The next wave of disruption in financial services: Generative AI and large language models

The financial services sector is one of the industries that stand to benefit from advancements in artificial intelligence (AI). In particular, generative AI and large language models have the potential to revolutionize how financial institutions operate and serve their clients. In this blog post, we will explore what generative AI and large language models are, specific use cases in financial services, how companies are investing in this technology, and the future of these technologies in the financial sector.

Generative AI and large language models: what are they?

Generative AI is a subset of AI that involves creating new content such as images, text, or sound using algorithms. The process involves training a model on a dataset of examples to enable it to learn patterns and create new content that is similar to the training data. Large language models, on the other hand, are generative AI models that specialize in generating human-like language. These models are designed to predict the probability of the next word in a sequence based on the preceding words.

The state-of-the-art in large language models is GPT-3, developed by OpenAI. GPT-3 has the capability to generate coherent and sophisticated text that resembles human writing. It has been used to create chatbots, virtual assistants, and content creation applications.

Specific use cases in financial services

Generative AI and large language models have several use cases in the financial industry. One such use case is fraud detection. Financial institutions can use generative AI models to generate synthetic data that mimics fraudulent transactions. The synthetic data can then be used to train fraud detection models to detect and prevent future fraudulent transactions.

Another use case is risk assessment. Financial institutions can use large language models to analyze large volumes of data, such as financial reports, and generate summaries and insights that aid in risk assessment. The generated text can help analysts to make informed decisions quickly and efficiently.

Investment research is also a use case for generative AI and large language models. Financial institutions can use these technologies to analyze and extract insights from large volumes of unstructured data, such as news articles and social media posts. The insights generated can be used to inform investment decisions, help identify new investment opportunities, and enhance portfolio management.

Companies investing in generative AI and large language models

Several companies in the financial industry are investing heavily in generative AI and large language models. JPMorgan Chase, for instance, has developed an AI-powered virtual assistant called COiN, which uses natural language processing and machine learning to extract information from legal documents. COiN has helped the company do in seconds what took lawyers 360,000 hours to do.¹

Goldman Sachs has also invested in AI technology to enhance its operations. The company has developed a platform called Marcus Insights, which uses large language models to analyze consumer data and provide personalized financial advice to its customers. Marcus Insights has helped Goldman Sachs to enhance customer engagement and retention.²

Another example is Morgan Stanley, which has developed an AI-powered chatbot called "AskResearch," which uses natural language processing to answer common research questions from clients. According to Morgan Stanley, with easier access to Wall Street information, clients stand poised to make better and quicker decisions that could save them up to $8 billion annually.³

Future of generative AI and large language models in financial services

Generative AI and large language models have the potential to transform how financial institutions operate and serve their clients. The technologies can help financial institutions to improve their operations, enhance risk management, and deliver personalized services to their clients.

As these technologies continue to evolve, we can expect to see even more use cases emerge in the financial industry. For instance, we may see the use of generative AI models to develop personalized investment strategies for clients, or to generate customized financial reports. We may also see the use of large language models to improve natural language understanding in chatbots and virtual assistants, enhancing customer engagement.

1. https://www.independent.co.uk/news/business/news/jp-morgan-software-lawyers-coin-contract-intelligence-parsing-financial-deals-seconds-legal-working-hours-360000-a7603256.html

2. https://www.businessinsider.com/goldman-sachs-mulling-rollout-of-marcus-ai-assistant-2020-7

3. https://gritdaily.com/ai-chatbot-morgan-stanley/