Rafaela - Luby Software

Why following tech and business events is strategic for your company

Staying current with market trends and new technologies is essential to driving business growth. Tech and business events create opportunities for leaders, companies, and professionals to exchange knowledge, share experiences, and explore solutions to real-world challenges. By attending these events, you meet people with shared interests, gain insight into innovations that are shaping industries, and expand your understanding of the market. They’re also a great way to discover how to apply technology in practical, strategic ways while building connections that may lead to valuable partnerships. The value of specialized events Industry events are more than just networking opportunities. They indicate where industries are headed, which technologies are gaining traction, and which trends could directly impact your industry. Whether you’re a startup leader, a corporate executive, or a solo entrepreneur, attending these conferences is essential for: Spot trends early: Learn about technologies that are gaining traction and how they could affect your market. Build meaningful connections: Meet people facing similar challenges or driving innovations you can adopt. Validate your strategies: Compare your approach to industry peers and gain confidence in your next moves. Expand your expertise: Deepen your understanding of topics through case studies, discussions, and workshops. These events are not just about finding solutions but also offering ways to refine your focus in a rapidly changing environment. Top US tech and business events you shouldn’t miss CES (Consumer Electronics Show) Hosted annually in Las Vegas, CES is one of the most recognized global tech events. It showcases cutting-edge innovations across a variety of industries, including AI, IoT, sustainability, and electric mobility. From major players to startups, CES is the place to discover where technology is heading and how it intersects with business opportunities. SXSW (South by Southwest) Interactive Held in Austin, Texas, SXSW Interactive is known for its blend of technology, culture, and business. The event attracts diverse audiences, ranging from tech developers to corporate leaders, and explores topics like emerging tech, media, and the future of work. Its cross-industry approach makes it unique and valuable for those seeking innovation beyond their immediate sector. AWS re:Invent Amazon Web Services’ flagship conference in Las Vegas is a must for anyone working in cloud computing, machine learning, or scalable technologies. With a strong focus on practical applications, AWS re:Invent offers technical sessions, hands-on workshops, and networking opportunities tailored for professionals who drive digital transformation within their organizations. Global opportunities for broader perspectives While US-based events are central to the technology and business calendar, there’s value in exploring international conferences as well. Events such as Web Summit (Lisbon), Future Blockchain Summit (Dubai), or Slush (Helsinki) highlight regional perspectives on global challenges. These gatherings can help you better understand how different markets approach innovation, from cryptocurrency regulations to AI-driven solutions. Expanding your reach beyond US borders allows for a more comprehensive understanding of the forces shaping the global economy. How to make the most of these opportunities To maximize the impact of your participation: Set clear goals: Identify whether you’re looking for partnerships, knowledge, or exposure. Research the agenda: Prioritize sessions that align with your business needs or areas of expertise. Engage strategically: Reach out to speakers, exhibitors, or attendees you’d like to connect with. Reflect and implement: Post-event, take the time to evaluate insights gained and apply them to your strategies. Simplify your planning with our list With so many tech and business events to choose from, it can be challenging to decide where to invest your time. To help, we’ve created a detailed resource featuring the most important technology and business events in the US and abroad. This free, curated list includes event details, dates, and links to help you plan your attendance efficiently. Industry events are opportunities to refine your expertise, expand your network, and gain a competitive edge. Whether you focus on US-based conferences or explore global options, the knowledge and connections you gain can drive meaningful changes in your organization. Download our event list to ensure you don’t miss the opportunities that matter most.

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Maximizing hyper-productivity with ChatGPT Agents

