  

Using platforms such as Dify and OpenAI, it is possible to configure these agents with natural language, making them easier to use even for those without technical training. This expands access to data intelligence in the financial sector.

The Future of AI in the Financial Sector

Artificial intelligence in the financial sector is no longer a distant promise — it is present in strategic decisions, customer service, and risk management. The adoption of technologies like AI agents for finance represents a leap forward in digital maturity. As technical challenges are overcome and platforms become more accessible, companies of all sizes will be able to use AI not only to automate, but to evolve.

Organizations that master the use of AI ethically, safely, and strategically will be ahead in delivering value and conquering the market. The future of finance is predictive, integrated, and data-driven — and it starts now. Want to learn how to build your own AI-powered financial agents without coding? Access the AI Agent Manager Training and discover the most practical way to apply all this in your context.

How AI is changing the market can be observed in practically all sectors of the economy, and this change is intensifying every day. Artificial intelligence (AI) is being recognized as a disruptive force that is profoundly reshaping the global market. From simple tasks to complex decisions, it has been integrated into processes in various sectors, transforming the way people work, consume and manage businesses.

Furthermore, when observing the effects of this transformation, it becomes clear how much the job market is being reconfigured: new opportunities arise, some professions lose ground and others adapt or are reborn with the support of technology, which demonstrates how AI is changing the market in a broad and profound way.

How AI is changing the job market

AI is accelerating the automation of repetitive and operational tasks. AI systems are already being used to efficiently perform:

• Customer service with chatbots.
  
• Predictive data analysis for sales and marketing.
  
• Automated financial and audit processes.
  
• Inventory control and logistics.
  

These changes not only reduce operational costs, they also require the workforce to be retrained for new roles, which reinforces how AI is changing the job market with great intensity.

Professions affected by artificial intelligence

According to PwC's report on the future of work (source), it is estimated that up to 30% of human tasks could be automated by the mid-2030s. This data shows, in practice, how AI is changing the job market and skills requirements.

Some of the roles most impacted by AI include:

• Telemarketing operators
  
• Administrative assistants
  
• Data Analysts (some tasks being replaced by generative AI)
  

On the other hand, new functions emerge, such as:

• Prompt Engineers
  
• Automation experts with NoCode
  
• Conversational Experience Designers
  
• Intelligent Agent Managers
  

Those AI agents, for example, have been increasingly used in companies seeking to automate decisions and perform tasks with minimal human intervention. According to an analysis of the The Verge, large companies such as OpenAI, Google and DeepMind are investing heavily in the development of these systems, which can already act independently in complex corporate processes. They are designed to operate autonomously, learn continuously and integrate with other technologies — which makes them key players in the ongoing digital transformation.

What's Happening Now: How AI is Changing the Marketplace in Numbers

The AI market is experiencing exponential growth. The sector is estimated to surpass US$ 500 billion in value by 2027. There is a global race for innovation, with startups, large companies and governments investing heavily in:

• Generative models (like ChatGPT)
  
• Robotic Process Automation (RPA)
  
• Artificial intelligence applied to health, education, law and agribusiness
  

This movement demonstrates how AI is being positioned as a strategic asset for growth and competitiveness.

Suggested reading:

AI Agent and Automation Manager Training

What are the negative aspects of AI in the job market?

Despite promising advances, important challenges also arise:

• Structural unemployment: functions terminated without sufficient time for requalification
  
• Digital inequality: not everyone has access to technological education
  
• Technological dependence: companies become hostages of platforms and algorithms
  
• Ethical and privacy issues: inappropriate use of data and biased automated decisions
  

These factors require public policies, business leaders and civil society to debate limits, transparency and responsibilities in the use of technology.

Opportunities and the future of work with AI

The key is in the conscious adaptation. The future of work will be driven by:

• Continuous learning and professional requalification
  
• Mastery of AI tools and NoCode platforms
  
• Creating new business models based on data and automation
  
• Development and management of autonomous AI agents
  

Increasingly, professionals and companies will need to adopt a stance proactive and experimental, turning AI into an ally.

