Neylson Crepalde, CTO of A3Data, a consulting firm specializing in data and AI, tells us how Generative AI can unlock the potential of the financial sector.
With more than half of financial sector CEOs believing that business models are under threat within a decade, according to research by PwC (PricewaterhouseCoopers), it is clear that bold innovations are needed for institutions to remain economically viable, especially traditional operations, which were born before the digital turn.
As a result, many executives are looking at Generative AI as a potential solution to boost companies’ operational efficiency, build different experiences for their customers, unlock new sources of revenue and regain market confidence.
One of the most powerful applications of Generative AI lies in improving consumer-facing communications and personalising services. In this context, by understanding a customer’s usage history and interactions, this technology can produce sophisticated and personalised text outputs, from investment advice to marketing content.
Likewise, when associated with voice generation resources, this technology could revolutionise call centre operations with more natural and empathetic interactions with customers, not to mention the advantages in the process of accelerating service, reducing response time, which can expand the ability to solve problems without necessarily having to expand the number of agents.
This customer-centric focus driven by Generative AI has the potential to increase satisfaction, reduce churn and create new opportunities for engagement. However, the question arises: Is this the great leap to finally bridge the gap between the digitisation of the financial sector and the personal experiences that customers crave? Certainly, it seems like it.
That’s because, in addition to the consumer experience, Generative AI opens the door for financial institutions to intelligently automate workflows and back-office operations. Proof of this is that nowadays, generative models can be used to draft reports, summarise data, generate code and fill out forms, among others, based on an organisation’s specific preferences and requirements.
This autonomous content creation could dramatically increase operational efficiencies across all departments, from risk management to custody services. Teams could focus more on strategic, innovative work rather than repetitive, routine tasks. Generative AI essentially multiplies the output of existing employees to meet the scaling needs of the business.
From an investment perspective, Generative AI could be used to run millions of simulations, considering market variables to assess risk exposure and identify new trading opportunities. This evolution of AI-driven decision-making has the potential to reduce institutional risk while maximising returns.
Of course, deploying this powerful technology is not without its challenges, as the governance of AI models, combating hallucinations and biases, and utilising responsible practices are all critical considerations. Therefore, experience in areas such as prompt engineering, protective legislation and data ethics will become mandatory.
With such potential to generate value to the business, Generative AI presents itself as a tool that needs to be at the top of the priority list, since the market has already started a race for the production of products and applications powered by this technology. Whoever comes out ahead will have a huge competitive advantage. But for this to be possible, it is up to the institutions to decide whether they will be leaders or mere spectators in the transformation of the market.
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