Artificial Intelligence / Rafaela

Businesses are under constant pressure to deliver results faster and with fewer resources. Artificial intelligence, particularly ChatGPT agents, has emerged as a practical solution to this challenge. These AI tools are transforming industries by automating tasks, analyzing data, and improving decision-making processes. According to recent studies, companies that implement AI-powered solutions report a 40% increase in efficiency in key operations. ChatGPT agents stand out as adaptable and collaborative tools capable of delivering measurable results across a variety of use cases. Understanding ChatGPT Agents ChatGPT agents are AI models designed to handle specific tasks, from answering questions to analyzing data. Their flexibility allows them to adapt to various business needs, such as customer support, generating insights, or optimizing workflows. Unlike traditional AI systems, which are often rigid and limited to predefined tasks, ChatGPT agents adapt to different situations. While rule-based agents follow strict instructions and narrow AI tools focus on single functions, ChatGPT agents combine flexibility and contextual understanding. This flexibility makes them more effective for businesses. For example, a ChatGPT agent managing customer inquiries can quickly resolve FAQs, escalate complex cases, and offer personalized recommendations. These capabilities go beyond automation, providing smarter and more effective solutions. Why use multiple AI Agents? The key advantage of using multiple AI agents is their ability to work together as a coordinated system. Think of it as a team of specialists, each handling a specific task —that’s the concept behind multi-agent orchestration. This approach ensures better efficiency and outcomes by sharing data and insights. For example: Customer Support Agent: Responds to inquiries, directs tickets, and manages customer satisfaction. Data Analysis Agent: Processes large datasets to provide clear and actionable insights. Marketing Agent: Designs and implements personalized marketing campaigns. Automation Agent: Handles repetitive tasks like scheduling and invoice generation. When orchestrated effectively, these agents can transform productivity, allowing teams to focus on strategic initiatives while AI handles the operational load. How does orchestration work? Orchestrating AI agents involves assigning distinct roles and enabling seamless collaboration through APIs and integration tools. Key steps include: Define clear roles: Each agent is assigned a specific task, ensuring there’s no overlap or redundancy. Enable data sharing: Agents exchange information in real-time, improving accuracy and efficiency. Optimize workflows: By automating routine processes, agents free up valuable human resources for creative problem-solving. For example, a Data Analysis Agent can provide insights to a Marketing Agent, which then adjusts campaign strategies based on that information. This type of coordination ensures operations run efficiently and achieve better results. Real-world applications of AI Agents Improving customer experience AI agents have changed customer support by making quality assurance (QA) more efficient. They analyze conversations across channels, languages, and teams, giving real-time insights into performance. These agents also create after-call summaries, help train support teams, and reduce the time spent on follow-ups, leading to a smoother experience for customers. These tools also create post-call summaries, train agents, and reduce follow-up times, delivering a smoother customer experience. Enhancing internal operations AI-powered tools in payroll management simplify processes by spotting discrepancies and automating repetitive tasks. This reduces manual effort, improves accuracy, and allows teams to focus on higher-value activities. Companies using these tools often see faster workflows and fewer errors, boosting overall efficiency. Driving R&D efficiency AI agents speed up product prototyping by running simulations and refining designs more quickly. Teams can complete development cycles faster, which reduces the time it takes to launch new products. By automating repetitive tasks, AI also frees up resources for innovation and improving product quality. Scaling decision-making AI agents provide executives with real-time dashboards that combine financial, operational, and customer data. These tools help leaders make informed decisions more quickly and respond to challenges with confidence, improving overall organizational agility. Why this matters for hyper-productivity When businesses integrate AI agents into their workflows, they unlock a new level of hyperproductivity. By automating routine tasks, optimizing complex processes, and enabling smarter decision-making, AI agents free teams to focus on innovation and growth. At Luby, we’ve seen this firsthand. By orchestrating ChatGPT agents in legacy system modernization projects, we’ve delivered unparalleled results, cutting project timelines by up to 50% while improving accuracy and customer satisfaction. How to get started with AI Agents If you’re ready to embrace hyper-productivity, follow this roadmap: Identify key challenges: Start by defining the problems you want to solve. Customize the Agents: Adapt AI tools to fit your specific needs using APIs and integrations. Encourage collaboration: Use orchestration platforms to connect agents and share data efficiently. Monitor and refine: Regularly assess how the agents are performing and make updates as needed. The future of hyper-productivity AI agents are transforming industries such as fintech and healthcare, demonstrating their versatility and impact in a wide range of applications. Gartner predicts that by 2025, companies using AI-driven orchestration will achieve a 25% increase in operational efficiency. At Luby, we believe the potential of orchestrated AI agents is only beginning to be realized. By adopting these tools, businesses can expand more efficiently, drive innovation, and deliver enhanced value to their stakeholders. What’s your next step toward hyperproductivity? Let’s explore together how AI agents can transform your operations and help you achieve your goals.

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AI Agents as a Service: redefining automation with continuous and adaptive solutions

