Society and the financial sector are undergoing a revolution driven by technological advances, being artificial intelligence (AI) and machine learning (machine learning) key elements. Applications and tools that would previously be considered futuristic and science fiction works are increasingly close to our daily lives, redefining customer experience, asset management, fraud prevention and other crucial aspects of the area.
The growing demand for automation and predictive analysis in finance is one of the most latent transformations. Processes that previously took days and needed countless people, can currently be done in seconds. A very simple example is the opening of an individual's bank account. It is unimaginable for young people today to think that it was necessary to take a queue of hours in the bank, wait for the manager to fill several documents, take a 3⁄4 photo and still have to return to the agency 15 days later to know whether or not the process was approved.
In the same vein, improving the customer experience is one of the use cases we feel most about day-to-day, when we think about integrating AI with machine learning, whether it's no frontend, with process automation, replacing manual tasks, improving customer service and implementing efficient chatbots, whether in the backend, by streamlining analyses such as loan granting and approval.
Another highlight is the application of deep learning in the assessment and management of credit risks, as seen in the partnership between Citi and Feedzai.The use of Big Data and machine learning in customer churn forecasting and asset analysis also highlights the versatility of these technologies. Without the tools on the scene, business models such as internet payments would be impossible, since card transactions are confirmed in seconds, with data navigating globally on a network interconnected with AI and ML to prove that a certain operation is being performed by the cardholder.
Transforming the use of AI and machine learning it also excels in stock market forecasting, using artificial neural networks and algorithms to estimate oscillations and discrepancies.The implementation of these technologies in credit scoring, exemplified by Equifax in the United States, highlights the scope on the agenda.
So artificial intelligence and machine learning are key catalysts in the midst of this whole scenario, delivering efficiency, security and actionable insights, predictors for the financial sector.
In Brazil, the Central Bank is still paving 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 reversed with the citizen ceasing to be “client” to become “user, increasing competition between companies and service providers and at the same time diversifying opportunities for the consumer.
