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Which Gen AI use cases are emerging as the most popular?

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Generative AI made a huge impact in 2023 – with the majority of financial services recognizing its potential to offer wide-ranging benefits and moving quickly to start exploring its implementation within their organizations. But what specific use cases are proving the most popular so far? This is a question we posed as part of our recent State of the Nation Survey, canvassing the opinions of 956 decision makers at financial institutions across nine different countries. As well as enabling financial institutions to personalize customer experiences at the front-end of their services, our research shows that decision-makers recognize myriad benefits over and above this. 

The generative AI use case that financial institutions are currently utilizing – or planning to utilize – in the highest numbers, involves leveraging it to collect and analyze ESG data for criteria classifications or decision making. With ESG and sustainability a firm fixture on the boardroom agenda, the potential for generative AI to extract information from unstructured data, including often-fragmented ESG data sources or siloed IT systems, is seen as a very compelling use case. 

Supporting financial institutions in their efforts to streamline the onboarding process whilst reducing financial crime is another widely mentioned use case, with one in three decision makers who are using or exploring generative AI interested in its potential to leverage data relating to Know Your Customer (KYC) or Anti-Money Laundering (AML) purposes. And, as fraud continues to become more sophisticated, the supporting role that generative AI could play will only grow in interest. 

Similarly, the ability for Gen AI to improve risk management, decision making and predictive analytics was also confirmed as a popular use case. 

Much like other industries, financial services organizations are also looking to leverage generative AI to reduce administrative tasks and boost productivity, and more generally to enhance IT operations where possible. These use cases will play a vital role in creating efficiencies and unlocking innovation, and their potential to close the innovation gap between traditional institutions and more agile competitors should not be downplayed. 

Our research shows that the proportion of financial decision makers utilizing or planning to utilize each of these use cases is relatively consistent at a global level – there is no standout single use case. This is not surprising, given the infancy of generative AI, and it is likely that future research we conduct will see a shift as the potential applications are explored, trialled, and rolled out. 

In the coming years, it will be fascinating to observe how generative AI is used across financial services worldwide, and which use cases – whether current or as yet undiscovered – advance across different markets. 

Of course, successful implementation of generative AI, irrespective of the specific application, will depend on how effectively and responsibly financial organizations can implement it in their front or back-end operations. Implementing a technology which is evolving so rapidly and has such powerful capabilities brings unique challenges – chiefly, ones relating to data privacy, computational capacity and legality. Choosing the right model, from developing proprietary Large Language Models (LLMs) to partnering with firms that integrate generative AI into their products and solutions, will allow financial institutions to reap the benefits of generative AI at their own pace and scale.

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