Author name: Rafaela

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

Artificial Intelligence /

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 /

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 /

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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Steps to a successful financial solution

6 steps to a successful financial solution

Leave a Comment / Banks and Fintechs /

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 /

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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Luby partnership

Luby and LendAPI join forces to transform the financial sector

Leave a Comment / News /

Luby is now a certified partner of LendAPI (Techstars ’24), reinforcing its commitment to innovation and digital transformation in the financial sector. This partnership enables us to bring advanced digital onboarding and underwriting solutions to more banks and fintechs, delivering a fast, secure, and efficient process. What this partnership can offer to our clients: Fast implementation: Luby’s solutions, combined with LendAPI, reduce onboarding time to just a few hours, enabling banks and fintechs to activate accounts and facilitate loans almost instantly. Integrated workflows: Leveraging LendAPI’s modular, API-based infrastructure, we design integrations that seamlessly adapt to each client’s system. This minimizes operational challenges and reduces time spent on complex technical integrations, allowing institutions to focus on delivering valuable experiences for their customers. Automated underwriting: Our automated underwriting process, driven by LendAPI’s decision engine, enables institutions to make faster and better credit decisions. By applying predefined rules on credit risk, income verification, and other key factors, lenders can achieve consistency in application decisions while minimizing manual tasks and reducing errors. Scalable and adaptable solutions: Together, Luby and LendAPI provide onboarding experiences that grow with the client’s business, removing technical barriers so financial institutions can focus on expanding their services. Transforming the financial market with high-impact digital onboarding   This partnership represents a significant step forward in the digital transformation journey of financial institutions, allowing Luby and LendAPI to bring to market a complete onboarding experience that eliminates traditional obstacles. Luby’s expertise in creating customized workflows, combined with LendAPI’s decision engine, results in a platform that is fast, adaptable, and aligned with the demands of a sector that is constantly evolving. Timothy Li, CEO of LendAPI, says: “The partnership with Luby expands our horizons and allows us to offer an end-to-end digital onboarding experience. This collaboration takes fintech innovation to new heights, offering our customers even greater opportunities for success.”  Modernizing underwriting legacy systems   Outdated infrastructures often limit scalability, slowing down loan processing, and making it harder to maintain compliance. By combining Luby’s modernization expertise with LendAPI’s advanced API-driven platform, we offer lenders a solution that not only speeds up onboarding but also transforms their underwriting systems into fast cloud-based operations. This upgrade allows institutions to easily integrate new technologies, fulfill regulatory obligations, and create a better client experience. “Through this partnership, we will deliver a streamlined digital onboarding solution, boosting client acquisition with an underwriting system already integrated with essential players. With only one integration required, the process becomes faster and more efficient, driving meaningful improvements in lender operations. The game has changed!”, said Luby’s CEO, Alon Lubieniecki. Innovation and growth with a customer focus   For Luby, this collaboration goes beyond a partnership – it’s a commitment to transform financial onboarding, redefining standards of speed, accuracy, and security. In a world where 40% of consumers prioritize fast, frictionless digital experiences, we are committed to delivering onboarding that makes a difference. The expectation is that this innovation will help our customers gain more efficiency, reducing the cost of acquisition and increasing customer retention. We are ready to transform the future of the digital experience in the financial sector, creating solutions that bring real results and drive growth for our partners. Talk to our experts and find out how we can accelerate your business!

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AI TRiSM

AI TRiSM: transforming trust and AI management

Leave a Comment / Artificial Intelligence /

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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AI and ML in legacy modernization

