Advancing Risk Mitigation for Hedge Funds While Unlocking Investment Power via Generative AI

Advancing Risk Mitigation for Hedge Funds While Unlocking Investment Power via Generative AI


In the dynamic landscape of generative Artificial Intelligence (AI), organizations seek to harness the potential of AI applications to boost productivity. I myself am a fairly new user of gen AI; this has to do with my years in the privacy-enhancing space. I do not like, nor easily trust, sharing my data, especially if it will be used to train someone else’s model and potentially leak my personal information.  

When we started our journey last year to develop SecurAI, which supports privacy-preserving use of gen AI services, it made perfect sense to us: as a company, we were already fairly integrated as the de facto solution for large-scale enterprises who were looking to collaborate on their data privately, with proven cryptographic security.

As Inpher’s CRO, my first step in taking a new solution to market is understanding the user landscape – or more specifically, understanding the problems we solve and who we solve them for. Together with the team, we started our research in the middle of last year, garnering feedback from key customers and partners who had early “beta” access to SecurAI. A few very specific areas of interest continually arose, and while there are a host of use cases for SecurAI, the one that came up consistently was hedge funds. 

In this blog, I will take a look at how hedge funds operate and how critical data is to their business, as well as the productivity, privacy, and overall security assurance SecurAI delivers.  

Hedge Funds and Data

A hedge fund is a type of investment fund that pools capital from accredited individuals or institutional investors and invests in a variety of assets, often with more flexibility and less regulatory oversight than traditional investment funds. Artificial Intelligence (AI) and the world of large language models (LLMs), or more importantly having the ability to fine-tune LLMs with their own proprietary data, are especially advantageous for hedge funds. 

Data is at the heart of a hedge fund’s ability to deliver well thought-out investment advice and employ a wide range of investment strategies, including long and short positions, leverage, derivatives, and alternative assets. Other data points may focus on looking across equities, fixed income, currencies, commodities, and other financial instruments.  Accessing, sharing, and serving key learnings from datasets enables hedge funds to increase their productivity, predictability, and overall customer satisfaction. 

Given the need to make data-driven decisions, financial firms use many types of AI, including machine learning (ML) algorithms that calculate credit risks or forecast market changes for tactical asset allocation.

How Hedge Funds Benefit from Generative AI

LLMs can enable hedge funds to more fully leverage large and diverse datasets. LLMs are designed to “understand” language, making sense of unstructured text data. At this stage, hedge funds are leveraging the capabilities of LLMs mainly to improve productivity. 

A recent survey from BNP Paribas found that 44% of money managers use ChatGPT in a professional capacity.

Following are a few of the most common applications for gen AI at hedge funds.

  • Write and debug basic code
    Taking over some of the low-risk tasks of junior employees, LLMs
    are good at code completion, editing, and finding and fixing errors.
  • Conduct initial research
    LLMs can speed up the early stages of research by reviewing, summarizing, and detecting patterns in long broker research reports, academic papers, regulatory filings and more.
    A paper from the University of Chicago showed ChatGPT can summarize lengthy corporate disclosures effectively enough to explain the stock reactions that follow.
  • Contribute to investor relations and marketing
    By synthesizing market data and fund returns, LLMs can explain performance and automate some of the basic work of investor relations. Hedge funds are also using LLMs to generate marketing text.
  • Automate regulatory and compliance tasks
    LLMs can be used for tasks like information retrieval and analysis of legal documents, to support regulatory and compliance-related paperwork.

Given the benefits listed above, it makes sense that McKinsey reports that the potential productivity lift in the banking industry from gen AI could lead to a 3-5% increase in global annual revenue, equivalent to $200 billion to $340 billion.

The Privacy Challenge

Although hedge funds are often less regulated than traditional investment funds, they are not without privacy policies and internal governance practices. Hedge funds hold sensitive data of high-net-worth individuals and institutional investors, such as names, social security numbers and bank account details. This data can be particularly appealing for hackers. Hedge funds also own proprietary trading algorithms and other types of intellectual property. So, while leveraging data is crucial to their businesses, privacy and security must always be preserved. 

As one hedge fund CEO expressed it, “We have to be very careful about risks of IP leakage with those types of tools because with ChatGPT, you’re sending queries to OpenAI servers.”

Hedge funds must balance this privacy-utility trade off across their business. In fact, governance concerns around the handling of sensitive data and intellectual property are hindering the full utilization of gen AI capabilities in hedge funds. 

The SecurAI Opportunity for Hedge Funds

Inpher SecurAI addresses these challenges by enabling hedge funds to ethically and responsibly leverage LLMs. Grounded in Confidential Computing principles, SecurAI facilitates the integration of LLMs into organizations without compromising privacy. The multi-layered defense, incorporating trusted execution environments (TEEs), advanced encryption, and remote attestation, ensures data protection throughout the AI inference process (both user inputs and model outputs are protected). Plaintext data is not visible to either the system administrator of the TEE or to any other external party (including Inpher). As AI technologies advance, SecurAI stands as a future-proof solution, committed to evolving with new cryptographic techniques and supporting diverse infrastructures and cloud environments. For more information, view our white paper


With Inpher SecurAI’s revolutionary approach to leveraging LLMs and gen AI securely, hedge funds can invest in increasing productivity and the value of their data. Experience the security and efficiency of SecurAI to build AI initiatives on a foundation of trust and reliability in an era where data breaches are costly, and trust is paramount.

Find out more today!