May 29, 2023
Investment banks are ready to embrace AI, but considering their specific regulatory and resource challenges, they need to tread carefully. We explore what they should keep in mind, and how leading firms are approaching this disruption.
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The emergence of Large Language Models (LLMs) is starting to reshape traditional industries, and investment banks are no exception. The models’ remarkable ability to understand natural language, and process vast amounts of data, could revolutionize the way investment banks approach risk management, manage information flows, build their investment strategies, or even tailor hyper personalized account management.
Nevertheless, industry leaders are treading carefully - they know that information is akin to currency, and that feeding a Large Language Model with copious amounts of potentially sensitive data is not without consequences. Above all, bankers want to establish that models won't break rules (from ensuring data privacy and protection to maintaining ethical standards in data collection and usage) and create unacceptable levels of regulatory, and reputational risk.
While banks, like many other sectors, are upbeat about the prospects of integrating AI into their workflows, the environment they operate in presents unique constraints that warrants a prudent and thoughtful approach to large language models.
While banks, like many other sectors, are upbeat about the prospects of integrating AI into their workflows, the environment they operate in presents unique constraints that warrants a prudent and thoughtful approach to large language models. Investment banks are assessing various factors such as compliance with existing or prospective regulation and resources. Indeed while many investment banks could fund research, they still lack focus and specialized skills, and often a strategic approach. To fully understand how AI can disrupt traditional workflows, let’s first explore the potential of utilizing Large Language Models in investment banking, from streamlining operations, to improving decision-making and driving a competitive advantage.
Faster, more insightful research: LLMs can help banks process vast amounts of information quickly and accurately, providing insights into market trends, investment opportunities, and risk management strategies. These new capabilities could significantly reduce the time and effort required for research, allowing bankers to make more informed decisions, faster.
More profitable trading strategies and more efficient market making: LLMs can help traders analyze and interpret news and market data in real-time, identifying patterns and trends that otherwise could be overlooked.
LLMs can be leveraged to analyze vast amounts of data and identify potential risks, such as controversies, fraud or cyber attacks, before they occur or spread. These early-warning signals could help banks proactively mitigate risks and reduce the likelihood of financial losses.
LLMs can provide a higher degree of customization and personalization for clients. Investment Banks can use it to process various data, including risk adversity profiles and other customer information to generate tailored strategies and recommendations. For example, an LLM could analyze a client's portfolio and suggest investment opportunities that align with their risk tolerance and financial goals. This could enable investment bankers to provide a more personalized and effective service ultimately enhancing customer satisfaction and retention.
And yet, despite the potential for significant progress, the investment banking industry faces several hurdles, with compliance being perhaps the most crucial of all. The challenge lies in the fact that, as is often the case, innovation is far outpacing regulators' ability to keep up. Although discussions are underway regarding how to manage this rapidly evolving field, a robust framework is still several months away. Investment banks cannot afford to wait that long, and therefore must navigate this uncharted territory autonomously. Fortunately, a few key initiatives, such as the EU Artificial Intelligence Act and the effort spearheaded by US Senator Chuck Schumer, offer some valuable guidance on the critical issues banks must keep in mind so that, when AI regulations finally come into force, they are better enabled to transition into the full observance of emerging regulation.
Both proposals include provisions on transparency, explainability, data protection, and human oversight, all of which are relevant to investment banks using large language models.
The challenge lies in the fact that, as is often the case, innovation is far outpacing regulators' ability to keep up. Although discussions are underway regarding how to manage this rapidly evolving field, a robust framework is still several months away. Investment banks cannot afford to wait that long, and therefore must navigate this uncharted territory autonomously.
The preexisting regulatory burden limits the type of data that can be fed into models, and constrains access to the outcome of its data processing. If a financial institution uses research to produce investment recommendations in any form, the link between the advice given and the source of the research should be traceable. More: depending on the jurisdiction, it might need to be materialized by a declared research relationship. Add a language model, and the process becomes meaningfully more complicated.
Investment banks must ensure that the large language models they use are transparent and explainable. This means that they need to be able to understand and justify the decisions made by the LLM, and ensure that they have access only to the data they are entitled to. This can also involve implementing "Chinese walls" to compartmentalize data and prevent contamination among various internal teams. Ultimately it could even lead to training a large language model for each client.
Investment banks also need to address the aspects related to the control and ownership of the large language models they use. This includes ensuring that they have clear policies and procedures in place for data entitlement, data confidentiality, and traceability. This will help to prevent any potential misuse or unauthorized access to the LLM.
Last but not least, investment banks need to have effective risk management processes in place to mitigate the risks associated with using large language models. These include identifying and assessing potential risks, implementing appropriate controls, and monitoring the LLM to ensure that it is functioning as intended.
With one eye at the ongoing regulatory debate and another one at the competition, banks also face another dilemma when it comes to LLMs:
Building a large language model from scratch is not a simple task. It requires a significant investment in time and resources, including access to large amounts of compliant financial data, computing power, and a team of experts to train and develop the model. Additionally, training the model correctly plays a pivotal role, requiring months of data preparations and overcoming numerous unforeseen issues with what Machine Learning experts are calling ETL (the process of extracting, transforming and loading the data into a model).
Given these challenges, many banks are asking themselves whether it is sensible to build their language models, or take advantage of the external resources at hand: from existing Large Language Models like GPT that can be trained in-house, to data partners that can fuel models with accurate, actionable external data and API solutions that are able to solve most pain points of in-house Machine Learning teams.
While building a LLM in-house is complex and time-consuming, it offers full control of the roadmap. At the same, using existing solutions saves resources and puts you ahead of the competition, but your model will suffer in terms of flexibility and customization. There’s no right or wrong answer, in the end it all comes down to prioritizing resources and goals.
As always, it pays to keep an eye on what the market leaders are doing.
JPMorgan Chase, one of the largest and most prominent banking institutions in the world, has been at the forefront of adopting artificial intelligence (AI) technologies to enhance its operations and services.
The bank has allocated hundreds of millions of dollars annually towards AI initiatives and has brought in top talent from the tech industry, including renowned roboticist Manuela Veloso, who was appointed to head an in-house R&D lab modeled after some of the leading AI research organizations in the world.
One of the bank's primary goals has been to develop AI systems that can help streamline and automate various aspects of its business, from fraud detection and customer service to trading and risk management. To this end, JPMorgan Chase has been heavily investing in its data and cloud infrastructure, ensuring that it has the necessary resources to support machine learning algorithms. One of its flagship projects in this area is a software service called IndexGPT that uses AI to analyze and select securities based on customer requirements.
Given these challenges, many banks are asking themselves whether it is sensible to build their language models, or take advantage of the external resources at hand.
Morgan Stanley has partnered with OpenAI and got early access to their latest text generation AI, GPT-4, the latest and most advanced language model developed by OpenAI. The plan is to create an internal chatbot that will generate answers exclusively from Morgan Stanley's proprietary content library, allowing advisors to ask questions and receive answers with links to source documents, eliminating the need to search through PDF files hosted on internal websites.
The partnership between Morgan Stanley and OpenAI marks a significant shift in the way financial institutions are looking to streamline their operations by utilizing cutting-edge AI technology.
This approach is particularly note-worthy because, by securing early access to GPT-4, Morgan Stanley gained a distinct advantage over their competitors. Moreover, it provides a compelling example of how banks can develop their own Language AI applications without having to invest in expensive development projects. The language model can be adapted and fine-tuned to meet the specific needs of the bank, making it a more efficient and cost-effective solution than building a model from scratch, while insulating their sensitive data and complying with the very strict regulations in the industry.
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