The impact of AI and ML on legacy modernization

Leave a Comment / Artificial Intelligence /

Modernizing legacy systems is increasingly important for organizations seeking competitiveness, innovation, and operational efficiency. These systems, often decades old, are essential to the functions of companies, but they limit their capacity for innovation and agility. Technologies such as AI and ML in legacy modernization facilitate the transformation of these systems, making them more efficient, scalable, and secure. The dilemma of legacy systems   Legacy systems are old technological infrastructures that, though functional, don’t keep up with the current market demands. These applications are known for several limitations like no flexibility, high maintenance cost, and security vulnerabilities. Besides this, integration with new technologies and scalability are often extremely complex. Many financial institutions, fintechs, and banks in the US face this problem, accumulating decades of data and critical processes that depend on legacy systems. However, these systems need to adapt to allow these companies to keep up with digital trends, meet customer expectations, and remain in compliance with new regulations. The role of AI and ML in legacy modernization   Modernizing legacy systems with AI and ML is not a trend, but a transformative change that enables organizations to create value from their old systems and maximize the use of their data. These technologies offer practical solutions to the challenges that legacy systems provide and can be applied to: 1. Intelligent automation and complexity reduction One of the biggest coups of AI and ML in legacy modernization perhaps is the intelligent automation of key processes. AI tools can be used to analyze legacy code and create automated documentation, which is especially useful in legacy systems where good documentation is lacking. This approach reduces the complexity involved in modernization, allowing companies to identify hidden interdependencies and business rules embedded in the code. A good example is DCI (Data Center Inc.), which, in partnership with Luby, restructured one of its main solutions using modern design and UX technologies. The main goal was to improve the user experience, resulting in a 25% reduction in the system’s response time and expanding its services to new clients.  2. Predictive models for decision making The ability to use historical data stored in legacy systems is one of the biggest gains from AI integration. Legacy systems often contain a wealth of data that has not been efficiently exploited. By applying machine learning algorithms, companies can create predictive models that help with strategic decision-making. These models can predict consumption patterns, and market trends, or even optimize operational processes based on historical data. For example, banks with years of customer behavior information can apply ML to develop better algorithms for credit scoring or improve customer service through better recommendations. This use of ML enhances efficiency and reduces operating costs since the system can automate decisions that previously required human intervention.  3. Data-driven modernization In addition to automation, AI plays a crucial role in turning data into actionable insights. Using neural networks and deep learning algorithms, AI can identify patterns in large volumes of data that would be impossible for human analysts. This data-driven approach is essential because when implementing AI and ML, companies must first map and understand existing data, ensuring that critical information is preserved and used effectively. An interesting example is Caliber, a credit company that, before partnering with Luby, was facing challenges with an outdated technological infrastructure. The migration to a microservices architecture resulted in a 35% increase in the capacity to process new loans and a reduction in operating costs by about 20%.  4. Enhanced security and compliance Legacy systems are notoriously vulnerable to security threats, as they often don’t receive updates and don’t meet modern compliance requirements. AI and ML can mitigate these risks in several ways. Firstly, ML algorithms can identify anomalous behavior in real time, alerting us to potential cyber threats. This is particularly relevant in highly regulated sectors such as finance, where security breaches can result in heavy fines and reputational damage. In addition, AI tools can automate regulatory compliance, ensuring that the updated system complies with local and international standards. In the financial context, where compliance with standards is crucial, AI can be used to monitor and ensure that data is properly managed, reducing the risks and failures associated with out-of-date legacy systems.  5. Compatibility with new technologies and scalability Integrating AI and ML into legacy systems also enables scaling that was not possible earlier. Using AI to create APIs that connect legacies with new technologies, such as the cloud or mobile platforms, allows the migration to more modern architectures without having to rewrite the whole system from scratch. This approach not only improves efficiency but also facilitates the adoption of new technologies. A very common example is the migration to cloud environments, where legacy systems can be integrated via APIs and operated as microservices. This allows companies to scale their operations as needed, without worrying about the hardware limitations of legacy systems. In addition, integration with the cloud facilitates the use of AI and ML, since most AI platforms work best in scalable cloud-based environments. Strategic approaches to use AI and ML in legacy modernization   Modernizing legacy systems with AI and ML can be done using different approaches. The choice of strategy depends on many factors such as the present state of your system, the objectives of your company, and the resources available.  Refactoring: It means rewriting a critical part of the legacy code to make it more efficient and scalable without losing its essential business functionality. AI-powered tools can speed up refactoring by automatizing the identification of redundant or irrelevant code and suggesting improvements. Rearchitecture: AI can help rewrite the whole legacy architecture and its transformation into a far more modern and flexible solution, like microservices architecture. This approach allows companies to take full advantage of new technologies such as big data and IoT. Complete rewrite: In extreme cases, where the legacy system is incompatible with modern needs, a complete rewrite may be the best option. AI can automate part of this process, significantly reducing the time needed

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The future of multi-cloud strategy

