The financial sector is at an inflection 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 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). However, this process brings significant challenges and remains one of the underlying pains of non-digital born institutions.
By allowing companies 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, analyzing customer behavior has become a crucial tool, made possible by access to massive real-time data. AI enables the identification of patterns, predicts risks, and offers more assertive decisions. But for this, it is essential that data be accessible and organized in a flexible and scalable infrastructure, features that the cloud offers adaptably at each stage of the process, such as model training and operation.
Migrating legacy systems to the cloud, however, presents a series of obstacles. Many financial institutions, especially those with more traditional infrastructure, still operate on local systems developed in decades past. These, although robust for their original functions, were not designed to handle the flexibility and connectivity required by modern platforms.
Restructuring for a cloud environment involves not only technological adjustments but also a profound transformation in business processes, ensuring that data migrates securely and daily operations are not interrupted.
Furthermore, preparing data for use in AI solutions requires more than simply transferring them to the cloud. Legacy systems often store information in a fragmented or hardly accessible way, making it impossible to provide for intelligent analysis. Data transformation 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 edge 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, leveraging the advantages of a modern infrastructure from the beginning. These companies focus on using this infrastructure and AI models in the core strategy, as part of the core business and value delivery they offer – which is often linked to values such as agility and cost-effectiveness. Additionally, the competitiveness of these institutions translates into a greater ability to offer personalized and innovative services, such as predictive credit scoring, with an efficiency that challenges the major players in the market.
On the other hand, traditional institutions have much larger amounts of data, which are not always accessible but have the potential to support more robust analyses.
Although complete 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 choosing critical business processes that can potentially benefit from AI-based solutions, reshaping them, and maintaining them in parallel with traditional processes so that they challenge each other and generate evidence about the feasibility and impact of 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 for the company to fully leverage the cloud and AI in the future, 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, 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 the potential for innovation and restrict growth in an increasingly digital and competitive environment.