Artificial Intelligence / Rafaela

With the rise of artificial intelligence (AI) technologies, a new approach is transforming how companies automate processes and deliver services: AI Agents as a Service. These solutions represent the future of automation, offering real-time adaptation and the ability to evolve to meet the dynamic demands of various sectors continuously. According to Grand View Research, the global AI market is projected to reach $1.8 trillion by 2030. In this article, we’ll explore the impact of AI agents on automating complex workflows, improving decision-making, and promoting operational efficiency. What are AI Agents as a Service? AI Agents as a Service combine artificial intelligence with service-based business models to create adaptable and scalable solutions. Unlike traditional systems, which follow fixed rules, AI agents are designed to learn continuously from data, events, and interactions. These agents act as intermediaries between the customer and the system, managing complex tasks autonomously. In a bank, for example, an AI agent can adjust risk strategies in real time based on market conditions. In a logistics center, it can optimize delivery routes by taking into account current traffic and the availability of resources. Benefits of AI agents for companies The adoption of AI agents offers several competitive advantages, including: 1. Automation of complex workflows AI agents can take on repetitive or time-consuming tasks, freeing up human teams to focus on strategic activities. In sectors such as healthcare, they can automate appointment scheduling, while in the financial industry, they can process large volumes of data for risk analysis. 2. Improved decision-making Thanks to predictive analysis and machine learning, AI agents can offer recommendations based on real-time data. This is especially relevant in sectors such as retail, where companies can personalize customer experiences on a large scale. 3. Real-time adaptation Unlike static systems, AI agents adjust their behavior based on new data. For example, in an e-commerce scenario, they can dynamically adjust prices to compete with market promotions. 4. Scalability AI agents can be easily scaled up to meet growing demands. This is essential for companies facing peaks in activity, such as holidays or product launches. 5. Cost reduction By automating processes and reducing operational errors, businesses can save substantial resources. This translates into greater efficiency and ROI. How AI Agents as a Service are transforming the industry Finance and banking In the financial sector, AI agents are playing a key role in modernizing processes. These agents are used to detect fraud in real time, automate underwriting processes in insurance and loans, and offer virtual assistants that provide quick and efficient solutions to customers. Logistics and Supply Chain Logistics and supply chain companies are benefiting from AI agents to monitor supply chains in real time, optimize routes and deliveries, and predict demand more accurately. This helps to avoid stock-outs or overstocking, ensuring greater operational efficiency. For instance, companies using AI for route optimization report a 15% reduction in transportation costs. Retail In retail, AI agents are transforming the customer experience. They offer personalized recommendations based on user behavior, provide 24/7 customer service through intelligent chatbots, and help with dynamic price and promotion management to maximize sales and satisfaction. According to Salesforce, 73% of customers prefer businesses that personalize their offers. Healthcare Hospitals and clinics are also adopting AI agents to optimize their services. These agents help schedule appointments and manage medical records, contribute to early diagnoses based on medical data, and monitor patients remotely in real time, promoting more agile and accurate care. Is your company ready for AI agents? Integrating AI agents can be a great differentiator for your business, but how do you know if the time is right? The first step is to identify the main challenges facing your company. If there are operational bottlenecks, manual processes that could be automated, or areas where faster data-based decisions need to be made, AI agents could be the ideal solution. In addition, it’s important to consider customization. Not every AI solution is a perfect fit for every company, so working with experts like Luby’s team can help you develop a system that meets your needs. Starting small, with pilot projects, is a smart way to test the effectiveness and results before expanding to other areas. Finally, remember that the success of AI agents depends on continuous adjustments. Monitoring performance, collecting feedback, and updating models regularly ensures that the technology evolves along with your business, maximizing benefits and broadening impact. Luby’s expertise in AI Agents as a Service At Luby, we believe in the transformative power of AI to revolutionize the way businesses operate. Here, our team of experts develops personalized solutions that incorporate AI agents to solve each customer’s specific challenges. Ready to take your operations to the next level? Talk to our experts and implement solutions that not only improve your operational efficiency but also open up new possibilities for innovation.

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AI governance platforms: building trust and accountability in artificial intelligence systems

Artificial Intelligence / Rafaela

Artificial intelligence is transforming entire industries, optimizing processes and driving innovation. However, its use also brings up critical challenges, such as the risk of biased decisions, privacy violations, and regulatory compliance issues. These challenges have put companies under the spotlight of regulatory agencies and consumers demanding greater accountability and transparency in using these technologies. In this scenario, AI governance platforms emerge as the answer to balancing innovation and responsibility. They are designed to ensure that AI systems operate ethically, transparently, and in alignment with organizational values and legal standards. Why is this relevant? AI-driven decisions already affect our lives, whether in credit approval, CV selection or health diagnosis. These systems can perpetuate prejudices, compromise privacy, or break laws when poorly managed. Companies that invest in AI governance can build trust with their customers, reduce risks, and meet growing regulatory requirements. According to a report by Gartner, AI governance platforms are among the main technology trends for 2025. In addition to helping organizations manage the legal, ethical, and operational performance of their AI systems, these solutions offer benefits such as creating, managing, and enforcing policies for the responsible use of AI, explaining how systems work, managing the lifecycle of models and promoting transparency to build trust and accountability. Key components of AI governance platforms For AI governance platforms to be effective, it is essential to understand the pillars that underpin them. These components form the basis for ensuring that AI systems operate in a fair, responsible way that is aligned with organizational objectives. Ethical AI: Aims to ensure that systems operate fairly, minimizing prejudice and aligning decisions with human values. Transparency: Helps companies understand how systems make decisions, promoting clear and explainable outputs for all stakeholders. Compliance: Aligns the use of AI with current regulations and industry standards, mitigating legal risks. Good practices for implementing AI governance Implementing effective AI governance goes beyond simply adopting technological tools. It is a strategic process that requires the definition of clear policies, multidisciplinary collaboration, and continuous monitoring. According to Gartner, AI governance guidelines vary significantly between regions and industries, making it challenging to establish consistent and universal practices. This reinforces the importance of personalizing approaches to meet the specific needs of each organization. Here are some of the best practices: Define clear policies: Create ethical guidelines and compliance goals that reflect the organization’s values. Adopt specialized tools: Use governance platforms to monitor AI performance, identify risks, and ensure transparency. Collaborate across teams: Engage IT, legal, compliance, and operations professionals to align AI systems with business needs and values. Promote regular audits: Continually evaluate AI models to identify and correct ethical or operational irregularities. Invest in internal education: Ensure that all employees understand the ethical principles and regulatory effects associated with the use of AI. By adopting these practices, companies can not only ensure the ethical and responsible use of AI but also strengthen their position as trusted leaders in their markets. Building a future with ethical and trustworthy AI The path to ethical and responsible use of AI requires more than tools. It requires a commitment to clear policies, consistent practices, and an organizational culture that prioritizes transparency. As global regulations, such as the AI Act in the European Union, develop, companies that implement AI governance platforms will be better placed to meet legal requirements and build a solid foundation of trust with their customers. With constant innovation, trust in AI systems is becoming a prerequisite for the future. At Luby, we are ready to help your company shape the future of technology with responsibility and credibility. Talk to our experts and take the next step in AI governance!

