ChatGPT and the Privacy Dilemma in Business Projects

In the financial sector, a new revolution is emerging, akin to that of Fintech, this time sparked by the new technologies of generative artificial intelligence, with direct implications in areas such as customer service and conversational operation channels. This, as is often the case, brings new risks related to security... Therefore, it is worth asking some questions: What application fields in the financial environment are going to be most impacted by the application of Machine Learning? What strategies should banks adopt to implement this technology in such a way that benefits are maximized and associated risks, such as data security and privacy, are minimized?

We consider it very important to align this technology with the regulations of financial services advice, as when implementing AI-based chatbots in banking entities, we must consider whether these technologies, especially those of text generation, might be making financial recommendations to clients, as a personal advisor would. After all, they have been designed in a way that they can fulfill this purpose, at least in terms of simulating a conversation with a human, even making recommendations that may sound plausible. This does not mean that a machine is truly aware of the recommendations it makes and the connotations this may entail. In any case, what is clear is that people will turn to technology for these types of issues, so those who develop it must introduce security measures and gradually improve the performance of these models.

It is also important that we are aware that at present there is much confusion about the types of Artificial Intelligence and the utilities they can offer in this sector, both internally for companies and in terms of how people can use them. Since we see that there is great expectation about what can be done with ChatGPT and GenAI, this can generate confusion about the specific applications of other machine learning-based technologies that equally offer other more specific utilities. This leads us to propose again that it is fundamental to carry out informative and educational work so that the full potential of these technologies can be truly harnessed without generating hype and avoiding the corresponding frustration on the part of users.

We also find that there are many doubts regarding the use of ChatGPT in enterprise projects, especially regarding privacy and security issues, so we consider that the approach should be like that of intranets in their time, where each entity can achieve great control over its use, something that is beginning to be resolved as major cloud providers like Google and Microsoft integrate these technologies into their services. This is why we are currently seeing a slow and cautious adoption in the development of Generative AI-based projects for large entities in the financial sector, and in other industries, so we will have to wait to better understand how these technologies work and what implications they have for the company.

In any case, at SNGULAR we will always consider that the key to the successful adoption of these technologies lies in having the right and well-prepared talent. We also dare to envision a near future where personalized GPTs for banking entities, or their equivalents developed by other technology companies, will arrive, although it will not be quick due to all the complexities of this sector and how novel this technology still is.

Learn more about Artificial Intelligence with the team at SNGULAR.