Today, there’s growing awareness across society of companies’ responsibilities to communities and the need for social equity and care for the environment. In response, we’re seeing more and more asset managers implement environmental, social and governance (ESG) targets and objectives into their investment processes.
For evidence of the growing momentum behind ESG investing, take a look at recent research by Morningstar. It reports net flows of US$20.6 billion into sustainable funds in 2019, more than three times their level the previous year. What’s more, ESG is good for business. The best-performing fund in Morningstar’s ESG universe in 2019 outpaced the returns on the S&P.
As ESG heats up, so does the pressure to demonstrate delivery
Such figures suggest that embracing ESG principles can help asset managers create a distinctive position with investors while also building long-term value. However, my frequent conversations with clients across the industry confirm that – all too often – the full integration of ESG into portfolio construction and decision-making is being held back by several challenges.
What are they? Investment managers have to provide robust insights and metrics on portfolio performance and progress against individual investors’ goals. But this is made difficult by issues – many of them data related – like inconsistencies across data providers, limited relevant data due to disclosures being voluntary, and a glaring lack of industry-wide standards around metrics.
To address these challenges, we’re seeing asset managers adopt multiple approaches in regard to data. Examples include in-depth research, periodic exploration of emerging sources of data, and adopting existing or new solutions from established providers. However, these different approaches are often inconsistent and applied as siloed point solutions, resulting in insights and performance reporting that is neither efficient nor scalable.
Four steps to success in ESG investing – by harnessing data and artificial intelligence (AI)
In my experience, a key to overcoming the challenges of ESG investing and reporting lies in new data and AI-powered capabilities. Used correctly, these could open the way to acceleration, scale and innovation.
1. Create a meaningful data foundation
Issues around data – including inconsistent availability and concerns over quality – can make it difficult to build key ESG data into the investment process and report ESG performance to clients. To overcome these challenges, firms need to establish the right data foundation and a scalable data and technology architecture covering every stage of the data lifecycle – from capture to curation and consumption to actionability.
Such an architecture is vital, because the sheer volumes of unstructured and incompatible data available today present a major challenge to asset managers seeking ESG-friendly investments. By accessing and making sense of new data signals from an expanding data universe – including online/social and non-traditional sources – managers can open up new discoveries and create customized scores. One example is using AI sensing data to generate proprietary measures of environmental risk. For example, leading firms are utilizing computer vision and high-resolution imagery to measure beach erosion. The resulting benefits from such activities can be amplified by using automation to harmonize data across different sources, including creating a common taxonomy and new data validation and anomaly detection capabilities.
For all this to succeed, firms need guidance on selecting and applying the right ESG factors in their data classification, extraction and validation, all aligned to their overall investment objectives. By clarifying and codifying their internal perspectives on ESG factors, managers can support and improve their decision-making.
2. Use AI to power ESG investing
By providing a platform on which asset managers can experiment and test investment ideas, AI is now rapidly revolutionizing how firms conduct research. Recent investments in technologies like blockchain can be integrated into the AI-enabled insight platform, and multiple unstructured sources of external data can be combined to support portfolio discovery and alpha generation. These AI technologies can process massive amounts of data – including ESG-related information – and provide valuable, actionable insights to support ESG investing.
3. Use AI to hyper-personalize the investor experience
Of course, in order to personalize an investment for a client, an asset manager first needs to understand the client’s reasons for being in the market and then recommend the right investment at the right time. AI can help by driving recommendations tuned to each investor’s unique preferences and goals, supplemented by dynamic, real-time updates. Does this matter to investors? Recent research from Morgan Stanley suggests the answer is yes: it found that 84% of investors want the ability to select products aligned with their personal sustainability interests.
4. Use AI to empower investment managers
Armed with the right ESG data, AI can support and improve asset managers’ decision-making over portfolio composition to generate alpha or reduce risks. This would enhance their ability to manage large, complex portfolios with significant ESG components. Smart use of AI can also help develop “learning loops”, using previous decisions and model performance against expectations to produce recommendations for future decisions.
Navigating your route to ESG investing
The message is clear. By embracing ESG investing with new data – and AI-powered solutions, asset managers can meet their clients’ evolving goals more effectively, build deeper and more durable relationships and generate higher value. But how and where to start?
An ESG strategy and roadmap can help firms navigate the practicalities of ESG investing. The strategy should include the buildout of best-of-breed ESG policies, culture and mindset, along with the research, portfolio construction and reporting capabilities needed to facilitate meaningful new insights and personalized customer experiences while managing regulation and compliance.
With such a strategy in place, asset managers could use AI to derive and apply insights around their ESG investments with speed, accuracy and scale. The result? In an industry facing ongoing margin compression and constant changes in investor preferences and regulation, AI could be the new differentiator – helping firms to develop new products and respond with timely offerings that meet and exceed their clients’ ESG expectations.
In my view, that’s a new route to higher value and growth. And the time to embark on it? Today.
Special thanks to Raul Guerrero and Joseph Tung for their contributions.