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Post-Quantum Cryptography: Prepare your data for the Quantum Era

Artificial Intelligence / Rafaela

Last week, Google revealed the Willow chip, a breakthrough in quantum computing capable of solving problems that would take 10 septillion years in less than five minutes. This breakthrough validates the imminent arrival of quantum computing and intensifies the urgency of adopting solutions such as Post-Quantum Cryptography (PQC) to protect critical systems and data. While quantum computing advances, traditional cryptography based on RSA and ECC becomes vulnerable. As a result, PQC has become a necessary transformation in how companies view digital security. In today’s article, we’ll talk about how Post-Quantum Cryptography can prepare large companies to face the challenges posed by this new era and prevent their most sensitive data from being at the mercy of a quantum future. Post-Quantum Cryptography and the warning brought by Willow The breakthrough of Google’s Willow chip represents a milestone in quantum computing, showing that it’s possible to scale qubits without compromising precision. Willow solves quantum computing’s biggest obstacle: the instability caused by increasing the number of qubits, which raises error rates to impractical levels. By combining greater stability with significantly faster calculations, it paves the way for solving problems such as molecular modeling, logistical optimization, and, above all, breaking encryption at unprecedented speeds. This progress reinforces the need to adopt Post-Quantum Cryptography. More than theoretical speculation, PQC is a practical response to the quantum threat, especially in the face of the “harvest now, decrypt later” phenomenon already exploited by malicious groups. This means that data encrypted today is vulnerable to being broken by quantum computers in the future, compromising financial, health, and other highly sensitive information. For large companies, PQC becomes a proactive digital protection and leadership strategy, positioning organizations at the forefront of cybersecurity. Strategies for adopting Post-Quantum Cryptography According to Gartner, post-quantum cryptography is one of the main strategic trends for 2025. With a responsible innovation bias, PQC has emerged as the main solution for mitigating the risks associated with the vulnerability of classic algorithms, such as RSA and ECC. However, its transition requires detailed analysis, practical experimentation, and agile implementation. 1. Detailed cryptographic inventory The first step is to map the entire security infrastructure to identify where vulnerable algorithms are being used. This includes mapping digital certificates, hardware security modules (HSMs), and certificate lifecycle management (CLM) systems. This analysis is essential to ensure that these components are compatible with post-quantum algorithms, such as those standardized by NIST. A well-structured inventory facilitates the creation of a strategic plan to replace or complement these technologies with quantum-resistant alternatives. 2. Experiments with post-quantum algorithms Testing post-quantum algorithms in controlled environments makes it possible to anticipate implementation challenges and adapt systems safely. Solutions such as CRYSTALS-Kyber and Dilithium, recommended by NIST, are ideal for getting started. These algorithms offer robust key encapsulation and digital signatures, which can be integrated into pilot projects before full implementation. For specific cases, such as authentication and key exchange, lattice-based algorithms or solutions, such as SPHINCS+ are also effective options. 3. Cryptographic agility The complete transition to post-quantum cryptography will be a gradual process. Adopting a hybrid approach, combining classic algorithms with post-quantum alternatives, ensures continuous protection while new technologies are integrated. This flexibility, known as cryptographic agility, allows companies to update their systems as standards evolve, minimizing risks and disruptions. The influence of Willow on company preparedness Willow’s advance highlights the need for strategic and proactive planning. Large companies can learn from the lessons of classical computing: legacy systems are often the first to become vulnerable when new paradigms emerge. The challenge is to protect sensitive data today and to ensure that systems can adapt to future quantum advances. This preparation includes: Investing in internal research and partnerships with technology leaders: Working closely alongside quantum computing pioneers can accelerate the adaptation process. Empowering IT and cybersecurity teams: Ensure that people understand the concepts of quantum computing and know how to implement post-quantum solutions. Explore the potential of new quantum benchmarks: While RCS has no direct commercial application, it demonstrates capabilities that will eventually be applicable in areas such as financial modeling and scientific simulations. The opportunity for disruption While quantum computing presents threats, it also opens doors for innovation. Companies that lead the transition to the quantum age will have a significant competitive advantage. It’s not just about protecting data but rethinking processes, creating new business models, and using quantum capabilities to solve problems previously considered unsolvable. For example, sectors like health, manufacturing, and logistics can use quantum computing to accelerate scientific discoveries, optimize supply chains, and predict consumption patterns with unprecedented precision. To this end, it’s essential to invest in quantum technology not just as a defense mechanism, but as a strategic tool. The advance of Willow is a milestone that requires a change of mentality in the corporate world. Large companies must adopt a proactive approach, starting now to re-evaluate their security infrastructures and experiment with post-quantum solutions. If you want to get ahead, talk to one of our experts and find out how to use innovation to shape the future of your business in a secure and innovative way.

