The financial sector is at a turning point! The pressure to innovate, provide faster and more personalized customer experiences, and ensure efficiency has never been higher. In this scenario, for companies that still maintain part of their operations in legacy technologies, migration to the cloud emerges as one of the main enablers for data integration, scalability of operations, and is crucial for the adoption of artificial intelligence (AI). This process, however, brings significant challenges and remains one of the latent pain points for institutions that were not born digital.
By enabling companies to scale their operations and integrate large volumes of data, the cloud becomes the foundation upon which AI solutions can be built. For credit granting, for example, analyzing customer behavior has become a crucial tool, made possible by access to massive amounts of data in real time. AI allows for the identification of patterns, the prediction of risks, and the delivery of more assertive decisions. But for this to happen, it is essential that the data is accessible and organized in a flexible and scalable infrastructure, characteristics that the cloud offers in a way that adapts to each phase of the process, such as model training and operation.
Migrating legacy systems to the cloud, however, presents a number of obstacles. Many financial institutions, especially those with more traditional infrastructure, still operate on on-premises systems developed decades ago. While these systems were robust for their original functions, they were not designed to handle the flexibility and connectivity required by modern platforms.
Restructuring to a cloud environment involves not only technological adjustments, but also a profound transformation of business processes, ensuring that data migrates securely and that daily operations are not interrupted.
Furthermore, preparing data for use in AI solutions requires more than simply transferring it to the cloud. Legacy systems often store information in a fragmented or difficult-to-access way, making it impossible to make it available for intelligent analysis. Transforming data from raw to structured requires a series of cleaning, normalization, and standardization steps—and any failure in this process can compromise the effectiveness of AI algorithms.
The competitive strength of new digital institutions
For companies born in the digital and cloud environment, the scenario is quite different. Financial startups and fintechs often avoid the challenges faced by traditional banks, taking advantage of modern infrastructure from the outset. These companies focus on using this infrastructure and AI models in their core strategy, as part of their core business and the value they deliver – which can often be linked to values such as agility and cost savings. Furthermore, the competitiveness of these institutions translates into a greater capacity to offer personalized and innovative services, such as predictive analytics for credit granting, with an efficiency that challenges the major players in the market. Traditional
institutions, on the other hand, possess much larger amounts of data, which are not always accessible, but which have the potential to support more robust analyses.
While a complete cloud migration might seem like a monumental task for large institutions, there are strategies that can facilitate this process in a more gradual and controlled way. Incremental approaches, such as the modular modernization of legacy systems, allow companies to make updates in small steps, reducing the risk of critical failures and service interruptions. With each update, companies can test and adjust the integration with new technologies, ensuring a smoother and more effective transition.
These small-scale approaches consist of choosing critical business processes that can potentially benefit from AI-based solutions, reshaping them, and maintaining them in parallel with traditional processes, so that both challenge each other and generate evidence about the viability and impact of the new solutions.
This method, besides being more financially viable, allows companies to maintain service continuity and protect data integrity. More importantly, it creates a solid foundation so that, in the future, the company can take full advantage of the cloud and AI, without the pressure of a radical and immediate transformation. Implementing AI is not about making a revolution all at once.
Whether for traditional companies undergoing modernization or for digital startups, migrating to the cloud has ceased to be a trend and has become a practical requirement. Competitiveness in the financial sector, driven by Artificial Intelligence, depends directly on the ability to integrate and manage data on a large scale, efficiently and securely. Ignoring this change can limit innovation potential and restrict growth in an increasingly digital and competitive environment.
