Society and the financial sector are undergoing a revolution driven by technological advances, with artificial intelligence (AI) and machine learning being key elements. Applications and tools that used to be considered futuristic and works of science fiction are becoming increasingly close to our daily lives, redefining customer experience, asset management, fraud prevention, and other crucial aspects of the field.
The growing demand for automation and predictive analytics in finance is one of the most prominent transformations. Processes that used to take days and required countless people can now be done in seconds. A very simple example is opening a personal bank account. It is unimaginable for young people today to think that it used to be necessary to wait in line for hours at the bank, wait for the manager to fill out numerous documents, take a 3/4 photo, and still have to return to the branch 15 days later to find out if the process was approved or not.
Along the same lines, enhancing customer experience is one of the most noticeable use cases we experience in our daily lives when we think about integrating AI with machine learning, whether in the front-end, automating processes, replacing manual tasks, improving customer service, and implementing efficient chatbots, or in the back-end, by speeding up analyses such as loan approval and processing.
Another highlight is the application of deep learning in credit risk assessment and management, as seen in the partnership between Citi and Feedzai. The use of Big Data and machine learning in customer churn prediction and asset analysis also demonstrates the versatility of these technologies. Without the tools in place, business models like online payments would be impossible, as card transactions are confirmed within seconds, with data navigating globally on an interconnected network with AI and ML to confirm that a particular operation is being carried out by the cardholder.
The transformation of AI and machine learning usage also stands out in stock market forecasting, with the use of artificial neural networks and algorithms to estimate fluctuations and discrepancies. The implementation of these technologies in credit scoring, exemplified by Equifax in the United States, highlights the scope at hand.
Therefore, artificial intelligence and machine learning are fundamental catalysts amidst this entire scenario, providing efficiency, security, and predictive insights for the financial sector.
In Brazil, the Central Bank is still paving the way for a revolution with the BC# agenda, which involves Pix, Drex, and Open Finance. Within this initiative, the use of AI and ML will be transformative for the country. The market logic will be inverted, with citizens no longer being ‘clients’ but becoming ‘users,’ increasing competition among companies and service providers and, at the same time, diversifying opportunities for consumers.
