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: Unlocking Transparency in Private Markets: Data-Driven Strategies in Asset Management

As asset managers continue to increase their allocations in private assets, the demand for greater transparency, risk oversight, and operational efficiency is growing rapidly. Managing private markets data presents its own set of unique challenges due to a lack of transparency, disparate sources and lack of standardization. Without reliable access, your firm may face inefficiencies,...

BLOG

Data Transparency ‘Crisis’ Hampering Private Markets: Report

Private markets investors are dogged by a “data transparency crisis” that is exposing them to greater risk of compromising their fiduciary integrity and losing their competitive edge, according to a new report. In what the authors call a private markets paradox, the report by Rimes states that investors are beset by a lack of data...

EVENT

AI in Capital Markets Summit London

Now in its 3rd year, the AI in Capital Markets Summit returns with a focus on the practicalities of onboarding AI enterprise wide for business value creation. Whilst AI offers huge potential to revolutionise capital markets operations many are struggling to move beyond pilot phase to generate substantial value from AI.

GUIDE

Managing Valuations Data for Optimal Risk Management

The US corporate actions market has long been characterised as paper-based and manually intensive, but it seems that much progress is being made of late to tackle the lack of automation due to the introduction of four little letters: XBRL. According to a survey by the American Institute of Certified Public Accountants (AICPA) and standards...