Blog article
See all stories »

What the ChatGPT do you know about generative AI in banking?

ChatGPT, while getting much attention, is just the beginning of a new era of using a specific category of AI as a tool for organisations to improve their operations and provide better service to customers. Taking a look at the core of its capabilities, the banking sector is one of the industries that stands to benefit most from the emergence of this new technology. 

While AI as a whole is designed to recognise patterns and make predictions, generative AI is a subsect of AI that generates new outputs based on the data they have been trained on. The most fascinating difference is the ability to generate answers to questions based on the context and conversational input given by the user, making it more versatile and capable of handling a bigger range of scenarios. It can also handle not just the conversation, but also other NLP (natural language programming) tasks like language translation, text summarisation and sentiment analysis.

One of the exciting possibilities of generative AI in the banking sector is its ability to generate human-like responses to questions and statements in conversation. In contrast to traditional AI chatbots, which are often less capable of understanding and generating natural language, generative AI has the advantage of responding more accurately and in detail. In short, it has the ability to digest information, adapt to the scenario and then provide a more personalised recommendation.

So how can this work in practice?

By using generative AI to analyse customer data, banks can better understand customer behaviour, preferences, and interests. This data can then be used to tailor products and services to the customers’ needs and preferences. For example, banks can use it to create an even more personalised offer, such as credit cards with preferential interest rates and rewards, that are tailored to particular customers.

Another area where generative AI can be used is to create more secure financial processes. By training models on banking transaction datasets, it could be an additional way that banks can detect suspicious or fraudulent transactions faster and more accurately than before. This could help banks protect their customers from financial losses and scams.

Despite the promise of the technology, there are some challenges and considerations that must be considered carefully before wider implementation. It’s important to note that generative AI is still a relatively new technology, and its potential applications in the banking sector are still being explored. 

One of the key considerations is data privacy and security. Generative AI algorithms need to analyse large volumes of diverse and representative customer data, and banks must ensure that this data is kept secure and not misused in any way. Banks must have robust security measures in place to protect customer data from unauthorised access and manipulation.

Then there is the issue of accuracy and bias. Generative AI algorithms are only as good as the data they are trained on, and it is important to ensure that the data used to train the algorithms is diverse, representative, accurate and up to date. If the data is not, then the results generated by the algorithms are also likely to be inaccurate, biased or nonsensical. 

Not least of all, there is the issue of regulatory compliance. Generative AI algorithms must be trained in accordance with financial regulations and while tougher regulations for this sort of technology are still up in the air, banks must ensure that they are compliant with hardened regulations before deploying generative AI in their operations.

Can’t we do some of this already?

AI is already being commonly used by banks to recognise patterns, calculate probability of outcomes and offer up the next best conversations or actions. Doing this at scale and in real-time using data from all channels can offer a differentiated and personalised customer experience. There are applications in areas of banking that right now are under real pressure to improve, such as operations and customer service, where Voice AI can listen, capture, and take actions automatically, or suggest to a customer service agent. This can help improve productivity and service levels – especially important for any areas which directly or indirectly influence revenue streams.

Ultimately, generative AI is a powerful technology that has the potential to deliver further improvements in a number of areas of the banking sector if configured correctly. However, banks must consider the challenges and considerations that come with implementing generative AI. By addressing these concerns, banks can leverage the power of generative AI to provide better products and services to their customers and become more competitive in the market.


Comments: (0)

Now hiring