The financial sector is at a turning point! The pressure to innovate, provide faster and more personalized experiences to customers, and ensure efficiency has never been higher. In this scenario, for companies that still maintain part of their operations on legacy technologies, migration to the cloud emerges as one of the main facilitators for data integration, operational scalability, and is crucial for the adoption of artificial intelligence (AI). This process, however, presents significant challenges and remains one of the latent pain points for institutions that were not born digital.
By enabling businesses to scale their operations and integrate large volumes of data, the cloud becomes the foundation on which AI solutions can be built.For credit granting, for example, customer behavior analysis has become a crucial tool, made possible by access to massive real-time data. AI allows for identifying patterns, predicting risks, and providing more accurate decisions. But, for that, it is essential that the data be accessible and organized in a flexible and scalable infrastructure, features that the cloud offers in an adaptable manner for each phase of the process, such as model training and operation.
The migration of legacy systems to the cloud, however, presents a series of obstacles. Many financial institutions, especially those with more traditional infrastructure, still operate on locally developed systems from past decades. These, although robust for their original functions, 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 in 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 hard-to-access manner, which prevents intelligent analysis from being possible. The transformation of 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 that were born in the digital environment and in the cloud, the scenario is quite different. Financial startups and fintechs often avoid the challenges faced by traditional banks, taking advantage from the start of the benefits of a modern infrastructure. These companies focus on utilizing this infrastructure and AI models in their core strategy, as part of their core business and value delivery – which is often linked to values such as agility and cost-effectiveness. Furthermore, the competitiveness of these institutions translates into a greater ability to offer personalized and innovative services, such as predictive analysis for credit granting, with an efficiency that challenges the major market players.
Traditional institutions, on the other hand, possess much larger amounts of data, which are not always accessible, but have the potential to underpin more robust analyses.
Although the full migration to the cloud may seem like a monumental task for these large institutions, there are strategies that can facilitate this process in a more gradual and controlled manner. Incremental approaches, such as 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 involve selecting critical business processes that can potentially benefit from AI-based solutions, reengineering them, and maintaining them alongside traditional processes so that both challenge each other and generate evidence about the feasibility and impact of the new solutions..
This method, in addition to 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 fully leverage 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 in the process of modernization or for digital startups, migration to the cloud has ceased to be a trend and has become a practical requirement. Competitiveness in the financial sector, driven by Artificial Intelligence, directly depends on the ability to integrate and manage large-scale data efficiently and securely. Ignoring this change can limit innovation potential and restrict growth in an increasingly digital and competitive environment.