Leave a Comment / Cloud /

The multi-cloud strategy is a solid reality in the business scene, allowing organizations to use the best services from different cloud providers to maximize their efficiency. As companies become increasingly digital, multi-cloud trends indicate a future marked by increased integration, enhanced security, and intelligent automation. The evolution of the multi-cloud   The evolution of multi-cloud strategies has become essential for businesses looking to enhance flexibility and avoid relying on a single provider. By using multiple vendors, companies can protect themselves against potential failures and changes in policy. According to Flexera, 89% of organizations already implemented multi-cloud solutions to boost reliability and tailor performance to their needs. When we talk about multi-cloud, it’s important to understand that there are three main types of cloud: public, private, and hybrid. The public cloud, such as AWS, Google Cloud, or Azure, offers shared services, which are generally more accessible and scalable. The private cloud is exclusive to one company, bringing more control and security. The hybrid cloud combines both, allowing flexibility and the use of various infrastructures to meet different operational and security needs. Looking ahead, Gartner estimates that by 2025, over 85% of large companies will adopt multi-cloud strategies. This shift is not just about risk management; it aims to create agile digital environments that support continuous innovation and efficiency in an ever-changing business landscape. Future trends for the Multi-cloud   Advanced integration and orchestration Advanced integration and orchestration are pillars for success in multi-cloud environments. Many companies don’t have in-house teams prepared to deal with this complexity, which makes managed cloud services (MSPs) increasingly indispensable. These providers stand out by facilitating communication between different platforms, creating a unified and fluid environment, regardless of the supplier. With the MSP market projected to reach $116.2 billion by 2025, expert support will be crucial to optimize resources, ensure compliance, and strengthen security. Security as a priority With the increasing complexity of multi-cloud strategies, security will continue to be a major concern. Solutions that monitor threats, manage identities, and guarantee encryption in multiple cloud environments will be essential to maintain confidence in the model. The trend is towards centralized, automated security solutions that offer visibility across all providers and environments. Automation and Artificial Intelligence Automation is becoming a strategic ally in multi-cloud management. With the use of artificial intelligence (AI) and machine learning tools, it will be possible to manage multiple clouds efficiently, automating complex processes such as load balancing, fault recovery, and dynamic resource allocation. These technologies should optimize operations, reduce costs, and allow companies to respond quickly to changes in the environment, improving their operational efficiency. Cloud-native and Containers The development of cloud-native applications, using containers and microservice architectures, will allow greater agility in moving workloads between providers. With the use of solutions such as Kubernetes and Docker, portability will be a differentiator, facilitating management and scalability in multi-cloud environments. Edge Computing and Multi-cloud Edge computing is increasingly integrated into multi-cloud strategies, especially in sectors such as IoT, telecommunications, and manufacturing. Companies are looking to reduce latency and optimize data processing by using solutions that leverage from the power of the cloud closer to the end user. It is estimated that by 2025, 75% of business data will be processed outside of traditional data centers and the cloud. Focus on interoperability Interoperability is one of the biggest challenges in multi-cloud environments. With the use of platforms such as AWS, Microsoft Azure, and Google Cloud, efficient integration between these providers is essential. By 2025, providers are expected to develop tools and APIs that facilitate this integration, allowing companies to create cohesive multi-cloud environments. This will increase flexibility, reduce dependence on single providers, and improve operational agility. The role of the multi-cloud in the business future   Companies that adopt the multi-cloud gain a significant strategic advantage. The model offers flexibility, allowing organizations to adjust their services according to their needs, optimizing costs, and ensuring high resilience. However, in the future, companies need to be prepared to deal with increased complexity, whether in terms of integration or security. Success will depend on choosing the right tools and a robust approach to orchestrating and managing multi-cloud environments. Growing this strategy doesn’t just mean diversifying providers, but ensuring that the entire IT ecosystem is aligned with business objectives. How to know if multi-cloud is the right strategy for your company   Adopting multi-cloud can bring significant benefits, but not all companies are ready for this change. To ensure that your organization gets the most out of this strategy, it is essential to carry out a detailed analysis of business needs, capabilities, and objectives. Checklist for multi-cloud adoption:   Here are some essential points to consider before adopting a multi-cloud strategy: Business needs: Check that multi-cloud meets your company’s strategic objectives. Are flexibility, resilience, and the ability to innovate essential? Workloads: Assess which workloads would benefit from multiple providers. For example, operations that require high availability or critical services could benefit from the redundancy offered by different clouds. Internal IT capabilities: Does your IT team have the expertise to manage different cloud environments? Multi-cloud requires advanced skills to integrate, monitor, and maintain multiple providers. If not, you may need to invest in training or hire specialists. Cost-effectiveness: Compare the costs of maintaining a single cloud versus a multi-cloud strategy. Consider hidden costs, such as data transfer fees between providers and licensing of monitoring tools. Security and Compliance: Does your company have specific compliance and security requirements? Multi-cloud can meet these needs by allowing different providers to comply with regulatory requirements in different regions, but you will need unified security policies. Automation tools: Automating provisioning and monitoring processes can help reduce the complexity of multi-cloud management. Check whether you already use or are willing to adopt automation tools to integrate these environments efficiently. Migration planning: Carefully plan the migration of your applications to different cloud providers. Identify possible incompatibilities and impacts on business continuity. Scalability and performance: Make sure that the multi-cloud can meet the scalability and performance requirements

