Cloud migration: the beginning of the AI revolution in the financial sector

The financial sector is at a tipping point! The pressure to innovate, provide faster and more personalized experiences to customers and, still, 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 facilitators 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 pains of institutions that were not born digital.

By enabling companies to scale their operations and integrate large volumes of data, the cloud becomes the foundation on which AI solutions can be built. For the granting of credit, for example, customer behavior analysis has become a crucial tool, made possible by access to massive data in real time. AI allows you to identify patterns, predict risks and offer more assertive decisions.But for this, it is essential that data is accessible and organized in a flexible and scalable infrastructure, characteristics that the cloud offers in an adaptable way to each phase of the process, such as training models and operating them. 

Migrating legacy systems to the cloud, however, presents a number of hurdles.Many financial institutions, especially those with more traditional infrastructure, still operate on on-premises systems developed in past decades.These, while robust to their original functions, are 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 operation is not disrupted.

In addition, 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 difficultly accessible manner, which makes it impossible to make it available for intelligent analysis. Transforming data from raw to structured requires a series of steps from cleaning, normalization and standardization 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 and cloud environment, the scenario is quite different. Financial startups and fintechs often avoid the challenges faced by traditional banks, taking advantage of the advantages of a modern infrastructure from the beginning. These companies focus on using this infrastructure and AI models in the central strategy, as part of the core business and value delivery that offer (which can often be linked to values such as agility and economy. In addition, 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 large players in the market.

Traditional institutions, on the other hand, have much larger amounts of data, which are not always accessible, but which has the potential to support more robust analysis.   

While full migration to the cloud may seem like a monumental task for these large institutions, there are strategies that can facilitate this process more gradually and in a 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.Each upgrade, companies can test and adjust 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 keeping them parallel to traditional processes, so that both challenge each other and generate evidence about the feasibility and impact of new solutions. 

This method, in addition to being financially more 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 making a revolution at once. 

Whether for traditional companies in the process of modernization or for digital startups, migration to the cloud is no longer 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 large-scale data, efficiently and safely. Ignoring this change can limit the potential for innovation and restrict growth in an increasingly digital and competitive environment.