INDUSTRY VIEW
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 consumerfacing communications and personalizing 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
One of the most powerful applications of Generative AI lies in improving consumer-facing communications and personalizing services . 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 . •
HOW CAN GENERATIVE AI UNLOCK THE POWER OF THE FINANCIAL SECTOR ?
NEYLSON CREPALDE , CTO OF A3DATA
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 .
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