About a-team Marketing Services
The knowledge platform for the financial technology industry
The knowledge platform for the financial technology industry

A-Team Insight Blogs

DataRobot on FactSet Adds Machine Learning into Investment Workflow

Subscribe to our newsletter

In early August, FactSet and DataRobot introduced an artificial intelligence (AI) investment workflow, DataRobot on FactSet, an automated machine learning (ML) tool that helps financial services firms – particularly those lacking significant data science teams – incorporate AI into their investment workflows.

At the moment there is a shortage of data scientists within the investment management industry – in particular, those who know how to code in Python –at a time when demand for this skill set is continuing to grow. For. example, for certain portfolio modelling or risk analysis approaches, investment management teams need to have in place automated data collection that will update their algorithms.

According to FactSet, the DataRobot on FactSet solution integrates machine learning technology from DataRobot into the FactSet platform, enabling clients to build, deploy, monitor, and manage sophisticated machine learning models quickly and easily. Investment managers without specific data science knowledge can use the tool to create AI applications for areas such as equity volatility, bond performance, and macroeconomic event predictions.

FactSet says the tool provides the guardrails required for investment managers without data science expertise to build and deploy advanced machine learning. For firms that already have existing data science teams, DataRobot on FactSet can increasing the speed and scale of their financial models, the company says.

“Clients are looking for more effective data and AI tools that will help them surface new investment insights faster and with greater efficiency,” said Rob Robie, executive vice president, analytics, at FactSet. “We are excited to be working with DataRobot to provide an elegant and intuitive solution that allows users to develop and execute successful machine learning strategies.” FactSet had already been using DataRobot tools internally for its own needs for several years.

“There is an unprecedented opportunity for investment professionals to capitalise on their data, and now is the time to adopt robust AI and machine learning capabilities,” said Rob Hegarty, general manager of financial markets and fintech, DataRobot. “We’re excited to work with FactSet on this dynamic integration which will help more organisations make data-driven decisions and realise the true value of AI.”

Subscribe to our newsletter

Related content

WEBINAR

Recorded Webinar: Agility as Alpha: How Trading Infrastructure Determines Who Wins in Volatile Markets

Tariff shocks, geopolitical realignment and macroeconomic regime shifts are redrawing the investment landscape faster than most firms’ technology stacks can keep up. For hedge funds and asset managers, the ability to move quickly into new asset classes, geographies or strategies is no longer just an operational concern – it is a front-office differentiator and, increasingly,...

BLOG

Clean Data Is Not Enough to Power AI

By Shai Popat, managing director, product and commercial strategy, financial information, SIX. Agentic AI projects are beginning to roll out across the financial industry. Many firms are testing AI’s feasibility by assigning it relatively simple tasks, such as summarising information or retrieving data and documents from internal databases. Two maxims are often cited when discussing...

EVENT

Eagle Alpha Alternative Data Conference, London, hosted by A-Team Group

Now in its 8th year, the Eagle Alpha Alternative Data Conference managed by A-Team Group, is the premier content forum and networking event for investment firms and hedge funds.

GUIDE

AI in Capital Markets Handbook 2026

AI adoption in capital markets has moved into a more disciplined phase. The priority is now controlled deployment: where AI can be used safely, where it can deliver measurable value, and how outputs can be governed, monitored and evidenced. The 2026 edition of the AI in Capital Markets Handbook examines how AI is being applied...