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The transformation of financial services with hyper-personalization

Inovação, Fintechs e Bancos, Inteligência Artificial / Rafaela

Hyper-personalization in the financial sector has become one of the main strategies for banks and financial institutions all over the world to enhance the customer experience to a higher standard. By combining technology with a customer-centric approach, banks are creating experiences tailored to the individual needs of each user, raising the level of engagement and increasing customer retention. With increased competition in the sector, the adoption of tools such as artificial intelligence (AI), big data, and machine learning has become essential for those who want to stand out in the market. Personalization strategy in line with customer needs Hyper-personalization goes beyond traditional personalization. Instead of simply using the customer’s name in communications or making generic recommendations based on previous purchases, it leverages real-time data and machine learning to provide offers and services that precisely align with each customer’s needs. In the banking context, this can mean offering specific loans at exactly the right time, adjusting credit limits automatically based on the customer’s financial behaviors and even predicting the need for service before the customer realizes it. How does technology support this strategy? The main factor enabling the implementation of hyper-personalization in the financial sector, through tools such as: Artificial intelligence (AI): AI is used to analyze large volumes of data in real-time and generate precise insights into customer behavior. It allows banks to predict user needs, detect patterns, and offer solutions without a formal request. Big data and data analysis: Large-scale data collection allows banks to understand customer behavior in detail, including transactions, digital interactions, and real-time feedback. Automation and intelligent chatbots: Automation via chatbots, powered by AI, offers immediate and personalized support to customers, resolving routine issues and freeing up time for human agents to deal with more complex problems. Open APIs: With the rise of open banking, APIs make it possible to integrate systems and share information securely, making it easier to offer new services without compromising data security. 5 benefits of personalization in financial services Increased customer satisfaction: Personalization makes customers feel valued, strengthening the relationship with the institution. Increased retention and loyalty: Personalized experiences increase loyalty and reduce churn. Increased revenue: Products and services offered at the right time generate more revenue. Operational efficiency: Chatbots and AI for automation reduce the workload of teams, allowing them to focus on complex issues. Fraud prevention and security: Technology increases accuracy in identifying fraud, ensuring greater security. Challenges in implementing hyper-personalization Although the technology that supports hyper-personalization is advanced, many financial institutions face barriers to implementation. Legacy systems and a lack of integration between platforms make the process challenging. For example, many banks still operate with old infrastructure that doesn’t support the real-time processing required for hyper-personalization. In addition, the integration of channels and data from different sources, such as mobile apps, physical branches, and telephone answering services, requires investment in flexible and modern architectures. Another sticking point is the issue of privacy and data protection. In an environment where regulations, such as the GDPR in Europe and the LGPD in Brazil, are increasingly strict, institutions need to ensure that the collection and use of personal data is done securely and transparently, without compromising customer trust. The adoption of solid data governance and regulatory compliance practices becomes a crucial aspect of the success of hyper-personalization. How has the financial sector used personalization? The financial sector has already realized the value of personalization in providing services and experiences. With the use of analytical data and innovative technologies, financial institutions are developing personalized solutions that meet the unique needs of each customer, promoting deeper engagement and enriching the user experience. Below, we’ll explore some use cases that not only increase customer satisfaction but also strengthen brand loyalty in an increasingly competitive market. Omnichannel experiences Creating a fluid journey between digital and physical channels is one of the greatest benefits of hyper-personalization. Financial institutions that adopt this approach ensure that the customer has a consistent experience, whether on the app, website, or physical branch. Bank of America is an example of an omnichannel experience, allowing customers to perform tasks such as depositing checks and scheduling appointments through mobile and desktop applications. Personalized offers in real-time Banks are increasingly investing in technologies that use real-time data to offer products and services at the exact moment when they are most relevant to the customer. Instead of receiving generic offers, customers benefit from personalized recommendations. In Europe, BBVA offers financial solutions that automatically adjust based on spending profiles and suggest new banking products based on previous transactions. Personalized fraud prevention Personalization is also present in financial security. With behavioral analysis, machine learning, and big data, banks identify suspicious patterns and take action before fraud occurs. This process is based on creating a unique profile for each customer, making it easier to detect anomalous activity. HSBC, for example, has implemented a fraud detection solution that, by understanding individual customer behavior, provides a proactive response to threats, preventing losses. Credit and investment automation Process automation is an area where hyper-personalization is having a substantial impact. By using AI and predictive analytics, banks offer financial recommendations tailored to each customer’s risk profile. This includes adjusting credit limits and suggesting investment products that maximize returns. XP Investimentos, for example, uses customer data to personalize investment offers, adjusting them according to individual profiles and goals. JPMorgan Chase, on the other hand, uses AI to monitor behavior and suggest credit and savings solutions according to the client’s objectives. Applications beyond the financial sector The hyper-personalization strategy, although strongly adopted by the financial sector, is also revolutionizing other industries. In retail, for example, big players like Amazon use machine learning algorithms to suggest products in real-time based on users’ shopping and browsing behavior, increasing conversion and satisfaction. In the health sector, hyper-personalization is being used to offer customized medical treatments based on genetic data and the patient’s health history, creating a more precise and efficient care