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DeFi: the future of finance without Intermediaries

Leave a Comment / Banks and Fintechs /

Technological innovation has transformed the financial sector in surprising ways. Among the most notable changes is the emergence of Decentralized Finance (DeFi) and Decentralized Banking, which are not only offering new opportunities but also challenging the traditional financial system. Aimed at democratizing access to financial services, DeFi has the potential to transform the way we deal with loans, investments, and asset trading. They are gaining prominence in global markets, including the US, by enabling direct transactions between users, without relying on intermediaries such as banks and brokers. What is Decentralized Finance (DeFi)? DeFi refers to an ecosystem of financial services that operate on blockchains, using cryptocurrencies and smart contracts. Unlike the traditional financial system, which relies on banks and centralized financial institutions, DeFi allows transactions directly between users, with total transparency and security, without the need for a central authority. These self-executing contracts are stored on the blockchain, which ensures that once the agreed conditions are met, execution takes place automatically. This is especially attractive for those looking for more privacy and lower fees in their financial transactions. DeFi’s main features and impact on the market DeFi is revolutionizing the financial sector by offering fast, accessible, and transparent alternatives to traditional services. Some of the main features that stand out are: Loans: Users can lend or borrow crypto-assets directly, without relying on traditional banks. Investments: Allows investors to earn passive income by making their assets available for other financial transactions. Crypto trading: Facilitates transactions without the need for centralized brokers, with significantly lower fees. Stablecoins: Digital currencies pegged to fiat currencies such as the dollar, which help reduce volatility and increase predictability. Global DeFi regulatory landscape The DeFi regulatory landscape is evolving rapidly around the world, with different countries seeking to balance innovation and financial security. In the US, the SEC (Securities and Exchange Commission) is monitoring DeFi platforms, suggesting that many of them could be treated as securities, which would imply compliance with traditional regulations. In addition, the US Treasury is on the lookout for risks such as money laundering and terrorist financing, requiring measures such as “Know Your Customer” (KYC) on DeFi platforms. In the European Union, MiCA (Markets in Crypto-Assets) is being implemented to regulate crypto-assets and DeFi platforms, promoting greater transparency and consumer protection, while DORA addresses cyber risks. On the other hand, countries like Singapore are taking a more flexible approach, allowing innovation while maintaining AML/CFT (Anti-Money Laundering and Combating the Financing of Terrorism) regulations. China and other nations have restricted or banned DeFi activity, concerned about financial stability. Advantages and risks Among the main advantages of DeFi are:  Global access: Anyone with the internet can participate. Lower costs: Without intermediaries, fees can be much lower. Privacy: Users don’t have to provide large amounts of personal data. Transparency: Transactions are recorded on the blockchain, allowing for public tracking. Flexibility: users can move their assets at any time. Risks, on the other hand, include: Insufficient regulation: DeFi still faces regulatory challenges, and many users are concerned about the lack of consumer protection. Security: Although blockchains are secure, flaws in smart contracts can be exploited by hackers. Volatility: The crypto asset market is highly volatile, which can pose a significant risk to less experienced investors. Complicated taxation: users need to keep their records for tax purposes. Real-life DeFi use cases in the US Platforms such as Aave and Uniswap are gaining popularity. On Aave, users can lend crypto-assets and earn interest without relying on traditional banks. On Uniswap, crypto trading takes place directly between users, eliminating brokerage fees and providing greater autonomy. These platforms have generated above-average returns compared to traditional financial products such as savings accounts and investment funds. Scalability and security challenges Scalability remains one of the biggest obstacles to DeFi’s growth. As more users join the ecosystem, the demand for transactions grows, which can overload blockchain networks. This results in higher fees and longer confirmation times. To solve this, solutions such as Layer 2 blockchains are being developed, but scalability remains a significant challenge. In addition, the decentralized nature of DeFi and the use of smart contracts create vulnerabilities that can be exploited by hackers. The misuse of “admin keys” – governance keys that allow a small group of people to change the platform’s parameters – also threatens the complete decentralization of DeFi, making it more susceptible to manipulation or failure. These challenges drive constant innovation in the sector, with teams of developers focused on improving the security and increasing the efficiency of DeFi platforms. The Future of DeFi  Although DeFi is still at an early stage, its transformative potential in the financial system is undeniable. In time, as regulatory and security issues are resolved, DeFi could be widely integrated into the global financial system, offering new avenues for access to capital and innovation. If you are interested in exploring how Luby’s technology solutions can help your company seize the opportunities of Decentralized Finance, fill out our form and get in touch with our team. We’re ready to help you navigate this new world of decentralized finance.

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