How does artificial intelligence help enhance banking data?

AI is emerging as a tool for leveraging information in a competitive banking environment As the volume of data in the financial sector grows, underlying technologies are evolving at the same pace. Among them is artificial intelligence, which is increasing its capacity to read, store, and analyze large volumes of information. We are starting to see how AI, combined with machine learning, allows the financial industry to develop even more personalized products and services, prioritizing customer needs so that institutions can adapt and improve their offerings. Algorithms are also paving the way for the identification of customers with greater potential for brand loyalty based on the value of their interactions, thereby enabling the design of better loyalty campaigns. As such, they shed light on interesting areas, such as customer turnover and transactional preferences, which allows for better marketing campaigns with timely offers and promotions that can be presented to the user through push notifications in e-wallets or emails. The capabilities of AI in terms of development and digital transformation, as well as in terms of promotion, are still in their infancy; we are just scratching the surface of this technology’s potential.

MAKING SENSE OF DATA WITH AI

First, it is important to establish that AI as we know it so far belongs to the narrow category; it’s a definition that may sound weak but in practice it has already surpassed the role of humans, especially in structured or fixed tasks. Narrow AI is focused on performing a single task. A practical example applied in finance is the use of bots for automated and repetitive tasks, such as solving questions by obtaining data from larger systems. Another example of narrow AI is facial recognition or voice detection. However, this type of intelligence is being programmed to perform machine-learning tasks under certain parameters, such as processing natural language and identifying artificial vision. The goal is that, by working with machine learning or deep learning, entities will develop systems that improve their performance as they consume data. Another objective is to be able to analyze large numbers of transactions, preferences, and financial decisions, in order to learn from the public that interacts with the platforms. In short, what AI is doing with machine learning is to make sense of all the data in order to improve both the customer experience and the products in the banking environment.

AI COMBINED WITH CLOUD BANKING

The need to store large amounts of data more easily is pushing the financial industry to look even more towards the cloud and AI. Full cloud spending, including cloud services and the hardware and software components that support the supply chain, will exceed US$1.3 trillion by 2025, and will maintain a compound annual growth rate of 16.9%, according to the International Data Corporation (IDC). The development of today's digital economies requires the instant availability of data, a special feature of the cloud. In that sense, among the key points we will see this year with regards to the use of the cloud and AI is that combined, they allow access to data from anywhere, at any time, guaranteeing security. Similarly, employing artificial intelligence in conjunction with the cloud will enable financial institutions to offer loans and credit backed by more granular data, an important tool for entities seeking to reach underbanked audiences such as in rural areas. The financial sector is slowly migrating to these flexible cloud architectures and is already better attuned to the idea of moving legacy systems into this environment and harvesting them using machine learning and AI. We will certainly see artificial intelligence increasingly become a competitive tool for banks as they analyze their data and offer better services to their customers.