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6 steps to a successful financial solution

Leave a Comment / Banks and Fintechs / Rafaela

Launching a successful financial solution requires strategic planning, market understanding, and precise execution. In the United States, the fintech sector continues to grow, attracting $36 billion in investments by 2023, according to CB Insights. The US has consolidated its global leadership, accounting for 41% of all investment deals in the sector, the highest share since 2016. With intense competition and new startups constantly emerging, following well-defined steps is essential to attract investors and gain a solid position in this dynamic ecosystem. In today’s article, we’ll explore six essential steps to launch a successful financial solution and stand out in a dynamic and competitive market. 1. Deep market understanding Before any initiative, it is essential to understand the environment in which the fintech will operate. Detailed market research is crucial to identify unmet consumer needs and gaps in current financial services. For example, the growing demand for inclusive and digital financial services in the United States has created significant opportunities to serve unbanked or underserved populations. In addition, competition analysis is indispensable. Studying the solutions offered by direct and indirect competitors helps identify opportunities for differentiation. In a competitive market, creating a unique value proposition based on concrete data and industry insights is the first step to developing a fintech solution that stands out. 2. Clear definition of business model A well-structured business model is essential to ensure the success of a financial institution. It all starts with a clear definition of the target audience, identifying whether the solution will be aimed at B2B customers, B2C customers, or both. The global B2B payments market is projected to grow at a compound annual growth rate of 10.7% between 2021 and 2030. This reflects a growing demand for solutions that meet the specific needs of companies, especially those focused on payment automation and cash flow optimization. Developing a unique value proposition is also essential. This means creating a solution that solves specific and relevant customer problems more efficiently or affordably than existing alternatives. This clarity in the business model helps to attract investors and consumers from the earliest stages. 3. Regulatory Compliance Regulatory compliance is a critical step for any fintech. In the United States, the regulation of the financial sector is complex, being carried out by various agencies such as the Securities and Exchange Commission (SEC), Consumer Financial Protection Bureau (CFPB) and Office of the Comptroller of the Currency (OCC), which issued more than 1,200 enforcement actions between 2000 and 2020. Understanding and complying with these regulations is essential to operating legally and building credibility. Besides, the Bank Secrecy Act requires fintechs to implement solid anti-money laundering programs. Navigating this complex regulatory environment is essential to operating legally and building credibility in the market. 4. Technological development Technology is at the heart of any fintech. Investing in a secure infrastructure is a priority to protect user data and prevent cyber attacks, one of the biggest risks in the sector. Platforms based on technologies such as blockchain or cloud computing are increasingly popular to ensure scalability and security. In addition, an efficient user experience (UX) is indispensable. Intuitive interfaces that prioritize ease of use and accessibility can be a key differentiator in the market. According to a PwC survey, 32% of global customers stop doing business with a brand they love after just one bad experience, underscoring the critical role of delivering a seamless and satisfying user experience in financial services. 5. Marketing strategy and customer acquisition Attracting and retaining customers is a central challenge for fintechs,with the average cost of customer acquisition ranging between $100 and $200 per user. Well-planned digital marketing strategies, using channels such as social media, content marketing, and email campaigns, are key to reaching the target audience. Another effective approach is to invest in strategic partnerships. Collaborating with complementary companies, like marketplaces or technology providers, can expand the fintech’s reach and offer more complete solutions. Building a trustworthy brand that is close to consumers is the key to long-term success. 6. Monitoring and continuous adaptation Studies show that 80% of consumers expect companies to offer personalized experiences. Collecting and analyzing user feedback allows fintechs to adjust their services to meet these expectations. Additionally, the adoption of emerging technologies, like artificial intelligence, is projected to increase operational efficiency by up to 40%, highlighting the importance of continuously innovating to remain competitive in the market. The path to success in the financial market Launching a successful Fintech solution requires strategic planning, constant adaptation, and customer focus. By understanding the market, defining a solid business model, ensuring regulatory compliance, and prioritizing the user experience, your company will be prepared to stand out in a competitive and constantly evolving sector. Embracing emerging technologies and maintaining a culture of continuous innovation are essential steps to winning over and retaining customers. Are you ready to turn your ideas into successful solutions? At Luby, we specialize in consulting and developing customized solutions for the financial market. Contact us and find out how we can help your company achieve extraordinary results.

