As artificial intelligence (AI) and machine learning technologies advance, large language models (LLMs) like ChatGPT are increasingly dominating media headlines. At Novatus Advisory, we strive to bridge the knowledge gap and dispel misconceptions surrounding these powerful tools, which represent a revolution in capabilities and, if used correctly, a generational opportunity for firms. This short article will explore the benefits, limitations, and alternative solutions for utilising ChatGPT and other LLMs within the Financial Services industry. ChatGPT, what is a Large Language Model? LLMs are a type of AI technology designed to process and generate human-like text based on vast amounts of training data. Their prowess in analysing unstructured text data enables them to extract valuable information and identify patterns in documents, emails, and news articles. Consequently, financial institutions can make data-driven decisions more effectively and efficiently and unlock data sets which were previously inaccessible to analysts. (..a note to the reader, we resisted the temptation to use ChatGPT to write this article!). Are LLMs the answer to all my data analytics challenges? Specific use cases where ChatGPT and other LLMs can provide firms with meaningful actionable insight include sentiment analysis, content generation, and summarisation. For example, LLMs can be employed to gauge market sentiment by analysing news articles and social media posts, helping traders make informed decisions. Additionally, these models can generate financial reports, draft emails, and summarise lengthy documents, saving valuable resource time. One of the most notable benefits of employing LLMs in the Financial Services industry is their ability to automate customer service tasks. LLMs can be utilised in chatbots to answer client queries and provide assistance, significantly reducing response times and enhancing the overall customer experience.
However, it is essential to recognise that LLMs may not be best suited for all use cases, especially when it comes to analytical tasks. As LLMs are primarily designed to process and generate text, they can struggle with numerical analysis or interpreting complex financial data. For instance, predicting stock prices or detecting financial fraud can be challenging for LLMs due to the nuanced and domain-specific nature of these tasks. Instead, alternative tools like numerical analysis, statistical modelling, and other more specialised machine learning solutions can be employed to enhance financial forecasting, risk assessment, and fraud detection. If AI is constantly learning from our inputs – are we giving away our secrets?
Several recent headlines have highlighted the potential risk and security concerns around ChatGPT and other open source LLMs – most notably employees inside Samsung’s semiconductor division reportedly sharing confidential information with the chatbot. Overreliance on ChatGPT and other LLMs can lead to potential data privacy issues. Although these models are trained on extensive amounts of publicly available data, there is a risk that they could inadvertently memorise sensitive information, resulting in unintended data leaks. Consequently, it is crucial to exercise caution when implementing LLMs in areas involving confidential or regulated data. Given the limitations of LLMs in certain aspects of financial services, it is vital to consider alternative tools and solutions that are better suited for specific use cases. A hybrid approach can be adopted, utilising LLMs for text-based tasks and domain-specific models for more intricate financial analyses. To conclude... While ChatGPT and other LLMs offer numerous benefits to the financial services sector, they may not be the optimal solution for every situation. Understanding their limitations and exploring alternative tools will enable financial institutions to make informed decisions about AI implementation. Novatus Advisory is experienced in assisting financial services organisations navigate the complex landscape of AI and machine learning. If you are considering where and how to leverage AI within your organisation, we would be delighted to speak to you on how to approach this and share our insight on specific use cases or industry trends. Please reach out to Will Basnett (email@example.com) who leads our Advisory team and Data Practice.