Recommended courses:

• Agents with OpenAI Course
  
• Make Course (Integromat)
  

AI and entrepreneurship: new market frontiers

Artificial intelligence is not only transforming the traditional job market, it is also paving the way for new business models. Digital entrepreneurs are using AI to create scalable products such as intelligent assistants, recommendation systems, and data-driven SaaS platforms. No-code tools combined with AI agents are enabling the emergence of lean, highly automated, and highly personalized startups.

A great example is the AI-based micro-SaaS, which solve very specific problems and can be created by a single person. Platforms like Bubble, FlutterFlow and Make, integrated with OpenAI models, make this scenario not only possible, but accessible.

For those who wish to explore this new territory, we recommend SaaS IA NoCode Training, designed to transform ideas into digital products using the power of artificial intelligence.

How AI is changing the market and shaping the future

Artificial intelligence is changing the market in an irreversible way. It is not only a technological revolution, but also a social, professional and economic transformation. The question is no longer “if” AI will impact your work, but “how will you position yourself in this new era”.

The good news is that there have never been so many accessible tools for those who want to learn AI in practice.

See all No Code Start Up training and courses

Do you still waste hours reading long articles, emails or documents? Artificial Intelligence (AI) can do it for you in seconds. AI tools AI to summarize texts are revolutionizing the way we study, work and produce content. With the advancement of technology, it has become possible to delegate the reading and synthesis of large volumes of information to intelligent systems, allowing you to focus on what really matters: making decisions based on accurate and relevant information.

In this article, you will discover how to summarize texts with AI, what are the best tools, practical examples and how to automate this task with no-code platforms, even if you don't have advanced technical knowledge.

Recommended Reading

• AI Agents on WhatsApp: New Trend for 2025
• How to automate tasks with Make
• Manus AI Agents See How the Chinese Platform Works

What is an AI for Text Summarization?

Imagine having to read a document that is dozens of pages long and needing to capture the essentials in just a few minutes. This is exactly where summary AIs come in. They are like those attentive coworkers who know exactly what to filter out in a sea of information. With each paragraph you read, these digital assistants separate the essential from the accessory, creating a summary that makes sense to you. And the best part is that they do this with the naturalness of someone who understands our way of thinking, highlighting precisely what is worth reading. This makes our routine easier, more productive and organized.

The use of AI allows for greater efficiency when dealing with long texts, providing a more dynamic and optimized reading experience. And the best part is that you can shape these tools to your liking: it’s as if you were teaching the AI to speak your language and that of your audience. Do you want a more direct summary for executives? Or something more detailed for students? With small adjustments to the settings, it’s as if you were giving personalized instructions to an assistant that understands exactly what you want.

What Types of Text Can AI Summarize Well?

• Articles and blog posts
• Corporate reports and emails
• Meeting or video transcripts
• PDFs and academic documents

These are just a few examples. AI can also work with texts in different languages, respecting cultural and linguistic contexts. This makes it a valuable tool in international corporate environments and academic institutions.

Top AI Tools for Text Summarization

ChatGPT

ChatGPT can be used with custom prompts or plugins to generate summaries with different styles and levels of detail. For example, you can configure a prompt to summarize academic articles into bullet points, adapt corporate reports to more objective language, or even generate executive summaries from meeting minutes. Additionally, with memory functionality or API integration, ChatGPT can be incorporated into automated workflows where it learns from feedback and adjustments, making each summary more aligned to your needs.

QuillBot

QuillBot offers a dedicated tool for summarizing sentences or paragraphs, ideal for academic texts and articles. It also allows you to adjust the level of detail in your summary, which is great for those who need a quick overview or a more in-depth summary. QuillBot also includes additional features such as paraphrasing and a grammar checker, which makes it an even more complete tool for those who work with large volumes of text.

SMMRY

SMMRY is a simple, online solution that focuses on condensing texts into a few user-adjustable sentences. What sets this tool apart is its minimalist approach, ideal for those who need a quick, straight-to-the-point summary. You can control the number of sentences you want in the final result and adapt the tool to remove specific sentences, such as those containing certain keywords or quotes.

