Artificial Intelligence (AI) and generative AI continue to make headlines. As
Chat GPT marks its first birthday, the technology behind it, Large Language Models (LLMs), continue to show a wide range of applications and have shown promising results across a variety of use cases and industries.
McKinsey reports that the productivity lift from generative AI can lead to an increase of 3-5% of annual revenue in the banking sector, which is equivalent to $200 billion to $340 billion of additional annual revenue. This article takes a deep dive into
these tools and sees how they can be of value to the financial industry.
What are LLMs and where do they fall in the larger AI space?
A LLM is a type of AI model focusing on natural language understanding and generation. The fundamental principle behind them is the use of deep learning techniques, such as neural networks with millions or even billions of parameters, to analyse and learn
from vast quantities of textual data. These models are trained on diverse datasets, allowing them to recognise patterns within text with much higher precision.
The term LLM is often heard with other phrases such as transformer models and neural networks. Understanding how LLMs fit in the larger AI space is helpful to get a better sense of their uses and applications.
What can LLMs do for the financial industry?
Given the large amount and variety of data that is available in the financial industry, LLMs can bring significant value to businesses in the sector. One area that it can help with is data-driven decision-making. Given the potential to work with unstructured
text data, LLMs are able to draw insights from data sources such as news reports, social media content, and publications. This allows companies in the financial industry to draw from novel and hitherto underutilised sources.
Optimising regulatory and compliance tasks is another benefit of LLMs in the financial industry. LLM-based technologies can be used for tasks such as information retrieval and document analysis to assist with regulatory and compliance-related paperwork.
LLMs are also able to automate monitoring and reporting tasks, allowing financial institutions to have pipelines that will function with minimal human intervention.
LLMs also have boosted the capabilities and expectations we have around chatbots and virtual assistants. LLM-powered chatbots such as ChatGPT have shown an immense capacity for human-like communication experiences. Incorporating these chatbots into financial
customer support services will improve the efficiency and the nature of customer interactions. For instance, a virtual personal adviser that can provide tailored insight into investments or personal financial management can be extremely well-received by customers.
Finally, we have recently seen a surge of LLM-based add-ons for existing tools and technologies. For instance, natural language-based instructions, programming assistants, and writing assistants are becoming extremely common. These LLM-based functionalities
can bring about significant innovation and efficiency to the finance industry.
What does the future look like?
The future does indeed look promising. LLM-based technologies can automate and streamline a variety of tasks, especially activities such as content retrieval and generation, but also tasks such as programming. This will bring about significant and sector-wide
efficiency increases. It will enable companies to make better sense of data, particularly unstructured text data, thereby allowing for more informed decision-making.
LLMs will also contribute to better customer experiences. With higher natural language processing capabilities led by LLMs, customer-oriented tools such as chatbots will be more capable of taking on a larger portion of customer support, as well as providing
improved support services. This will improve the quality of customer experience while freeing up valuable human time and capacity to engage in more value-generating tasks.
The AI space is rapidly changing, with businesses constantly aiming to be at the cutting edge of innovation. With LLMs, these changes can be expected more than ever in the financial sector.
How can businesses better prepare for LLM-led changes in the financial industry?
AI is here for the long term, and will significantly change the way businesses function across the industry. Embracing the changes sooner than later will allow companies to make the most out of the emerging technologies. This is especially applicable in
areas that contain tedious, process-oriented tasks. Using AI and LLMs to automate such tasks can create significant boosts in productivity.
The industry is evolving rapidly, with new technologies and improvements introduced regularly. Companies that invest the time and effort to understand these changes and potential improvements are likely to gain a competitive advantage over time. And these
extend beyond the business, to the workforce also. Enabling employees to go a few steps beyond their usual set of skills, to building AI and LLM-based capacities, will lead to increases in employee productivity. This will also future-proof the workforce, allowing
businesses to confidently tackle the challenges that may emerge with rapidly changing AI.
The bottom line
Large Language Models (LLMs) are poised to revolutionise the financial industry through enhanced data analysis, improved customer interactions, and streamlined regulatory tasks.
These sophisticated AI tools can analyse and interpret vast amounts of unstructured data, offering insights that drive data-driven decision-making.
Businesses in the financial sector must adapt swiftly, embracing LLMs to automate repetitive tasks, and invest in upskilling their workforce to harness the full potential of these emerging technologies. This proactive approach will ensure they are well-positioned
to leverage the transformative impact of AI and LLMs, securing a future that is more efficient, innovative, and customer-focused.