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The impacts of generative AI on the future of payments

Leave a Comment / Artificial Intelligence / Rafaela

Generative Artificial Intelligence (GenAI) is at the center of technological discussions in 2024, leading to changes in many sectors. This trend is no different in payments, one of the pillars of modern finance. With the potential to move billions of dollars, gen AI is transforming everything from basic day-to-day operations to personalization and security strategies. Today, we will explore the impact of this technology on payments, the challenges it brings, and the main opportunities for companies. The GenAI revolution Unlike traditional AI, which performs specific tasks, generative AI goes beyond, adapting to new contexts and creating new solutions. This ability to “think and create” is transforming financial services, including payments, by reimagining how solutions are delivered and problems are solved. According to McKinsey, generative AI has the potential to add between $2.6 trillion and $4.4 trillion to the global economy annually. In addition, productivity in the financial sector could increase by up to 30% by 2028, benefiting everything from traditional banks to innovative fintechs. Generative AI in the payment cycle The payment journey goes far beyond a simple transaction – it involves several steps that need to work in an integrated and efficient way. From data capture and authentication to processing, approval, and financial reconciliation, each phase carries its challenges. This is where genAI comes in, bringing innovative solutions to optimize and automate the entire process. With its ability to adapt and learn continuously, generative AI is transforming this journey into something more agile, secure, and strategic. Check out some of its main uses in the financial sector: Process automation Companies are using generative AI to automate repetitive tasks and reduce human error. An example is the use of automated invoice reading systems to extract critical data and process it quickly. Tools such as Paymo’s PM Payments allow payments to be made directly from an invoice with a single click, eliminating the waiting time for manual transfers and ensuring greater agility. In addition, generative AI’s power to understand structured and unstructured data makes it ideal for financial operations on a global scale, where language barriers and different document formats could delay processes. Fraud and security The fight against fraud is one area where generative AI is already bringing impressive results. With the ability to analyze billions of pieces of data in seconds, generative models identify patterns of usual behavior and flag atypical transactions. Visa, for example, has introduced Visa Account Attack Intelligence (VAAI), which uses AI to predict attacks in online transactions, protecting companies against fraud that would cost billions. By combining traditional fraud detection algorithms with the power of GenAI, companies can respond more efficiently to emerging threats, reducing friction for legitimate customers while making the job of scammers more difficult. Personalization and customer experience In the digital world, personalization is the key to attracting and retaining customers. Generative AI analyzes consumer behaviors and transaction records to offer recommendations adapted to each user. For example, advanced systems can suggest payment methods, offer personalized discounts, or even predict the customer’s next purchases. Also, chatbots equipped with Generative AI are already helping financial institutions to offer real-time support, with contextualized responses that increase customer satisfaction. This eliminates long waits and improves the customer experience with the service. Dynamic pricing Generative AI is also changing the way banks and fintechs set prices for financial products like loans and insurance. By combining market data, customer behavior, and historical patterns, the technology adjusts prices in real time to reflect demand and minimize risk. This strategy, widely used by platforms such as Amazon and Uber, is now coming to the financial sector. For consumers, this can mean more competitive rates, while companies can optimize their profit margins. Reconciliation and operational efficiency For companies that handle thousands of transactions a day, manual reconciliation can be a time-consuming and error-prone process. With Generative AI, systems can identify inconsistencies automatically, simplifying audits and financial reporting. This not only saves time but also reduces operating costs. Managing the risks of Generative AI The adoption of Generative AI in payments, while promising, brings significant challenges that require close attention. The use of large volumes of data, which are essential for its operation, increases the risk of privacy violations, making strong governance and regulatory compliance indispensable. In addition, AI models can reflect or amplify biases present in training data, which requires constant auditing to ensure fair decisions. Another crucial point is the need for reliability and transparency, especially in sensitive areas such as credit approval and fraud detection, where decisions must be explainable and understandable to everyone involved. Besides the technical and regulatory aspects, the adoption of Generative AI also raises ethical and economic issues. Companies need to balance enthusiasm for the opportunities with the responsibility to avoid negative impacts, such as the reproduction of harmful stereotypes or intellectual property violations. On the economic level, the transformation in skills and occupations will require investments in qualifications and clear strategies for managing transitions in the labor market. For a fair and sustainable use of AI, companies must share learnings transparently with governments and other sectors, promoting a balance between innovation and responsibility. Shaping the future of payments with GenAI Generative AI is bringing operational efficiency, large-scale personalization, and improved security to payments. However, to take advantage of the full benefits of this technology, companies need to keep in mind challenges like data privacy, transparency, and integration with existing systems. Success will depend on the ability to innovate with responsibility, placing customers’ needs at the center of technological solutions. At Luby, we are prepared to help financial institutions sail through this digital transformation. Our expertise in technological solutions for the financial sector allows us to develop strategies that effectively integrate Generative AI, leading to tangible results such as reduced operating costs, increased revenues, and secure, personalized experiences for customers. Do you want to transform your payment operation with Generative AI? Contact us!

