Kathy McDermott
Managing Director, Research
Kathy McDermott has more than 25 years of experience in the financial services industry. Kathy, who joined Cutter Associates in 2011, has authored multiple Cutter Research reports on a wide range of topics. She spent ten years as a consultant for a variety of asset management firms, with a focus on business analysis for front- and middle-office projects. Kathy was previously the senior business analyst, equity trading systems, at Wellington Management, specializing in the firm’s proprietary electronic and basket trading applications. Earlier in her career, Kathy worked for Thomson Financial Services (now Thomson Reuters), supporting FirstCall and PORTIA clients in Hong Kong, Japan, Singapore, and Thailand, and later went on to manage PORTIA implementations. She also worked at LongView (now Linedata) as an account manager and then product manager of electronic trading. Kathy earned her bachelor of arts in mathematics from Hamilton College.
Recent research assignments and publications include the following:
- Alternative Data and the Expanding Universe of Investment Information
- Cutter Benchmarking: Data Management
- Cutter Benchmarking: Market Data Administration
- DataOps: In Theory and Practice
- Enabling Data Analytics
- The Evolving Front Office Support Model
- Evolving Data Governance: Building a Data Culture
- Making Snowflake Part of Your Modern Data Platform
- Managed Data Services
- Market Data Administration
- Order Management Systems
- Outsourced Trading: Has the Time Come?
- Reference Data Management Solutions
Tom Phipps
Consulting Principal – Front Office
Tom Phipps has more than 25 years of experience in the investment management industry, with a background in the automation of the investment process, including portfolio management, risk analytics, trading, compliance, performance, operations, investment accounting, data management, and systems integration. He leads Cutter Associates’ Front Office practice, providing deep expertise in front-office vendors and operating models. His consulting experience includes strategic advisory, systems selection, and change management execution engagements for some of the world’s most prominent investment management firms. He also leads the development of Cutter’s Strategy and Search methodologies.
Tom gained his expertise in technology and operations at software vendors and asset management firms, where he held leadership roles in technology management. His prior experience includes Senior Manager at Deloitte Consulting, Chief Technology Officer at PanAgora Asset Management, Chief Technology Officer at Atlas Venture, Vice President of Operations at DSTi North America, and various management roles in technology and professional services at NCS Financial Systems (now SunGard AMS). Tom holds a bachelor’s degree in operations management and management information systems from Florida State University.
The following blog post is one in a series of Cutter 2024 Trends, Themes, and Predictions that provides insights into industry challenges and considerations for firms in 2024 and beyond.
Generative AI (GenAI) burst onto the scene just over a year ago when OpenAI’s ChatGPT set the record for the fastest-growing user base. While buy-side firms and vendors catering to them are excited, they are justifiably cautious about the benefits of this new technology. Although they are all in with experimenting with GenAI and looking for an edge over competitors, they also understand that this technology requires oversight. They should consider it an accelerator or assistant ─ a kind of copilot to the human expert ─ and not something that can produce a finished product. Copilots are coming to the front office.
Get Ready for AI Regulations
As firms develop their GenAI use cases, they are keeping an eye on coming regulations around AI, including data privacy, conflicts of interest, and market manipulation.
In December 2023, the EU Parliament and Council reached a provisional agreement on the AI Act to establish rules around AI usage and associated risks.
The Wall Street Journal reported in December 2023 that the U.S. Securities and Exchange Commission (SEC) has sent requests for information (known as a “sweep”) to several investment advisers to learn about AI-related marketing documents, algorithmic models, third-party providers, and compliance training. The Wall Street Journal report quoted an SEC spokesperson as saying that the existence of a sweep does not imply misconduct and that examinations are not public. (The spokesperson neither confirmed nor denied the sweeps’ existence). The SEC has already published a proposed rule around predictive data analytics and conflicts of interest.
These are just a couple of examples of regulations that will surely expand and be forthcoming from other industry regulatory bodies and governments.
Firms can apply GenAI to numerous front-office use cases to reduce monotonous tasks, optimize greenfield coding and writing, and help identify alpha opportunities. The front office has used AI for years, but GenAI and advances in cloud storage and compute power allow users to do more with greater ease. But because oversight and data privacy are so essential, firms should consider the technology an accelerator, not a substitute. For example, using GenAI to write a draft version of monthly commentary gives a portfolio manager the starting point to refine, confirm the content, and finish the commentary. Although GenAI accelerates the process of writing the commentary, the portfolio manager still adds invaluable market knowledge, firm practices, and judgment to the final version.
Other examples where firms are testing AI as a copilot in the front office include screening investable universes, suggesting efficient trading strategies and placement recommendations, municipal bond price discovery, and summarizing large amounts of text (research, financial statements, transcripts, deal documents, and terms sheets), and suggesting trends to pay attention to. Increasing efficiency in the front office is obviously appealing, but many firms are more interested in the alpha-generating capabilities of AI.
AI, more than any other technology, offers high potential rewards for the front office that are equally matched by the technology’s substantial risks. Firms are dedicating data scientists and data management resources to the front office to study these and establish best practices for using AI. Firms have tasked these teams with analyzing AI use cases, ensuring data quality, and testing the technology’s effectiveness in solving challenges and addressing front-office inefficiencies. To avoid reputational, financial, and operational risks, these teams are charged with understanding the potential benefits and placing guardrails around the technology and data.
Multiple vendors are also incorporating copilots. Microsoft Copilot is available to enterprise customers, although most investment management firms are in the testing phase of this technology. Vendors focused on the investment management sector are also rolling out copilot products. BlackRock, the most recent example, introduced its private preview of eFront Copilot in December 2023. GitHub Copilot is a coding accelerator for quants and other developers. State Street is developing a copilot for Alpha, while Snowflake has Snowflake Copilot in private preview.
Although it won’t happen this year, at some point, it will be table stakes for vendor products to include copilot features. Simultaneously, investment managers are dedicating data scientists to the front office to build and test proprietary GenAI copilots for various front-office use cases. Hold the plane ─ the copilots are coming!
To learn more about this topic, or speak with a research analyst or consultant, contact us at [email protected].