Resoomer

Resoomer is designed for argumentative and academic texts, with support for multiple languages. The tool is especially useful for those who need to analyze long texts with a clear logical structure, such as essays, dissertations, and legal articles. Resoomer allows you to quickly identify the main arguments, central ideas, and conclusions, making dense texts easier to understand. In addition, it offers browser integration to summarize web content in real time, which is a benefit for researchers and students.

Scholarcy

Scholarcy makes it easy to summarize scientific articles and also generates keywords and study sheets. This tool is especially effective for those who work with academic publications, as it not only condenses the content but also highlights important sections such as objectives, methodology, results and conclusions. Scholarcy also allows the extraction of tables, figures and references, organizing this information in a quick-read format. Integration with reference managers such as EndNote and Zotero is a plus for researchers.

Zamzar Summarizer

Zamzar Summarizer allows you to convert and summarize files such as PDF and DOCX in a simple and straightforward way. The great advantage of Zamzar is its ability to handle a wide variety of file formats, offering not only text summarization but also conversion between formats such as TXT, HTML, and EPUB. This makes it ideal for professionals who deal with documents on different platforms and need to integrate them into a single digital workflow. In addition, the tool can be used without the need for installation, directly from the browser, which further speeds up the process.

Notion AI

Notion AI is ideal for Notion users, making it easy to summarize documents and take notes within the app itself. In addition to summarizing text, Notion AI also allows you to rewrite paragraphs, generate headings, and create lists from text content. Integrated directly with your pages and databases, Notion AI streamlines the workflow for teams using the platform to manage projects, documentation, and ideas.

Automating Summaries with AI and No-Code

Use AI to summarize texts goes far beyond manual tools. By combining no-code platforms like Make with robust AI, you can completely automate the process. Imagine you receive an email with a long document, and the system automatically summarizes the content and saves it to your cloud. This is productivity with AI applied intelligently.

Practical Example

“How I Built an Automatic Email Summarizer with Make + OpenAI”

In this example, you will learn step by step how to set up this automation. First, we define a trigger in Make to detect new emails with attachments. Then, we connect the flow to the OpenAI API, which processes the text and generates a summary. Finally, the summary is sent to your email or saved in a Google Doc. This solution is ideal for those who receive a lot of reports or proposals and need to evaluate them quickly.

Another interesting approach is to create a custom agent using the N8N it's the ChatGPT. With N8N, you can create more complex and adaptable workflows. For example, you can set up a workflow where the system automatically processes, via API, the documents you send to a Google Drive folder, summarizes them and classifies them by topic in spreadsheets. The agent learns from the edits the user makes, identifies text types and adjusts the summary style according to the topic, becoming more efficient with each interaction.

For those who want to create robust automations with N8N, check out our N8N Course.

Practical Tips to Improve Your Results with AI

If you are just starting to use AI to summarize texts, some good practices can make all the difference:

• Adjust the prompts: If the AI doesn’t deliver exactly what you want, refine your instruction.
• Test different tools: Not all AIs respond the same way. Explore and see which one works best for you.
• Automate when possible: Use no-code tools to create flows that save you time.
• Review summaries: Even with AI, reviewing ensures that the content is aligned with your needs.

Let's clear up your doubts?

If you still have questions about how to apply AI to summarize texts, here are some answers that may help:

What are the best AI options? to create summaries? Many users use tools like ChatGPT, QuillBot and SMMRY for their practicality and the efficient results they offer. Depending on the volume and type of text, you may want to opt for a more automated solution, such as integration with Make.

Can I automatically summarize long texts? Yes! Using AI to summarize text in conjunction with automation platforms, you can set up systems that process long documents automatically.

How to ensure that the summary is unique? Personalize prompts and always review content. While AI rewrites, human adjustments add value and avoid originality issues.
With AI, you can transform the way you deal with information on a daily basis. From automating email summaries to creating complete workflows, the possibilities are endless. Integrating these tools into your daily routine is the first step to saving time, increasing productivity, and standing out professionally.

Want to master AI automation? Get started with our Makeup Course it's the Agents Course with OpenAI.