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AI TRiSM: transforming trust and AI management

Leave a Comment / Artificial Intelligence / Rafaela

As artificial intelligence advances in business, companies need to adopt new structures to ensure their technologies are used safely, ethically, and effectively. According to CX Trends 2024, 75% of companies believe that a lack of transparency and clarity in AI models can pose serious reputational risks. AI TRiSM offers a structured approach to these challenges, helping organizations build reliable and scalable AI models aligned with regulatory requirements and market expectations. A new security standard AI TRiSM stands for three core pillars: trust, risk, and security. It covers a set of practices and tools to ensure AI systems are developed and deployed safely, transparently, and ethically. In a landscape where AI safety incidents can have substantial economic and reputational effects, AI TRiSM is becoming a necessary response. According to Gartner, by 2026, half of companies developing AI will adopt AI TRiSM practices to reduce risks and strengthen confidence in their systems. These practices enhance security and reliability across advanced technologies like facial recognition and autonomous vehicles, which, without proper safeguards, can be vulnerable to cyberattacks, data manipulation, and even biased outcomes. With AI TRiSM, companies gain a structured framework to manage trust and security, reducing reputational risks and protecting their operations. Essential components of AI TRiSM for business The successful implementation of AI TRiSM depends on the integration of four fundamental pillars: Explainability and transparency: AI models can be highly complex, and their opacity raises concerns. In regulated sectors like finance, transparency is essential for the success of AI as it allows users to understand model decisions and have faith in the technology. Model operations and maintenance: AI TRiSM ensures that models are constantly monitored and adjusted when necessary. This enables rapid adaptation to changes in the market and consumer expectations, increasing competitiveness. AI application security (AI AppSec): security is a critical issue in AI systems. AI AppSec protects the system against attacks, preventing intrusions and the compromise of sensitive data – a pressing issue for companies in the US financial sector, which face complex data protection challenges. Privacy and data protection: besides protecting consumer data, AI TRiSM promotes compliance with regulations such as the GDPR and CCPA, keeping AI in line with increasingly stringent privacy policies and avoiding sanctions. These four pillars establish a solid foundation to ensure the integrity and accountability of AI models. For companies striving to stand out in terms of reliability and security, AI TRiSM is a strategic advantage. How AI TRiSM can boost your business AI TRiSM not only reduces risk but is also a genuine competitive edge with the potential to increase business value. With it, it’s possible: Increase customer trust and buy-in: customers and partners tend to trust organizations that demonstrate transparent and responsible use of AI more. In the financial sector, for example, the explainability of AI ensures data-driven decisions are accurate and regulatory-compliant. Facilitating regulatory compliance: data privacy and security regulations are constantly evolving. That’s why companies that adopt AI TRiSM are better prepared to face these changes, avoiding the risk of sanctions. Optimize processes and improve operational efficiency: by constantly monitoring models, failures can be identified and adjusted quickly, reducing downtime and operating costs. Promote innovation with security: the security provided by AI TRiSM allows companies to experiment with new models and technologies without compromising data integrity or risking their reputation. Examples of AI TRiSM implementation One of the most common challenges in implementing AI TRiSM is the need for multidisciplinary teams with expertise in AI, security, ethics, and regulation. In addition, setting up a technological infrastructure for continuous monitoring and automated compliance can be a problem for some companies. However, its benefits outweigh these challenges, helping to avoid significant costs and protect the company from potential violations. AI TRiSM is also adaptable, evolving alongside business needs. Companies that apply AI TRiSM report significant improvements in performance and consumer confidence. Financial institutions use AI TRiSM to make their anti-fraud systems more transparent, offering explanations for decisions and creating robust barriers against attacks. They also integrate AI TRiSM to ensure that automated credit analysis and identity verification practices are secure and compliant with regulations. AI TRiSM and the future of AI in business As the use of AI grows, AI TRiSM will grow to cover new areas, such as generative AI and the automation of complex decisions. For this reason, technologies such as secure and explainable machine learning have become indispensable for keeping this framework relevant and effective. In addition, integration with real-time monitoring tools and automated audits strengthens security and continuous compliance, a growing requirement in the market. Today, companies that invest in this structure are better prepared for the future. After all, transparency, ethics, and security form a solid foundation for operating responsibly and reliably. Luby is ready to help your company exploit the maximum potential of AI with security and integrity, ensuring that you stand out in a competitive market. Connect with our experts today to learn more!

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Author name: Rafaela

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