Lisa Masten
Consulting Principal – Data and Analytics
Lisa Masten has more than 20 years of experience in the investment management industry, leading projects and designing solutions in the areas of performance and attribution, data management, market data administration, portfolio analytics, and investment accounting. Lisa leads Cutter Associates’ Data and Performance practice, where she advises and designs operating models, selects systems, and implements business and technology solutions. She also organizes the Implementation practice for Cutter, including leading development of its adaptable delivery framework and project toolkit.
Prior to joining Cutter, Lisa was a Senior Manager at Invesco, where she managed a global team responsible for designing and implementing data, performance, and accounting solutions. She has held roles implementing and supporting processes and technology across the front, middle and back office at multiple asset management firms. Lisa holds a Bachelor of Arts in finance and computer science from North Central College in Illinois.
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
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.
2024 will be known as the year of AI. No topic or technological innovation is bigger than generative AI, and the excitement ─ and concerns ─ about the technology has reached a frenzy. Investment management firms have used AI in its various forms for years in different pockets and for different use cases ─ for example, predictive data science models, algorithmic trading models, and behavioral analysis. But advances in the technology in the last year promise to make it easier to implement AI and will open up new use cases for streamlining workflows, generating operational efficiencies, and optimizing portfolio risk and performance.
For years, Cutter has written about the lack of AI usage in vended data management tools, often in reference to well-established EDM platforms. Obviously, vendors were being cautious, but the technology was also not as sophisticated as it is today. With the recent AI advances, particularly around generative AI, it appears that the tide is turning, and we are starting to see real action in using AI in vended data management tools. But beware of the hype ─ not every “GPT”-branded tool will add value.
We expect to see vendors of data quality and data observability tools relying heavily on AI to advance those products. As firms continue to add datasets and datasets become larger and more complex, AI-enabled data quality and observability tools will play a crucial role in firms increasing the quality and trustworthiness of their data. As a first step for training AI models, firms must ensure that high-quality data is used to train the models.
Asset managers will also continue experimenting with AI. Larger firms with the resources and means to hire expertise will lead the way in proprietary applications, while AI toolkits will allow smaller firms to experiment with the technology.
However, all firms will cautiously roll out new AI applications. Firms are keenly aware of the reputational risk of failure and the coming regulations proposed by the SEC, the European Union, and other countries around AI and data privacy, conflicts of interest, and market manipulation. Human intervention and governance of AI will be required not only to remain compliant with forthcoming regulations, but also to ensure trust in the technology and results.
AI is here to stay, but we predict that the rollout among investment management firms will be measured and controlled.
To learn more about this topic, or speak with a research analyst or consultant, contact us at [email protected].