Society and the financial sector are undergoing a revolution driven by technological advances, with artificial intelligence (AI) and machine learning (machine learningkey elements. Applications and tools that would previously have been considered futuristic and science fiction works are increasingly close to our daily lives, redefining the 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 numerous people can now be completed in seconds. A very simple example is opening a personal bank account. It is unimaginable for young people today to think that in the past it was necessary to stand in line for hours at the bank, wait for the manager to fill out several documents, bring 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, improving customer experience is one of the use cases we feel most on a daily basis when we think about integrating AI withmachine learning, either infront-end, with process automation, replacing manual tasks, improving customer service and implementing efficient chatbots, whether inback-end, by speeding up analyses such as granting and approving loans.
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 andmachine learningin customer churn prediction and asset analysis, it also highlights 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 traveling globally across an interconnected network with AI and ML to verify that a specific operation is being carried out by the cardholder.
The transformation of the use of AI andmachine learningalso excels in stock market forecasting, using 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 of the discussion.
Therefore, artificial intelligence and machine learning are fundamental catalysts in this entire scenario, providing efficiency, security andinsights predictive 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 logic of the market will be inverted, with the citizen ceasing to be a "customer" and becoming a "user," increasing competition among companies and service providers and, at the same time, diversifying opportunities for the consumer.
