UST helps global asset management company accelerate ESG portfolio optimization by 5x times
This European multinational company provides investment management solutions to clients around the world. With over £200 billion in assets under management, the company employs nearly 1,000 people.
Labor-intensive manual processes hampered ESG data collection and sharing
This asset management company wanted to add sustainability factors into its portfolio optimization processes. To accomplish that goal, company leadership assembled a cross-functional team of business, IT, and data science personnel to gather and provide asset managers with environmental, social, and governance (ESG) data to help make sustainability-based investment decisions. However, the ESG team struggled with time-consuming manual processes to:
- Compile and consolidate data - from many external sources, like carbon footprint and deforestation data from global non-governmental organizations (NGOs), the World Inequality Database, and MSCI’s ESG ratings
- Map ESG data - to existing internal portfolio asset information
- Make the data accessible - to asset managers so they can easily analyze the information to make ESG investment decisions
Consolidated ESG data made easily accessible for portfolio optimization
UST implemented an automated data engineering solution along with a visualization dashboard so portfolio managers can easily access and analyze ESG investment data. UST's xpresso.ai platform, an enterprise framework with accelerators to help companies create artificial intelligence (AI) and machine learning (ML)-based analytics solutions, was implemented for this project. Data pipelines were merged with internal portfolio asset information with external ESG data. The consolidated sustainability data is fed into portfolio optimization calculations that are automatically compared to global ESG index benchmarks. With this enhanced portfolio asset data, asset managers can use the intuitive ESG dashboard to generate several efficient frontiers to consider in their investment decisions. Asset managers can run “what if” scenarios by adjusting calculation parameters, such as tracking error and active weight constraints and selecting different ESG metrics to make data-driven decisions so funds are deployed to meet investor ESG goals.
5x faster ESG portfolio optimization with automated data analytics pipelines
Now that the company can run automated ESG data analytics pipelines daily, asset managers can optimize portfolios for sustainable investing 5x faster-using the most current ESG metrics and sustainability data. The time-consuming, tedious, error-prone, manual data collection and analysis processes have been replaced with an automated data analytics process that seamlessly integrates ESG data with existing asset management information.