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Exploring Gen AI use cases in financial services


With the explosion of Gen AI in recent times, financial services organizations are looking to harness the technology to its fullest potential.  

Early forms of generative AI have been used across the industry for a decade, particularly in the form of synthetic data generation, but fear and fascination dominate boardroom conversations in equal measure. As a heavily regulated industry, trepidation is understandable, but exciting use cases are just on the horizon.  

The Gen AI lab 

Today, financial services use cases for Gen AI are mostly confined to accelerating and optimizing internal processes. But experimentation is increasingly revealing the shape of things to come.  

Retrieval-Augmented Generation (RAG) systems, for example, are enhanced Gen AI solutions, as they allow large language models (LLMs) to access external sources of information in conjunction with internal data. This enables external corpuses of information to be seamlessly integrated to deliver more accurate results. An example in FS might be customer FAQs, where information is provided on products and services via a chatbot, but further context is drawn from external sources that are outside of the chatbot’s training data. Harnessing generative AI in this way can help streamline customer queries and provide high-quality responses.  

Another area that is seeing increased interest is Copilots geared towards specific products. Where once a product may have relied on multiple microservices and APIs to deliver an outcome, the same products can now be routed through a chatbot interface, with the chatbot directly calling the APIs and delivering relevant information, cutting down the need for multiple dashboards and providing a more seamless experience.  

An interesting use case could be using generative AI to enhance natural language analytics. Traditional analysis of data tables requires manual intervention and scripting from business analysts and data scientists to develop dashboards and draw insights. Generative AI, NLP and advanced analytics can make it possible for clients to ask questions in natural language and get the answers they need directly.  

The Gen AI frontier in FS 

The next big breakthrough for generative AI across industries will be the growth of multimodal models that can process and generate information in multiple formats, such as text, audio, and video data. Many of the immediate use cases will center on content generation, such as marketing and internal training assets, but these tools will quickly evolve. 

With the ever-evolving emphasis on product responsibility, especially with regard to accessibility, multi-modal models will provide new mediums of interaction for underserved communities. For example, leveraging voice capabilities to access information relative to chat-only applications will help visually impaired customers. There are many other analogous use cases that will make financial services more inclusive, which must be a key consideration for financial institutions.   

Today, generative AI is very much living up to its hype, demonstrating incredible progress and productivity gains. I’d strongly advise every organization to explore both internal and external use cases and to focus on delivering ‘enterprise fluency’ for Gen AI, setting out skills development roadmaps that will see technical and non-technical employees embed Gen AI into their workflows.  

Gen AI is an accessible technology that can be used—and misused by anyone—from software developers generating code, to HR and finance teams generating reports. Without a strong organization-wide approach that considers AI maturity across job roles and departments, financial services firms will remain in discovery mode and will therefore not maximize the technology’s unprecedented potential.


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